#  On the Causal Nature of Sentiment Analysis

Zhiheng Lyu
University of Hong Kong
<EMAIL_ADDRESS>
&Zhijing Jin††footnotemark:
MPI & ETH Zürich
<EMAIL_ADDRESS>
&Fernando Gonzalez
ETH Zürich
<EMAIL_ADDRESS>
Rada Mihalcea
University of Michigan
<EMAIL_ADDRESS>
&Bernhard Schölkopf
MPI for Intelligent Systems
<EMAIL_ADDRESS>
&Mrinmaya Sachan
ETH Zürich
<EMAIL_ADDRESS>
Equal contributions.

###### Abstract

Sentiment analysis (SA) aims to identify the sentiment expressed in a text,
such as a product review. Given a review and the sentiment associated with it,
this paper formulates SA as a combination of two tasks: (1) a causal discovery
task that distinguishes whether a review “primes” the sentiment (Causal
Hypothesis C1), or the sentiment “primes” the review (Causal Hypothesis C2);
and (2) the traditional prediction task to model the sentiment using the
review as input. Using the peak-end rule in psychology, we classify a sample
as C1 if its overall sentiment score approximates an average of all the
sentence-level sentiments in the review, and C2 if the overall sentiment score
approximates an average of the peak and end sentiments. For the prediction
task, we use the discovered causal mechanisms behind the samples to improve
the performance of LLMs by proposing causal prompts that give the models an
inductive bias of the underlying causal graph, leading to substantial
improvements by up to 32.13 F1 points on zero-shot five-class SA.111Our code
is at https://github.com/cogito233/causal-sa.

On the Causal Nature of Sentiment Analysis

Zhiheng Lyu††thanks:  Equal contributions. University of Hong Kong
<EMAIL_ADDRESS>Zhijing Jin††footnotemark:  MPI & ETH Zürich<EMAIL_ADDRESS>Fernando Gonzalez ETH Zürich<EMAIL_ADDRESS>

Rada Mihalcea University of Michigan<EMAIL_ADDRESS>Bernhard Schölkopf MPI
for Intelligent Systems<EMAIL_ADDRESS>Mrinmaya Sachan ETH Zürich
<EMAIL_ADDRESS>

##  1 Introduction

Sentiment analysis (SA) is the task of identifying the sentiment $y$ given a
piece of text $x$. The field has a rich history originating from subjectivity
analysis Wiebe (1994); Hatzivassiloglou and Wiebe (2000), and developed
rapidly with the availability of large opinionated online data such as reviews
with ratings (Turney, 2002; Nasukawa and Yi, 2003; Zhang et al., 2015; Keung
et al., 2020, inter alia).

Figure 1:  An overview of the paper structure, where we first investigate the
causal discovery task, and then use it to improve LLM performance. For each
document-level text review, we parse its emotion arc consisting of the
sentiment of each sentence in the review, and then use the peak-end rule
Kahneman et al. (1993); Kahneman (2011) to identify whether the overall
sentiment is an average of the arc (corresponding to Slow Thinking), or an
average of the peak and end sentiments (corresponding to Fast Thinking).

Despite recent advances in large language models (LLMs), it is still
challenging to address the fine-grained five-class SA (which corresponds to
the five star ratings in most datasets) for document-level classification Choi
et al. (2020); Fei et al. (2023); Truică et al. (2021), due to the subtle
nature of the task including aspects such as commonsense reasoning, the
presence of sarcasm, or inter-aspect relations, among others Poria et al.
(2023).

In this paper, we propose a causally-informed solution for the SA task.
Different from the approach of naïvely applying the up-to-date LLMs, we
leverage insights from causal inference to propose a reformulation for SA into
two tasks, as in Figure 1: (1) a causal discovery task to identify the cause-
effect relation between the review $X$ and the sentiment $Y$, and (2) the
traditional prediction task $f:{x}\mapsto y$ to model the sentiment using the
review as input.

We first look into the causal discovery task. In the study of affect science
Salovey and Mayer (2004); Barrett (2006); Feinstein (2013), language can be
the cause of emotion Satpute et al. (2013); Kassam and Mendes (2013) – namely
a review priming the following sentiment, i.e., the Causal Hypothesis C1 of
$X\rightarrow Y$); or emotion can affect the use of language Barrett (2006) –
namely sentiment priming the review as an ad-hoc justification for the
emotion, i.e., the Causal Hypothesis C2 of $Y\rightarrow X$.

Given the possibility of both causal directions $X\rightarrow Y$ or
$Y\rightarrow X$ in the SA data, we identify the actual underlying mechanism
based on insights from psychology Kahneman (2011); Epstein (1994).
Specifically, we identify the correspondence of the above two causal
mechanisms with the Fast and Slow Thinking systems Kahneman (2011): (1) a
review-driven sentiment (as in C1) largely resembles the Slow Thinking process
applying reasoning based on evidence, and (2) the process of first coming up
with the sentiment and then justifying it by a review (as in C2) conforms to
Fast Thinking. Given this correspondence, we apply the peak-end rule from
psychology Kahneman et al. (1993); Kahneman (2011). As shown in the right part
of Figure 1, we classify a sample as C1 if its overall sentiment score
approximates an average of all the sentence-level sentiments in the review,
and as C2 if the overall sentiment score approximates an average of the peak
and end sentiments.

Based on the identified causal mechanism behind SA data from the causal
discovery task, we further explore how it can improve prediction performance
in the era of LLMs. Existing literature highlights “causal alignment,” namely
to align the prediction direction along the underlying causal direction Jin et
al. (2021); Schölkopf (2022); Schölkopf et al. (2021), but to our knowledge we
are the first to explore how causal alignment improves model performance of SA
in the era of LLMs. Specifically, we answer three subquestions: (Q1) If using
the standard SA prompt, do models perform differently on C1/C2 data? (Q2) Does
it help if we make the prompt aware of the underlying causality, i.e., use
causal prompts? And (Q3) When prompted causally, do LLMs mechanistically
understand the corresponding causal processes?

Our empirical results show that under the standard prompt, LLMs perform better
on data corresponding to the C2 causal process. Moreover, causal prompts
aligned with the causal direction of the data can substantially improve the
performance of zero-shot SA. Finally, we apply mechanistic interpretability
methods to probe the models, and find that there is still improvement space
for LLMs to correctly grasp the essence of the two causal processes. In
summary, the contributions of this paper are as follows:

  1. 1.

We approach SA as a combination of two tasks: a causal discovery task, and a
prediction task.

  2. 2.

For causal discovery, we ground the two possible causal processes in
psychology, and use the peak-end rule to identify them.

  3. 3.

For the prediction task, we inspect existing LLMs’ performance on data
corresponding to the two underlying causal processes, and design causal
prompts to improve model performance by up to 32.13 F1 points.

##  2 Problem Formulation of SA

In this section, we formulate SA as a combination of two tasks: the
traditional prediction task in NLP and the causal discovery task in
statistics, which we will introduce in the following.

###  2.1 The Prediction Task (in NLP)

SA is a prediction task to identify the sentiment $y$ given a piece of text
$x$. We adopt the setup in most existing SA datasets Maas et al. (2011); Zhang
et al. (2015); Keung et al. (2020), where the text $x$ is a review consisting
of $n$ sentences $(t_{1},\dots,t_{n})$, and the label $y$ is a sentiment score
corresponding to the star rating of the review in 1 (most negative), 2, …, 5
(most positive).

###  2.2 The Cause-Effect Discovery Task (in Statistics)

As a separate problem, there is an established task in causal discovery, the
causal-effect problem (see the review by Janzing, 2019), which aims to tell
the cause from effect using only observational data. Its formal formulation is
as follows: Suppose we have an i.i.d. dataset
$\mathcal{D}:=\\{(x_{i},y_{i})\\}_{i=1}^{n}$ containing $n$ observational data
pairs of the two variables, $X$ and $Y$. The task is to infer whether $X$
causes $Y$ (i.e., $X\rightarrow Y$), or $Y$ causes $X$ (i.e., $Y\rightarrow
X$), if one out of the two is true. In causality, “$\rightarrow$” indicates
the directional causal relation between two variables. The two hypotheses can
also be expressed in their equivalent structural causal models (SCMs; Pearl et
al., 2000) as introduced in Peters et al. (2017):

$\displaystyle\text{Causal Hypothesis 1 (C1): }X\rightarrow Y$ (1)
$\displaystyle\Leftrightarrow Y:=f_{Y}(X,N_{Y})\text{ with }N_{Y}\perp X~{},$
(2) $\displaystyle\text{Causal Hypothesis 2 (C2): }Y\rightarrow X$ (3)
$\displaystyle\Leftrightarrow X:=f_{X}(Y,N_{X})\text{ with }N_{X}\perp Y~{},$
(4)

where $N_{i}$ is an unobserved noise term orthogonal to the input
distribution.

###  2.3 Causality and NLP Model Performance

For many years, causality and machine learning have been two separate domains
on their own. Recently, researchers started to think about how the causal
knowledge of the data can improve machine learning performance on the
prediction task, especially for the two variable cause-effect case Schölkopf
et al. (2012); Jin et al. (2021); Ni et al. (2022). The essence of this line
of research is that causality makes the two learning tasks $x\mapsto y$ and
$y\mapsto x$ asymmetric, as one function’s prediction direction aligns with
the ground-truth causal direction behind the two random variables, and another
contradicts. We call this phenomenon “causal alignment,” or “direction match,”
of the prediction task and the causality.

To contrast the contribution of our work, we review the previous literature on
causal alignment, which only shows its effect on the performance of trained-
from-scratch machine learning models, without any indications in the era of
LLMs:

  1. 1.

Causal alignment makes a model more robust against covariant shifts (Jin et
al., 2021; Schölkopf, 2022; Schott et al., 2018, inter alia)

  2. 2.

Semi-supervised learning (SSL) only works under causal misalignment, as the
cause variable contains no information about the mechanism, but the effect
variable does. So in the misaligned case, additional $P_{X}$ (i.e., the effect
variable) helps SSL Schölkopf et al. (2012); Jin et al. (2021).

  3. 3.

Learning a causally-aligned model induces less Kolmogorov complexity (a more
minimal description length) than the causally-misaligned model on the same
$X$-$Y$ data Jin et al. (2021); Janzing and Schölkopf (2010)

  4. 4.

Causal alignment significantly affects model performance in supervised
learning, in the case of machine translation Ni et al. (2022).

All the above findings are drawn under the training condition that we can
isolate the training data to be only of one causal direction. In the era of
LLMs, we face substantial differences: (1) the training data can be a mixture
of both causal directions, (2) the operationalization of the prediction task
is through prompting, but no longer a separate model for each direction, and,
(3) in general, lots of research attention shifts to designing better prompts
for already pre-trained models in their inference mode.

Given these changes, we use the rest of the paper to address the following
novel research questions:

  1. 1.

What is the causal direction in SA? (Section 3)

  2. 2.

Can causal alignment help us improve SA prompts in the era of LLMs? (Section
4)

##  3 Causal Discovery: Does Sentiment Cause the Review, or Vice Versa?

###  3.1 Problem Setup

As mentioned previously, the setup of the bivariate causal discovery problem
is to infer whether $X$ causes $Y$ (C1), or $Y$ causes $X$ (C2), based on a
dataset $\mathcal{D}:=\\{(x_{i},y_{i})\\}_{i=1}^{n}$ containing only
observational data of the joint distribution.

#####  3.1.0.0.1 Challenges

The common paradigm to check causal discovery results is to generate simulated
data, of which the ground truth causal graph is known Zhang and Hyvärinen
(2009); Spirtes and Zhang (2016). However, in the context of the established
SA datasets, such as Yelp Zhang et al. (2015), Amazon Keung et al. (2020), and
App Review Grano et al. (2017), we would not be able to track each individual
user and survey their original causal process when composing the review and
the rating. Another solution would also be difficult, as it would require SA
to abandon all the above well-established datasets, and meticulously collect
new data while surveying the users’ underlying causal process.

#####  3.1.0.0.2 Our Approach

In the context of our work, we propose that there are still rich findings that
we could derive from the observation-only data in the existing datasets,
without interviewing or conducting new costly data collection.

The key to our approach is the psychology theories of the two causal
processes, as the relation between sentiment and text has been well-studied
and verified by randomized control trials (RCTs), among many other
experiments. In the rest of the section, we first introduce in Section 3.2 the
psychology theories of fast and slow thinking, followed by the Peak-End Rule
as the quantitative signal. Then, we operationalize the theory with
computational techniques in 3.3.0.0.1, and the report findings on three
different SA datasets in Section 3.4.

###  3.2 Psychological Processes Underlying Sentiment Processing

#####  3.2.0.0.1 Two Systems of Emotional Responses

In psychology, the bifurcation into System 1 and System 2 in human decision-
making, including sentiment processing, has garnered substantial empirical
support Kahneman (2011); Epstein (1994).

System 1, or the “Fast Thinking” system, operates involuntarily, effortlessly,
and without conscious awareness. It is often optimized in evolution to provide
rapid responses to environmental stimuli LeDoux (1998), and guides most of our
daily cognitive processing Kahneman (2011), and emotional responses such as
fear or joy Zajonc (1980).

Conversely, System 2, often termed as “Slow Thinking,” is deliberate, slower,
and more rational, requires more conscious effort Kahneman (2011), and allows
for self-regulation and thoughtful consideration before making decisions
Baumeister et al. (1998). The interplay between these systems influences
everything from mundane to critical decisions, highlighting the complexity of
human emotional and cognitive processing Kahneman and Frederick (2002);
Kahneman (2011).

#####  3.2.0.0.2 Correspondence to the Two Causal Processes

There is a nice correspondence between the fast/slow thinking systems and our
two causal hypotheses. As mentioned previously, the Causal Hypothesis 2 (C2)
posits $Y\rightarrow X$, where the sentiment $Y$ causes the review $X$, which
aligns well with the Fast Thinking system Kahneman (2011); LeDoux (1998), as
it rapidly generates an emotional reaction $Y$, and then writes text to
justify it $Y$. On the other hand, the Causal Hypothesis 1 (C1) refers to the
case where $X\rightarrow Y$, namely the review $X$ causing the sentiment $Y$.
It is an instance of Slow Thinking Baumeister et al. (1998); Kahneman and
Frederick (2002), which deliberately uses conscious efforts to list out the
up- and downsides of an experience in the review $X$, and come up with a
thoughtful final decision as the rating $Y$.

#####  3.2.0.0.3 Quantitative Signals of the Two Processes

In sentiment processing, an evidence for the two processes is the famous
Kahneman et al. (1993) study illustrating the Peak-End Rule of how individuals
recall and evaluate past emotional experiences, which we show in Figure 1. As
we know, fast thinking is prone to systematic biases and errors in the
judgment Tversky and Kahneman (1974), and the Kahneman et al. (1993) study
provides important quantitative results showing that, in the Fast Thinking
system, people’s emotional memories of an experience are disproportionately
influenced by its most intense point (the “peak”) and its conclusion (the
“end”), rather than by the average experience as in the Slow Thinking system.
The important role of peak and end for the fast thinking system implies that
it is the intensity of specific moments that dominate memory and judgment.

| Yelp | Amazon | App Review
---|---|---|---
| All | C1 | C2 | All | C1 | C2 | All | C1 | C2
# Samples | 34,851 | 19,557 (56%) | 15,294 (44%) | 2,582 | 1,393 (54%) | 1,189 (46%) | 9,696 | 3,809 (39%) | 5,887 (61%)
# Sents/Review | 11.11 | 11.30 | 10.87 | 6.70 | 6.62 | 6.80 | 6.34 | 6.33 | 6.35
# Words/Sent | 15.53 | 15.55 | 15.49 | 11.04 | 11.28 | 10.77 | 10.53 | 10.90 | 10.29
Vocab Size | 64,864 | 48,889 | 44,826 | 10,271 | 7,609 | 7,049 | 20,248 | 12,773 | 15,400
Avg Sentiment | 2.93 | 2.74 | 3.18 | 2.94 | 2.82 | 3.07 | 2.9 | 2.72 | 3.02
Avg $\lambda_{1}$ | 3.78 | 2.97 | 4.83 | 3.77 | 3.10 | 4.55 | 6.03 | 5.18 | 6.58
Avg $\lambda_{2}$ | 4.48 | 6.05 | 2.48 | 4.21 | 5.72 | 2.45 | 5.10 | 7.96 | 3.26
Table 1: Statistics of the entire datasets and their C1 and C2 subsets for
Yelp, Amazon, and App Review. We can see that a roughly balanced number of
reviews aligning with the C1 and C2 processes.

###  3.3 Operationalizing the Theory

We summarize the previous psychological insights in the upper left part of
Figure 1, where the Causal Hypothesis 1 corresponds to taking the average of
all emotional experiences mentioned in the review $X$ for the sentiment $Y$,
and the Causal Hypothesis 2 uses the peak and end emotions in the review $X$
to derive the sentiment $Y$. In this section, we introduce a formalization of
the theory, and suggest signals to distinguish the two causal hypotheses.

#####  3.3.0.0.1 Emotion Arc

To capture the aforementioned trajectory of emotional experiences, we use the
concept of the emotion arc Reagan et al. (2016), an example of which we
visualize in Figure 1. Contextualizing it in the task of SA, we formally
define an emotion arc of the review as follows. Given a review $x$ consisting
of $n$ sentences $(t_{1},\dots,t_{n})$, we identify the sentiment for each of
them, thus obtaining a series of sentiment labels $(s_{1},\dots,s_{n})$. We
denote this series as the emotion arc $\bm{e}:=(s_{1},\dots,s_{n})$ of the
review.

#####  3.3.0.0.2 The Two Causal Processes

Provided the notion of the emotion arc $\bm{e}:=(s_{1},\dots,s_{n})$ for a
review $x$, we formulate the sentiment labels corresponding to the two causal
processes as follows:

Slow Thinki ng (Causal Process 1): $\displaystyle\hat{y}_{\mathrm{avg}}$
$\displaystyle=\frac{1}{n}\left(s_{1}+\dots+s_{n}\right)~{},$ (5)
$\displaystyle\lambda_{1}$ $\displaystyle=|y-\hat{y}_{\mathrm{avg}}|~{},$ (6)
Fast Thinki ng (Causal Process 2): $\displaystyle\hat{y}_{\mathrm{peakEnd}}$
$\displaystyle=\frac{1}{2}\left(\mathrm{Peak}(s_{1},\dots,s_{n})+s_{n}\right),$
(7) $\displaystyle\lambda_{2}$
$\displaystyle=|y-\hat{y}_{\mathrm{peakEnd}}|~{},$ (8)

where $\lambda_{i}$ indicates the alignment of the actual sentiment $y$ with
the Causal Process $i$, and $\mathrm{Peak}(\cdot)$ selects the sentiment with
the strongest intensity by its distance from the neutral sentiment 3, which is
the middle point among the sentiment range 1–5, i.e.,
$\mathrm{Peak}(s_{1},\dots,s_{n}):=s_{\operatorname*{argmax}_{i}|s_{i}-3|}$.

Example of a C1-Dominant Review
---
$\bm{\cdot}$ This was a great spot to take a break from it all and just people
watch. $s_{1}=4.57$
$\bm{\cdot}$ We sat at the bar facing the casino and we were entertained the
whole time.$s_{2}=4.67$
$\bm{\cdot}$ The mini grilled cheese (appetizer) was fantastic. $s_{3}=4.53$
$\bm{\cdot}$ It came with a tomato based dipping sauce that was the perfect
compliment to the bite sized wedges. $s_{4}=4.20$
$\bm{\cdot}$ Tip - ask for two dipping sauces because one just won’t do.
$s_{5}=1.60$
Stars $y$: 4
Psychology Scores ($\downarrow$): $\lambda_{1}=0.0884<\lambda_{2}=0.8683$
Example of a C2-Dominant Review
$\bm{\cdot}$ I read the reviews and should have steered away… but it looked
interesting. $s_{1}=3.72$
$\bm{\cdot}$ Salad was wilted, menus are on the wall, with no explanation so
you are ordering blind, service was NOT with a smile from the bartender to the
waitress, to the server who helped the waitress, and the waitress never
checked back to see how everything is. $s_{2}=2.20$
$\bm{\cdot}$ Terribly overpriced for what you get, and as an Italian, this
does not even pass for a facsimile thereof! $s_{3}=1.45$
$\bm{\cdot}$ Stay away for sure. $s_{4}=1.85$
$\bm{\cdot}$ I only gave them one star, as I had to fill something in, they
should get no stars! $s_{5}=1.32$
Stars $y$: 1
Psychology Scores ($\downarrow$): $\lambda_{1}=1.1827>\lambda_{2}=0.3647$
Table 2: Examples of C1- and C2-dominant reviews.

Here, we interpret $\lambda_{i}$ as an indicator for each causal process,
where a small value (with the best value being zero) implies the alignment
with the process $i$. We show two examples in Table 2, one aligning well with
the Causal Process C1 with a small $\lambda_{1}$, and another aligning well
with the Causal Process C2 with a small $\lambda_{2}$.

|  | Random | GPT-2 XL | LLaMa-7B | Alpaca-7B | GPT-3 | GPT-3.5 | GPT-4
---|---|---|---|---|---|---|---|---
F1 | Overall | 19.82 $\pm$2.07 | 10.23 $\pm$4.12 | 31.78 $\pm$5.32 | 46.01 $\pm$5.35 | 52.71 $\pm$1.73 | 57.98 $\pm$5.11 | 59.54 $\pm$4.69
C1 Subset | 21.36 $\pm$2.26 | 5.80 $\pm$3.11 | 27.30 $\pm$4.73 | 37.77 $\pm$7.66 | 43.96 $\pm$2.93 | 58.64 $\pm$1.48 | 58.62 $\pm$2.54
C2 Subset | 20.43 $\pm$2.95 |  16.37 $\pm$5.33 |  37.66 $\pm$7.86 |  55.82 $\pm$4.02 |  65.40 $\pm$1.37 |  59.09 $\pm$9.13 |  62.57 $\pm$6.85
Accuracy | Overall | 19.78 $\pm$2.07 | 23.06 $\pm$2.10 | 39.28 $\pm$5.07 | 47.72 $\pm$4.19 | 53.22 $\pm$1.35 | 58.36 $\pm$4.13 | 59.84 $\pm$4.17
C1 Subset | 20.61 $\pm$2.23 | 16.18 $\pm$1.59 | 36.55 $\pm$4.05 | 42.14 $\pm$5.26 | 43.61 $\pm$2.89 |  59.89 $\pm$1.09 | 59.62 $\pm$1.96
C2 Subset | 18.86 $\pm$2.78 |  30.79 $\pm$2.68 |  42.33 $\pm$7.24 |  53.93 $\pm$3.91 |  63.93 $\pm$1.28 | 56.66 $\pm$8.08 |  60.08 $\pm$7.05
Table 3: Performance of different models on the five-class classification of Yelp-5. We use five paraphrases for the prompt (in Section A.3), and report the average performance with the standard deviation.  |  | Random | GPT-2 XL | LLaMa-7B | Alpaca-7B | GPT-3 | GPT-3.5 | GPT-4
---|---|---|---|---|---|---|---|---
F1 | Data=C1, Prompt=C1 | 20.47 $\pm$2.47 | 6.12 $\pm$2.77 | 55.16 $\pm$7.16 | 52.74 $\pm$5.04 | 38.36 $\pm$6.66 | 60.62 $\pm$3.24 | 54.23 $\pm$4.17
Data=C1, Prompt=C2 | 20.26 $\pm$2.31 | 15.98 $\pm$3.59 | 36.22 $\pm$8.95 | 35.10 $\pm$5.40 | 54.44 $\pm$1.64 | 52.85 $\pm$6.01 | 58.58 $\pm$3.33
Data=C2, Prompt=C1 | 22.35 $\pm$3.02 | 31.98 $\pm$8.66 | 56.69 $\pm$8.46 | 54.74 $\pm$14.26 | 74.64 $\pm$3.06 |  78.18 $\pm$1.21 | 72.52 $\pm$3.68
Data=C2, Prompt=C2 | 20.35 $\pm$2.18 |  48.50 $\pm$7.66 |  66.82 $\pm$7.90 |  71.22 $\pm$3.99 |  77.09 $\pm$1.33 | 78.16 $\pm$1.81 |  76.80 $\pm$1.36
Acc | Data=C1, Prompt=C1 | 19.60 $\pm$2.67 | 12.96 $\pm$2.91 | 58.23 $\pm$5.30 | 55.81 $\pm$4.00 | 43.16 $\pm$6.20 | 60.39 $\pm$3.25 | 54.06 $\pm$3.97
Data=C1, Prompt=C2 | 19.68 $\pm$2.46 | 22.61 $\pm$5.73 | 43.07 $\pm$8.18 | 41.45 $\pm$4.06 | 56.36 $\pm$1.94 | 53.11 $\pm$5.89 | 58.68 $\pm$3.45
Data=C2, Prompt=C1 | 20.97 $\pm$3.19 | 43.51 $\pm$6.30 | 57.54 $\pm$7.21 | 54.82 $\pm$12.59 | 76.60 $\pm$3.23 | 77.38 $\pm$1.43 | 70.69 $\pm$3.93
Data=C2, Prompt=C2 | 19.03 $\pm$2.18 |  51.59 $\pm$5.09 |  68.16 $\pm$9.24 |  71.83 $\pm$4.33 |  76.70 $\pm$1.35 |  78.92 $\pm$1.31 |  75.38 $\pm$1.70
Table 4:  Performance on Yelp using the causal prompts on the two causal
subsets. We report the average performance across the five paraphrases for
each prompt, with the standard deviation.

###  3.4 Findings on SA Datasets

#####  3.4.0.0.1 Dataset Setup

We adopt three commonly used datasets in SA: Yelp Zhang et al. (2015), Amazon
Keung et al. (2020), and App Review Grano et al. (2017). For the Amazon data,
we concatenate each review’s title with its text. Since the model performance
on many binary classification datasets is saturated Poria et al. (2023); Yang
et al. (2019), we use the 5-way classification version of the SA datasets when
applicable.

Since we need to utilize the emotion arc, we keep only reviews with at least
five sentences, after sentence tokenization using the Spacy package Honnibal
and Montani (2017). We apply this filtering above on the test set of the Yelp
dataset, the English test of Amazon, and the unsplit entire dataset of App
Review. We report the statistics of remaining samples in Table 1.

To obtain the emotion arcs, we calculate the sentiment score of each sentence
by the sentiment-analysis pipeline222https://huggingface.co/distilbert-base-
uncased-finetuned-sst-2-english from Huggingface Wolf et al. (2020).

#####  3.4.0.0.2 Causal Discovery

For each input sample, we process them as in Table 2, namely first obtaining
the sentence-level sentiments to form the emotion arc, and then calculating
the alignment scores $\lambda_{1}$ and $\lambda_{2}$ for each causal process,
respectively. We consider an example as dominated by the causal process
$C_{i}$ if the alignment score $\lambda_{i}$ is more optimal than the other.

We report the resulting statistics in Table 1. For each dataset, we describe
their overall statistics, as well as the statistics of data with the
underlying causal process of C1, and that of C2. We can see that Yelp and
Amazon have an almost balanced split of C1 and C2, while App Review has 61% C2
data compared to 39% C1 data. See Section B.2 for an additional visualization
of the $\lambda_{1}$-$\lambda_{2}$ distribution across the three datasets.

##  4 How to Improve Sentiment Classifiers with Causal Alignment?

Using our proposed causal discovery method, we have identified two distinct
subsets with their corresponding causal processes C1 and C2. Now, we address
the last practical question proposed in Section 2.3:

> Can causal alignment help us improve SA in the era of LLMs?

Specifically, we take the commonly used approach in the era of LLMs, i.e.,
prompting pre-trained LLMs for the SA task, and look into how alignment with
the underlying causal process could help SA performance. We answer the
following three subquestions in this section:

  1. Q1.

Using the standard SA prompt, do models perform differently on C1/C2 data?
(Section 4.1)

  2. Q2.

Does it help if we make the prompt aware of the underlying causality, i.e.,
use causal prompts? (Section 4.2)

  3. Q3.

When prompted causally, do LLMs really understand the causal processes?
(Section 4.3)

###  4.1 Q1: Do Models Perform Differently on C1/C2 Data?

#####  4.1.0.0.1 Experimental Setup

The first question is whether models perform differently on data with the
causal nature of C1 or C2. We use the subsets identified by our
psychologically-grounded causal discovery, and test a variety of available
autoregressive LLMs, including the open-weight GPT-2 Radford et al. (2019),
LLaMa Touvron et al. (2023), and Alpaca Taori et al. (2023); as well as the
closed-weight models with OpenAI API, the instruction-tuned GPT-3 (text-
davinci-002) Brown et al. (2020); Ouyang et al. (2022), GPT-3.5
(gpt-3.5-turbo-0613), GPT-4 (gpt-4-0613) OpenAI (2023).

We use the standard prompt formulation for SA in the format of “[Instruction]
Review Text: {$x$}$\backslash$n Label:”. The experiments are on a set of
randomly selected 1K samples from the test set of Yelp-5 Zhang et al. (2015),
due to the time- and cost-expensive inference of the above LLMs. (E.g.,
LLaMa/Alpaca takes 96 GPU hours to run.) See more experimental details in
Section A.1.

#####  4.1.0.0.2 Results

We show the performance of the six LLMs in Table 3, and report the F1 and
accuracy across the five-class classification on Yelp-5. We can see that the
existing LLMs perform the best on the subset with the causal process C2,
implying that the decision pattern of LLMs is closer to the Fast Thinking
system, which takes the peak-end average of the emotion arc.

###  4.2 Q2: Do Causal Prompts Help?

#####  4.2.0.0.1 Designing Causal Prompts

Inspired by the fact that models perform differently on C1/C2 data, our next
question is, will it help if we directly give a hint to the LLMs about the
underlying causal graph?

|  Prompt Design
---|---
C1 |  As a customer writing a review, I initially composed the following feedback: “[review]”
After carefully considering the facts, I selected a star rating from the
options of “1”, “2”, “3”, “4”, or “5”. My final rating was:
C2 |  As a customer writing a review, I initially selected a star rating from the options “1”, “2”, “3”, “4”, and “5”, and then provided the following explanations in my review: “[review]”
The review clarifies why I gave a rating of
Table 5: Causally-aware prompts describing the SA task in contexts with the C1
and C2 causal graphs.

To this end, we propose the idea of causal prompts, which are prompts that
describe the causal story behind the input and output variables. We list our
designed prompts for the C1 and C2 stories in Table 5.

#####  4.2.0.0.2 Results

We report the performance for all combinations of the dataset natures and
prompt natures in Table 4, where we find that the most-performant setting uses
Prompt C2 on the data subset with the same causal nature, C2. This alignment
leads to the best performance across almost all models by both F1 and
accuracy. On the C2 data, we also see that Prompt C2 outperforms the standard
SA prompt in Table 3 by a substantial margin, such as 32.13 F1 points increase
for GPT-2, and 14.23 F1 points increase for GPT-4.

However, although Prompt C2 shows a strong performance, the other causal
prompt, i.e., Prompt C1, does not always help the data subset C1 in all cases,
from which we raise a further question – how well do LLMs really
mechanistically understand our prompts? We explore this question in the next
section.

###  4.3 Q3: Can LLMs Correctly Capture the Causal Stories in the Prompts?

Although the proposal of the two causal prompts is intuitive for humans, we
still need to inspect whether LLMs are able to understand them correctly.

#####  4.3.0.0.1 Method

Mechanistically, for a model to solve SA for the causal process C1 correctly,
it needs to treat the sentence-level sentiments across all sentences equally;
and for a model to solve SA for the causal process C2 correctly, it needs to
pay more attention to the peak and end sentiments on the emotion arc.

Targeting the two mechanisms, we use causal tracing Meng et al. (2022) to
attribute the final sentiment prediction to the source sentences in the input.
Briefly, causal tracing uses causal mediation analysis Pearl (2001) to
quantify the causal contribution of the internal neuron activations of a model
to its final prediction Vig et al. (2020). We use causal tracing to inspect
the causal effects of the hidden states on the model prediction, using the
open-weight models, LLaMa and Alpaca. We use the causal effects of the first-
layer neurons for each sentence, which we aggregate to obtain the final
prediction. See implementation details in Section A.2.

#####  4.3.0.0.2 Results

Figure 2: Causal attribution in LLaMa-7B and Alpaca-7B, showing how much each
sentence contributes to the prediction probability.

We plot the causal attribution results in Figure 2, showing the contribution
of each sentence to the final prediction.

If a model can capture the two mechanisms correctly, then Prompt C1 should
trigger uniform attention over all the sentences (which is roughly observed in
Figure 2 in the row of “Prompt C1”), whereas Prompt C2 should trigger special
attention to the sentences corresponding to the peak and end sentiments (for
which we see existing models mostly pay attention to the middle sentence, as
in the “Prompt C2” row in Figure 2).

To check the exact causal effect of the peak sentence to the predictions under
Prompt C2, we aggregate the causal effect of the peak sentence, and obtain the
effect size of 0.0013 for LLaMa, and 0.0029 for Alpaca, which is close to the
middle sentence’s contribution value. Still, it is worth pointing out that
none of the models pay enough attention to the end sentence under Prompt C2,
which implies that they do not fully grasp the C2 mechanism correctly.

##  5 Related Work

#####  5.0.0.0.1 SA

The task of SA aims to identify the sentiment given a piece of text. It has a
rich history originating from subjectivity analysis Wiebe (1994);
Hatzivassiloglou and Wiebe (2000), and developing rapidly with the
availability of large opinionated online data such as reviews with star
ratings (Turney, 2002; Nasukawa and Yi, 2003; Zhang et al., 2015; Keung et
al., 2020, inter alia). Most literature on SA focuses on building
computational models, from using traditional linguistic rules Hatzivassiloglou
and McKeown (1997); Choi and Cardie (2008), to the application of machine
learning methods, from traditional naive bayes and support vector machines
Pang et al. (2002); Moraes et al. (2013); Tan et al. (2009), to early deep
learning models Socher et al. (2013); Kim (2014); Xing et al. (2020), and
finally entering the era of LLMs Hoang et al. (2019); Raffel et al. (2020);
Yang et al. (2019).

#####  5.0.0.0.2 Psychology and Affective Science

In the study of emotion, or affect science Salovey and Mayer (2004); Barrett
(2006); Feinstein (2013), previous work finds that not only the emotion people
perceive influences or prime how they communicate in the moment Barrett
(2006), but language can also influence emotion, which can be observed in
functional magnetic neuroimaging Satpute et al. (2013), and also experiments
showing the act of self-reporting the emotion in writing can change the
physical reaction to the emotion Kassam and Mendes (2013). In his seminal
work, Kahneman (2011) uses the two systems of thinking to reveal the
mechanisms of how people come up with their sentiment, where fast thinking
conforms to the peak-end rule Kahneman et al. (1993), and slow thinking is
more reflective of the overall sentiment.

#####  5.0.0.0.3 Cause-Effect Distinction

Distinguishing the cause from effect based on observational data is a long-
standing and fundamental problem in causality Hoyer et al. (2008); Zhang and
Hyvärinen (2009); Janzing (2019). Existing methods to address this problem are
based on statistics Hoyer et al. (2008); Peters et al. (2010); Shajarisales et
al. (2015); Mooij et al. (2014), physics Janzing (2007); Janzing et al.
(2016), information theory Janzing et al. (2012); Chaves et al. (2014); Mejia
et al. (2022), and algorithmic complexity Janzing and Schölkopf (2010); Jin et
al. (2021). However, we are the first to look at the rich nature of NLP
datasets, and directly approach the difference in the causal and anticausal
mechanisms grounded in interdisciplinary insights.

As for our causal prompts, the most similar studies are the non-causally-
grounded explorations for prompt tuning, such as by varying the patterns of
masked language modeling Schick and Schütze (2022) and using the noisy channel
method Min et al. (2022). However, these studies are not aware of the
underlying causal processes, thus neglecting the connection of prompts with
the causal nature of data, and also the explicit causal story of the
sentiment-review relation.

##  6 Conclusion

In conclusion, we have formulated the task of SA into a prediction problem and
a causal discovery problem. We first identified the cause-effect relation
among existing SA datasets, namely whether the review primes the rating, or
the sentimental judgment primed the review writing process. To achieve this
causal discovery, we obtain insights from existing psychology studies, namely
aligning the above two causal processes with the famous Fast Thinking and Slow
Thinking systems, with their distinct qualitative signals. Given the causal
understanding of the dataset, we further improve the performance of LLMs on SA
using our proposed causal prompts. Our research paves the way for more
causally-aware future research in SA.

## Limitations and Future Work

This study has several limitations. First, the rapid progression of LLMs makes
it challenging to keep up with all newly proposed models and architectures.
Since our work covers only a set of recent LLMs at the time of this study, we
encourage future research to apply our methods to additional LLMs and other SA
datasets.

Although our study is grounded in well-established psychological theories,
there remains the possibility that new theories could emerge, necessitating
updates to the calculation of the $\lambda$ values for the two causal
processes. However, the causal processes identified in this work appear
plausible, as evidenced by the effectiveness of the causally aligned prompts
in improving language model performance.

Regarding the causal graph formulation, we focus on basic bivariate causal
graphs, but future work could include more variables, such as confounders.
Additionally, while this study primarily addresses the English language,
different phenomena may exist across other languages and cultures, which
merits exploration in future research.

## Ethical Considerations

Regarding data concerns and user privacy, our study employs several
established NLP datasets, and the examples we cite do not include sensitive
user information.

Concerning potential stakeholders and misuse, this research primarily
introduces a new perspective on the SA task. A possible negative impact
concerns the general application of SA, which could be used to analyze user
mentality for surveillance or fraudulent purposes. We acknowledge that studies
on SA inherently involve these risks, and we firmly oppose the misuse of SA
models in such contexts.

## Acknowledgment

We thank Luigi Gresele and Dominik Janzing for constructive discussions of the
formulation and landscape of existing literature of causality. We thank Julius
von Kügelgen for brainstorming the idea of quantifiable footprints for causal
and anticausal relations as a followup of Jin et al. (2021). The work also
benefits from the discussion with Jacob Eisenstein on whether NLP datasets are
causal or anticausal as a followup of Veitch et al. (2021). For the domain
knowledge on psychology, especially affect science, we thank Prof Erik C. Nook
at the Department of Psychology at Princeton University for the discussions
and pointers. We thank labmates at LIT lab at University of Michigan for many
helpful feedback on the writing, especially Joan Nwatu, Siyang Liu, and Aylin
Gunal.

This material is based in part upon works supported by the German Federal
Ministry of Education and Research (BMBF): Tübingen AI Center, FKZ:
01IS18039B; by the Machine Learning Cluster of Excellence, EXC number 2064/1 –
Project number 390727645; by the Precision Health Initiative at the University
of Michigan; by the John Templeton Foundation (Grant #61156); by a Responsible
AI grant by the Haslerstiftung; and an ETH Grant (ETH-19 21-1). Zhijing Jin is
supported by PhD fellowships from the Future of Life Institute and Open
Philanthropy, travel support from ELISE (GA no 951847) for the ELLIS program,
and API credits by the OpenAI Researcher Access Program.

## Author Contributions

For the idea formulation, Zhijing Jin drew inspirations from her previous work
Jin et al. (2021) and proposed to extend it to LLMs. During the internship of
Zhiheng Lyu at ETH Zürich hosted under Prof Mrinmaya Sachan and mentored by
Zhijing, Zhiheng first explored whether LLMs show a distinct fingerprint for
causal and anticausal prompts in a workshop paper Lyu et al. (2022). Then
Zhijing got inspired by chats with psychology researchers in affect science
that the phenomena of sentiment-primed text and text-primed sentiments can be
grounded in actual psychology studies. Hence, we started to explore this
paper’s idea together.

Zhiheng Lyu conducted all the experiments and analyses, proposed various novel
experimental designs, structured the storyline, and did tremendous hard work
to make the paper happen.

Zhijing Jin closely mentored the project, proposed the initial idea as well as
the Fast and Slow Thinking formulation, and wrote the entire current version
of the paper.

Fernando Gonzalez joined the project in the final but crucial iteration, and
helped with the implementation of the modeling part, mechanistic
interpretability, as well as part of the data analysis.

Professors Mrinmaya Sachan, Rada Mihalcea, and Bernhard Schölkopf supervised
the project and gave lots of constructive advice on the writing.

## References

  * Barrett (2006) Lisa Feldman Barrett. 2006.  Solving the emotion paradox: Categorization and the experience of emotion.  _Personality and social psychology review_ , 10(1):20–46.
  * Baumeister et al. (1998) RF Baumeister, E Bratslavsky, M Muraven, and DM Tice. 1998.  Ego depletion: is the active self a limited resource?  _Journal of Personality and Social Psychology_ , 74(5):1252–1265.
  * Brown et al. (2020) Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020.  Language models are few-shot learners.  In _Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual_.
  * Chaves et al. (2014) Rafael Chaves, Lukas Luft, Thiago O. Maciel, David Gross, Dominik Janzing, and Bernhard Schölkopf. 2014.  Inferring latent structures via information inequalities.  In _Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, UAI 2014, Quebec City, Quebec, Canada, July 23-27, 2014_ , pages 112–121. AUAI Press.
  * Choi et al. (2020) Gihyeon Choi, Shinhyeok Oh, and Harksoo Kim. 2020.  Improving document-level sentiment classification using importance of sentences.  _Entropy_ , 22(12):1336.
  * Choi and Cardie (2008) Yejin Choi and Claire Cardie. 2008.  Learning with compositional semantics as structural inference for subsentential sentiment analysis.  In _2008 Conference on Empirical Methods in Natural Language Processing, EMNLP 2008, Proceedings of the Conference, 25-27 October 2008, Honolulu, Hawaii, USA, A meeting of SIGDAT, a Special Interest Group of the ACL_ , pages 793–801. ACL.
  * Epstein (1994) Seymour Epstein. 1994.  Integration of the cognitive and the psychodynamic unconscious.  _American psychologist_ , 49(8):709.
  * Fei et al. (2023) Hao Fei, Bobo Li, Qian Liu, Lidong Bing, Fei Li, and Tat-Seng Chua. 2023.  Reasoning implicit sentiment with chain-of-thought prompting.  In _Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)_ , pages 1171–1182, Toronto, Canada. Association for Computational Linguistics.
  * Feinstein (2013) Justin S Feinstein. 2013.  Lesion studies of human emotion and feeling.  _Current opinion in neurobiology_ , 23(3):304–309.
  * Grano et al. (2017) Giovanni Grano, Andrea Di Sorbo, Francesco Mercaldo, Corrado A Visaggio, Gerardo Canfora, and Sebastiano Panichella. 2017.  Software applications user reviews.
  * Hatzivassiloglou and McKeown (1997) Vasileios Hatzivassiloglou and Kathleen R McKeown. 1997.  Predicting the semantic orientation of adjectives.  In _Proceedings of the 35th annual meeting of the association for computational linguistics and eighth conference of the european chapter of the association for computational linguistics_ , pages 174–181. Association for Computational Linguistics.
  * Hatzivassiloglou and Wiebe (2000) Vasileios Hatzivassiloglou and Janyce M Wiebe. 2000.  Effects of adjective orientation and gradability on sentence subjectivity.  In _Proceedings of the 18th conference on Computational linguistics-Volume 1_ , pages 299–305. Association for Computational Linguistics.
  * Hoang et al. (2019) Mickel Hoang, Oskar Alija Bihorac, and Jacobo Rouces. 2019.  Aspect-based sentiment analysis using BERT.  In _Proceedings of the 22nd Nordic Conference on Computational Linguistics, NoDaLiDa 2019, Turku, Finland, September 30 - October 2, 2019_ , pages 187–196. Linköping University Electronic Press.
  * Honnibal and Montani (2017) Matthew Honnibal and Ines Montani. 2017.  spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing.  To appear.
  * Hoyer et al. (2008) Patrik O. Hoyer, Dominik Janzing, Joris M. Mooij, Jonas Peters, and Bernhard Schölkopf. 2008.  Nonlinear causal discovery with additive noise models.  In _Advances in Neural Information Processing Systems 21, Proceedings of the Twenty-Second Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 8-11, 2008_ , volume 21, pages 689–696. Curran Associates, Inc.
  * Janzing (2007) Dominik Janzing. 2007.  On causally asymmetric versions of occam’s razor and their relation to thermodynamics.  _arXiv preprint arXiv:0708.3411_.
  * Janzing (2019) Dominik Janzing. 2019.  The cause-effect problem: Motivation, ideas, and popular misconceptions.  In Isabelle Guyon, Alexander R. Statnikov, and Berna Bakir Batu, editors, _Cause Effect Pairs in Machine Learning_ , pages 3–26. Springer.
  * Janzing et al. (2016) Dominik Janzing, Rafael Chaves, and Bernhard Schölkopf. 2016.  Algorithmic independence of initial condition and dynamical law in thermodynamics and causal inference.  _New Journal of Physics_ , 18(9):093052.
  * Janzing et al. (2012) Dominik Janzing, Joris M. Mooij, Kun Zhang, Jan Lemeire, Jakob Zscheischler, Povilas Daniusis, Bastian Steudel, and Bernhard Schölkopf. 2012.  Information-geometric approach to inferring causal directions.  _Artif. Intell._ , 182-183:1–31.
  * Janzing and Schölkopf (2010) Dominik Janzing and Bernhard Schölkopf. 2010.  Causal inference using the algorithmic Markov condition.  _IEEE Transactions on Information Theory_ , 56(10):5168–5194.
  * Jin et al. (2021) Zhijing Jin, Julius von Kügelgen, Jingwei Ni, Tejas Vaidhya, Ayush Kaushal, Mrinmaya Sachan, and Bernhard Schoelkopf. 2021.  Causal direction of data collection matters: Implications of causal and anticausal learning for NLP.  In _Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing_ , pages 9499–9513, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
  * Kahneman (2011) Daniel Kahneman. 2011.  Thinking, fast and slow.  _Farrar, Straus and Giroux_.
  * Kahneman and Frederick (2002) Daniel Kahneman and Shane Frederick. 2002.  _Representativeness revisited: Attribute substitution in intuitive judgment_ , pages 49–81. Cambridge University Press.
  * Kahneman et al. (1993) Daniel Kahneman, Barbara L Fredrickson, Charles A Schreiber, and Donald A Redelmeier. 1993.  When more pain is preferred to less: Adding a better end.  _Psychological science_ , 4(6):401–405.
  * Kassam and Mendes (2013) Karim S Kassam and Wendy Berry Mendes. 2013.  The effects of measuring emotion: Physiological reactions to emotional situations depend on whether someone is asking.  _PloS one_ , 8(6):e64959.
  * Keung et al. (2020) Phillip Keung, Yichao Lu, György Szarvas, and Noah A. Smith. 2020.  The multilingual Amazon reviews corpus.  In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_ , pages 4563–4568, Online. Association for Computational Linguistics.
  * Kim (2014) Yoon Kim. 2014.  Convolutional neural networks for sentence classification.  In _Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, October 25-29, 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL_ , pages 1746–1751. ACL.
  * LeDoux (1998) Joseph E LeDoux. 1998.  _The emotional brain: The mysterious underpinnings of emotional life_.  Simon and Schuster.
  * Lyu et al. (2022) Zhiheng Lyu, Zhijing Jin, Rada Mihalcea, Mrinmaya Sachan, and Bernhard Schölkopf. 2022.  Can large language models distinguish cause from effect? In _UAI 2022 Workshop on Causal Representation Learning_.
  * Maas et al. (2011) Andrew Maas, Raymond E Daly, Peter T Pham, Dan Huang, Andrew Y Ng, and Christopher Potts. 2011.  Learning word vectors for sentiment analysis.  In _Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies_ , pages 142–150.
  * Mejia et al. (2022) Sergio Hernan Garrido Mejia, Elke Kirschbaum, and Dominik Janzing. 2022.  Obtaining causal information by merging datasets with MAXENT.  In _International Conference on Artificial Intelligence and Statistics, AISTATS 2022, 28-30 March 2022, Virtual Event_ , volume 151 of _Proceedings of Machine Learning Research_ , pages 581–603. PMLR.
  * Meng et al. (2022) Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022.  Locating and editing factual associations in gpt.  _arXiv preprint arXiv:2202.05262_.
  * Min et al. (2022) Sewon Min, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022.  Noisy channel language model prompting for few-shot text classification.  In _Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , pages 5316–5330, Dublin, Ireland. Association for Computational Linguistics.
  * Mooij et al. (2014) Joris M. Mooij, Jonas Peters, Dominik Janzing, Jakob Zscheischler, and Bernhard Schölkopf. 2014.  Distinguishing cause from effect using observational data: Methods and benchmarks.  _CoRR_ , abs/1412.3773.
  * Moraes et al. (2013) Rodrigo Moraes, JoãO Francisco Valiati, and Wilson P GaviãO Neto. 2013.  Document-level sentiment classification: An empirical comparison between svm and ann.  _Expert Systems with Applications_ , 40(2):621–633.
  * Nasukawa and Yi (2003) Tetsuya Nasukawa and Jeonghee Yi. 2003.  Sentiment analysis: Capturing favorability using natural language processing.  In _Proceedings of the 2nd international conference on Knowledge capture_ , pages 70–77. ACM.
  * Ni et al. (2022) Jingwei Ni, Zhijing Jin, Markus Freitag, Mrinmaya Sachan, and Bernhard Schölkopf. 2022.  Original or translated? A causal analysis of the impact of translationese on machine translation performance.  In _Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies_ , pages 5303–5320, Seattle, United States. Association for Computational Linguistics.
  * OpenAI (2023) OpenAI. 2023.  GPT-4 technical report.  _CoRR_ , abs/2303.08774.
  * Ouyang et al. (2022) Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, and Ryan Lowe. 2022.  Training language models to follow instructions with human feedback.  _CoRR_ , abs/2203.02155.
  * Pang et al. (2002) Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. 2002.  Thumbs up? Sentiment classification using machine learning techniques.  In _Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing, EMNLP 2002, Philadelphia, PA, USA, July 6-7, 2002_ , pages 79–86.
  * Pearl (2001) Judea Pearl. 2001.  Direct and indirect effects.  In _UAI ’01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, University of Washington, Seattle, Washington, USA, August 2-5, 2001_ , pages 411–420. Morgan Kaufmann.
  * Pearl et al. (2000) Judea Pearl et al. 2000.  _Causality: Models, reasoning and inference_.  Cambridge University Press.
  * Peters et al. (2010) Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. 2010.  Identifying cause and effect on discrete data using additive noise models.  In _Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2010, Chia Laguna Resort, Sardinia, Italy, May 13-15, 2010_ , volume 9 of _JMLR Proceedings_ , pages 597–604. JMLR.org.
  * Peters et al. (2017) Jonas Peters, Dominik Janzing, and Bernhard Schölkopf. 2017.  _Elements of causal inference: Foundations and learning algorithms_.  The MIT Press.
  * Poria et al. (2023) Soujanya Poria, Devamanyu Hazarika, Navonil Majumder, and Rada Mihalcea. 2023.  Beneath the tip of the iceberg: Current challenges and new directions in sentiment analysis research.  _IEEE Trans. Affect. Comput._ , 14(1):108–132.
  * Radford et al. (2019) Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019.  Language models are unsupervised multitask learners.  _OpenAI Blog_ , 1(8).
  * Raffel et al. (2020) Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J. Liu. 2020.  Exploring the limits of transfer learning with a unified text-to-text transformer.  _Journal of Machine Learning Research_ , 21(140):1–67.
  * Reagan et al. (2016) Andrew J. Reagan, Lewis Mitchell, Dilan Kiley, Christopher M. Danforth, and Peter Sheridan Dodds. 2016.  The emotional arcs of stories are dominated by six basic shapes.  _EPJ Data Sci._ , 5(1):31.
  * Salovey and Mayer (2004) Peter Salovey and John D Mayer. 2004.  _Emotional intelligence_.  Dude publishing.
  * Satpute et al. (2013) Ajay B Satpute, Jocelyn Shu, Jochen Weber, Mathieu Roy, and Kevin N Ochsner. 2013.  The functional neural architecture of self-reports of affective experience.  _Biological psychiatry_ , 73(7):631–638.
  * Schick and Schütze (2022) Timo Schick and Hinrich Schütze. 2022.  True few-shot learning with Prompts—A real-world perspective.  _Transactions of the Association for Computational Linguistics_ , 10:716–731.
  * Schölkopf (2022) Bernhard Schölkopf. 2022.  Causality for machine learning.  In Hector Geffner, Rina Dechter, and Joseph Y. Halpern, editors, _Probabilistic and Causal Inference: The Works of Judea Pearl_ , volume 36 of _ACM Books_ , pages 765–804. ACM.
  * Schölkopf et al. (2012) Bernhard Schölkopf, Dominik Janzing, Jonas Peters, Eleni Sgouritsa, Kun Zhang, and Joris M. Mooij. 2012.  On causal and anticausal learning.  In _Proceedings of the 29th International Conference on Machine Learning, ICML 2012, Edinburgh, Scotland, UK, June 26 - July 1, 2012_. icml.cc / Omnipress.
  * Schölkopf et al. (2021) Bernhard Schölkopf, Francesco Locatello, Stefan Bauer, Nan Rosemary Ke, Nal Kalchbrenner, Anirudh Goyal, and Yoshua Bengio. 2021.  Towards causal representation learning.  _CoRR_ , abs/2102.11107.
  * Schott et al. (2018) Lukas Schott, Jonas Rauber, Matthias Bethge, and Wieland Brendel. 2018.  Towards the first adversarially robust neural network model on mnist.  _arXiv preprint arXiv:1805.09190_.
  * Shajarisales et al. (2015) Naji Shajarisales, Dominik Janzing, Bernhard Schölkopf, and Michel Besserve. 2015.  Telling cause from effect in deterministic linear dynamical systems.  In _Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015_ , volume 37 of _JMLR Workshop and Conference Proceedings_ , pages 285–294. JMLR.org.
  * Socher et al. (2013) Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Ng, and Christopher Potts. 2013.  Recursive deep models for semantic compositionality over a sentiment treebank.  In _Proceedings of the 2013 conference on empirical methods in natural language processing_ , pages 1631–1642.
  * Spirtes and Zhang (2016) Peter Spirtes and Kun Zhang. 2016.  Causal discovery and inference: Concepts and recent methodological advances.  In _Applied informatics_ , volume 3, pages 1–28. SpringerOpen.
  * Tan et al. (2009) Songbo Tan, Xueqi Cheng, Yuefen Wang, and Hongbo Xu. 2009.  Adapting naive bayes to domain adaptation for sentiment analysis.  In _Advances in Information Retrieval, 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings_ , volume 5478 of _Lecture Notes in Computer Science_ , pages 337–349. Springer.
  * Taori et al. (2023) Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023.  Stanford alpaca: An instruction-following llama model.  https://github.com/tatsu-lab/stanford_alpaca.
  * Touvron et al. (2023) Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurélien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample. 2023.  Llama: Open and efficient foundation language models.  _CoRR_ , abs/2302.13971.
  * Truică et al. (2021) Ciprian-Octavian Truică, Elena-Simona Apostol, Maria-Luiza Serban, and Adrian Paschke. 2021.  Topic-based document-level sentiment analysis using contextual cues.  _Mathematics_ , 9(21).
  * Turney (2002) Peter D. Turney. 2002.  Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews.  In _Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics, July 6-12, 2002, Philadelphia, PA, USA_ , pages 417–424. ACL.
  * Tversky and Kahneman (1974) Amos Tversky and Daniel Kahneman. 1974.  Judgment under uncertainty: Heuristics and biases: Biases in judgments reveal some heuristics of thinking under uncertainty.  _Science_ , 185(4157):1124–1131.
  * Veitch et al. (2021) Victor Veitch, Alexander D’Amour, Steve Yadlowsky, and Jacob Eisenstein. 2021.  Counterfactual invariance to spurious correlations in text classification.  _Advances in Neural Information Processing Systems_ , 34.
  * Vig et al. (2020) Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Simas Sakenis, Jason Huang, Yaron Singer, and Stuart Shieber. 2020.  Causal mediation analysis for interpreting neural nlp: The case of gender bias.
  * Wiebe (1994) Janyce M Wiebe. 1994.  Tracking point of view in narrative.  _Computational Linguistics_ , 20(2):233–287.
  * Wolf et al. (2020) Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020.  Transformers: State-of-the-art natural language processing.  In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations_ , pages 38–45, Online. Association for Computational Linguistics.
  * Xing et al. (2020) Xiaoyu Xing, Zhijing Jin, Di Jin, Bingning Wang, Qi Zhang, and Xuanjing Huang. 2020.  Tasty burgers, soggy fries: Probing aspect robustness in aspect-based sentiment analysis.  In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_ , pages 3594–3605, Online. Association for Computational Linguistics.
  * Yang et al. (2019) Zhilin Yang, Zihang Dai, Yiming Yang, Jaime G. Carbonell, Ruslan Salakhutdinov, and Quoc V. Le. 2019.  Xlnet: Generalized autoregressive pretraining for language understanding.  _CoRR_ , abs/1906.08237.
  * Zajonc (1980) Robert B Zajonc. 1980.  Feeling and thinking: Preferences need no inferences.  _American psychologist_ , 35(2):151.
  * Zhang and Hyvärinen (2009) Kun Zhang and Aapo Hyvärinen. 2009.  Causality discovery with additive disturbances: An information-theoretical perspective.  In _Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part II 20_ , pages 570–585. Springer.
  * Zhang et al. (2015) Xiang Zhang, Junbo Zhao, and Yann LeCun. 2015.  Character-level convolutional networks for text classification.  _Advances in neural information processing systems_ , 28.
  * Zhou et al. (2023) Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2023.  Large language models are human-level prompt engineers.  In _The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1-5, 2023_. OpenReview.net.

##  Appendix A Implementation Details

###  A.1 Model Details

#####  A.1.0.0.1 Using Closed-Weight Models

For the use of GPT model series, we use the OpenAI API,333
https://openai.com/api/ with a text generation temperature of 0. We spent
around 400 USD across around 20-30K single API calls.

#####  A.1.0.0.2 Using Open-Weight Models

For reproducibility, we set the generation temperature to 0 for all the models
used in our work. For the open-weight models, GPT2-XL, LLaMa-7B and Alpaca-7B,
it took around 24 hours on 4 GPUs RTX 2080 to generate their predictions on 1K
data points for the 5 paraphrases of the causally-neutral prompt (denoted as
C0), and on 500 data points for the 5 paraphrases of the C1 prompt, and 5
paraphrases of the C2 prompt. The causal tracing experiments with LLaMa-7B and
Alpaca-7B on 100 data points took around 24 hours each using one GPU V100.

###  A.2 Implementation Details for Causal Tracing

We introduce the workings of the causal tracing method Meng et al. (2022) as
follows. First, we compute the hidden states of the residual stream of
LLaMa-7B’s layers for two inputs, (1) the original input: the prompt+review,
and (2) the corrupted input: prompt + a corrupted version of the review by
adding random noise immediately after the token embeddings. Then, we restore
one by one the clean state of the residual stream into the corrupted version
and measure the effect of the clean state on the probability of the originally
predicted token for each token sequence and layer position.

Since this process is highly time-consuming, taking around 12 hours for 50
samples even using the smallest LLaMa model with 7B parameters, we do a case
study on the 7B LLaMa and Alpaca using 100 random samples from the 1K test
set. For these experiments, we follow the idea of APE Zhou et al. (2023) to
use the best-performing prompts on the 1k test set for C1 and C2.

###  A.3 Prompts

####  A.3.1 Prompts to Get paraphrases

Since we need to report the average performance across five paraphrases of the
same prompt, for each original prompt, we call GPT to generate the four
paraphrases.

Below is the prompt that we used for this paraphrase generation process:

> You are an expert in prompt engineering for large language models (LLMs).
> And you are also a native English speaker who writes fluent and
> grammatically correct text.
>
> Given the following prompt for NLP sentiment analysis, you provide four
> alternative prompts.
>
> ####### Original Prompt ####### [Our original prompt]
>
> ####### Alternative Prompt 1 #######
>
> … (Then, we let the model to generate all the way to “Alternative Prompt
> 4”.)

We queried the GPT-4 model with temperature 0 on June 8, 2023.

####  A.3.2 Neutral Prompt

In addition to the standard prompt to query LLMs in the main paper, we show
its four paraphrases in Table 6.

Prompt Design
---
As a proficient data annotator in natural language processing (NLP), your
responsibility is to determine the sentiment of the given review text. Please
assign a sentiment value from “1” (very negative) to “5” (very positive).
Review Text: “[review]”
Sentiment Score:
As a skilled data annotator in the field of natural language processing (NLP),
your task is to evaluate the sentiment of the given review text. Please
classify the sentiment using a scale from “1” (highly negative) to “5” (highly
positive).
Review Text: “[review]”
Sentiment Rating:
As an expert data annotator for NLP tasks, you are required to assess the
sentiment of the provided review text. Kindly rate the sentiment on a scale of
“1” (extremely negative) to “5” (extremely positive).
Review Text: “[review]”
Sentiment Score:
As a proficient data annotator in natural language processing (NLP), your
responsibility is to determine the sentiment of the given review text. Please
assign a sentiment value from “1” (very negative) to “5” (very positive).
Review Text: “[review]”
Sentiment Assesment:
Table 6: Four additional paraphrases of the neutral prompt (C0) generated with
GPT-4.

####  A.3.3 Causal Prompts

In addition to the standard C1 and C2 prompts in the main paper, we show the
four paraphrases for each of them in Tables 7 and 8, respectively.

Prompt Design
---
As a customer sharing my experience, I crafted the following review:
“[review]”
Taking into account the details of my experience, I chose a star rating from
the available options of “1”,“2”, “3”, “4”, or “5”. My ultimate rating is:
As a client providing my opinion, I penned down the subsequent evaluation:
“[review]”
Upon thorough reflection of my encounter, I picked a star rating among the
choices of “1”,“2”, “3”, “4”, or “5”. My conclusive rating stands at:
As a patron expressing my thoughts, I drafted the ensuing commentary:
“[review]”
After meticulously assessing my experience, I opted for a star rating from the
range of “1”,“2”, “3”, “4”, or “5”. My definitive rating turned out to be:
As a consumer conveying my perspective, I authored the following assessment:
“[review]”
By carefully weighing the aspects of my interaction, I determined a star
rating from the possibilities of “1”,“2”, “3”, “4”, or “5”. My final verdict
on the rating is:
Table 7: Four additional paraphrases of the causal prompt C1 generated with
GPT-4. Prompt Design
---
As a customer sharing my experience, I first chose a star rating from the
available choices of “1”,“2”, “3”, “4”, or “5”, and subsequently elaborated on
my decision with the following statement: “[review]”
The review elucidates the reasoning behind my assigned rating of
As a client providing my opinion, I initially picked a star rating from the
range of “1” to “5”, and then proceeded to justify my selection with the
following commentary: “[review]”
The review sheds light on the rationale for my given rating of
As a patron expressing my thoughts, I started by selecting a star rating from
the scale of “1” to “5”, and then offered an explanation for my choice in the
following review text: “[review]”
The review expounds on the basis for my designated rating of
As a consumer conveying my perspective, I began by opting for a star rating
within the “1” to “5” spectrum, and then detailed my reasoning in the
subsequent review passage: “[review]”
The review delineates the grounds for my conferred rating of
Table 8: Four additional paraphrases of the causal prompt C2 generated with
GPT-4.

##  Appendix B Additional Experimental Results

###  B.1 Few-Shot Results

For reproducibility and controllability, we use the zero-shot prompting
setting across the experiments in the main paper, to avoid randomness in few-
shot prompting according to which examples are selected as the few shots, and
the order of the examples.

As a supplementary information in case this is of some readers’ interest, we
provide the few-shot prompting results in Tables 9 and 10.

|  | Random | GPT-3 Few-Shot
---|---|---|---
F1 | Overall | 19.82 $\pm$2.07 | 63.35 $\pm$0.80
C1 Subset | 21.36 $\pm$2.26 | 54.44 $\pm$1.24
C2 Subset | 20.43 $\pm$2.95 | 75.65 $\pm$0.45
Accuracy | Overall | 19.78 $\pm$2.07 | 64.14 $\pm$0.86
C1 Subset | 20.61 $\pm$2.23 | 54.22 $\pm$1.28
C2 Subset | 18.86 $\pm$2.78 | 75.18 $\pm$0.53
Table 9: Few-shot performance of the standard SA prompts on Yelp-5. We use five paraphrases for the prompt, and report the average performance with the standard deviation.  |  | Random | GPT-3 Few-Shot
---|---|---|---
F1 | Data=C1, Prompt=C1 | 20.47 $\pm$2.47 | 49.18 $\pm$0.76
Data=C1, Prompt=C2 | 20.26 $\pm$2.31 | 52.79 $\pm$2.64
Data=C2, Prompt=C1 | 22.35 $\pm$3.02 | 80.46 $\pm$1.29
Data=C2, Prompt=C2 | 20.35 $\pm$2.18 | 75.88 $\pm$1.86
Acc | Data=C1, Prompt=C1 | 19.60 $\pm$2.67 | 50.12 $\pm$0.71
Data=C1, Prompt=C2 | 19.68 $\pm$2.46 | 53.83 $\pm$2.46
Data=C2, Prompt=C1 | 20.97 $\pm$3.19 | 81.21 $\pm$1.17
Data=C2, Prompt=C2 | 19.03 $\pm$2.18 | 76.36 $\pm$1.53
Table 10:  Few-shot performance on Yelp using two different causal prompts on
the two causal subsets. We use five paraphrases for each prompt, and report
the mean performance with the standard deviation.

###  B.2 $\lambda_{1}$-$\lambda_{2}$ Distribution Plot

Hence, we plot the distributions of the C1- and C2-dominant samples in the
$\lambda_{1}$-$\lambda_{2}$ plot in Figure 3.

Figure 3: The $\lambda_{1}$-$\lambda_{2}$ plot on Yelp-5 (left), Amazon
(middle), and App Review (right). We draw the $y=x$ diagonal line, and the
orange dots in the upper-left triangle represent the C1-dominant subset, and
green dots in the lower-right triangle are the C2-dominant subset.

##  Appendix C Emotion Arc Clustering

We analyze the emotional arc patterns of Yelp reviews. Reagan et al. (2016)
identified 6 basic emotional arc shapes in stories. However, reviews are
usually shorter and therefore present fewer variations. We take each sentence
of the review and predict its sentiment. Then we divide the review into ten
bins and compute an average sentence sentiment for each decile to make reviews
with different lengths comparable. Reviews shorter than 10 sentences generate
null values for some deciles which we fill with the information of the next
decile. In Figure 4, we illustrate the 4 clusters found, they have the
following characteristics:

#####  C.0.0.0.1 Positive + Early Rise:

This cluster primarily comprises highly positive reviews, where customers
express satisfaction and praise for their overall experience. Interestingly,
21.1% of these reviews begin with a negative first sentence, which often
indicates initially low expectations or a negative first impression. However,
despite the initial negativity, the reviews tend to turn positive as customers
elaborate on their positive experiences.

#####  C.0.0.0.2 Negative + Early Fall:

This cluster mainly consists of predominantly negative reviews. Similarly to
the Positive cluster, some reviews (28.14%) start with a sentence with the
opposite sentiment, usually indicating high expectations followed by
disappointment.

#####  C.0.0.0.3 Rise:

The main characteristic of this cluster is the positive ending of the review,
despite the initial negativity observed in the first half, with an average
sentiment of -1.63. An important fraction of the reviews in this cluster
(52.49%) start with a positive comment as a summary, but then proceed to
highlight the negative aspects of the experience. Despite the initial
criticisms, the reviews conclude with positive points, suggesting that the
overall experience was still satisfactory.

#####  C.0.0.0.4 Fall:

In contrast to the previous cluster, the Fall cluster is characterized by a
negative ending of the review, despite a generally positive first half with an
average sentiment of 2.18. An important proportion (36%) of the reviews in
this cluster begin with a negative comment as a summary, but then proceed to
describe the positive aspects before eventually highlighting the negative
ones. This cluster showcases a shift in sentiment from positive to negative,
indicating a decline in satisfaction as the review progresses.

Positive + Early Rise
---
Review: Was there last Friday. Seats right in front if the stage. The show was
good. The headliner, while a bit long, was good. Fantastic service from our
waitresses. Will definitely go back.
Review: This is by far my favorite Panera location in the Pittsburgh area.
Friendly, plenty of room to sit, and good quality food & coffee. Panera is a
great place to hang out and read the news - they even have free WiFi! Try
their toasted sandwiches, especially the chicken bacon dijon.
Negative + Early Fall
Review: Pass on this place, there are better restaurants mere feet away.
The menu here is too large, which is a sure sign none of the food is going to
be good. And, its not good. Some of the salads are alright, but its just not
good food.
The service is friendly and prompt, but the beer is over priced. They do have
a good selection though.
This place is open late if you need a bite to eat, but there are so much
better options out there.
Review: Wings are overpriced. And the quality of them are bad. They were tough
and greasy. The staff are pleasant but then over all experience was too
expensive for a sports bar.
Rise
Review: To be honest, I feel that this is one of the most overpriced
restaurants in the entire city. The food is average to good, the place is
beautiful with outdoor seating, but in my opinion the price is just not worth
it. They have a really good happy hour, so I would definitely recommend going
to that and maybe trying an appetizer or two.
Review: The first time I came here, I waited in line for 20 minutes. When it
was my turn, I realized I left my wallet in the car. It hurt so bad, I didn’t
come back for a year.
I can walk to this place from my house- which is dangerous because those
biscuits are just OH SO DREAMY. I can’t describe them. Just get some.
Do I feel guilty about noshing on fabulous Strawberry Napoleons and Jewish
Pizza (kind of like a modified, yet TOTALLY delicious fruitcake bar) at
10:15am? Hecks, naw… But they do have quiche and some other breakfast-y items
for those who prefer a more traditional approach to your stomach’s opening
ceremony.
Just go early :) They open at 10 on Saturdays. And bring cash…it’s easier that
way.
Fall
Review: It’s cheap, I’ll say that, but otherwise it’s bland food served by
workers who mostly don’t seem to notice they’re working, and when they do,
only respond snarkily. There are many better vegetarian and vegan options to
choose from
Review: I do like my Mad Mex, however predictable and non-authentic it may be.
The portion sizes are mammoth and I come away with a satisfied sense of
regret. Their beer menu is happily extensive. Charging me $9 for chips and
salsa is a bit of crime, wouldn’t ya say though!?! I mean, c’mon! Our service
has most times been lacking–a bit rushed and on the inattentive side. Also,
why do you require your wait staff to not servestraws/ lemons/etc unless asked
by cusotmers—weirdness-cut out these odd cost-cutting, anti-service friendly
measures please
Table 11: Example reviews for each emotion arc cluster. Figure 4: Four emotion
arc clusters.

##  Appendix D Additional Interpretability by Shapley Values

We further analyze the effect of each part of the prompts on LLaMa’s
predictions. Using 50 reviews, we compute the shapley values of each token. In
Figure 5 we observe that the tokens with the largest shapley values are the
ones in the end, which is expected since they are the ones helping to form a
grammatically correct sentence. To account for that, we subtracted the average
shapley values computed for the other possible start rating answers. In Figure
6 we show the adjusted shapley values. We observe that the tokens in prompt C1
have a larger effect than the tokens in prompt C2. The words introducing the
review have a positive effect on C2 but a negative one on C1. Whereas, the
phrase “I chose a star rating” has a negative effect on C2 but a positive one
on C1.

Figure 5: Shapley values for the 2 types of prompt. Figure 6: Adjusted shapley
values for the 2 types of prompt.