#  Psychologically-Inspired Causal Prompts

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

###### Abstract

NLP datasets are richer than just input-output pairs; rather, they carry
causal relations between the input and output variables. In this work, we take
sentiment classification as an example and look into the causal relations
between the review ($X$) and sentiment ($Y$). As psychology studies show that
language can affect emotion, different psychological processes are evoked when
a person first makes a rating and then self-rationalizes their feeling in a
review (where the sentiment causes the review, i.e., $Y\rightarrow X$), versus
first describes their experience, and weighs the pros and cons to give a final
rating (where the review causes the sentiment, i.e., $X\rightarrow Y$).
Furthermore, it is also a completely different psychological process if an
annotator infers the original rating of the user by theory of mind (ToM)
(where the review causes the rating, i.e., $X\xrightarrow{\mathrm{ToM}}Y$). In
this paper, we verbalize these three causal mechanisms of human psychological
processes of sentiment classification into three different causal prompts, and
study (1) how differently they perform, and (2) what nature of sentiment
classification data leads to agreement or diversity in the model responses
elicited by the prompts. We suggest future work raise awareness of different
causal structures in NLP tasks.111Our code and data are at
https://github.com/cogito233/psych-causal-prompt.

Figure 1:  The review sentiment classification task might have different
underlying psychological processes. We frame each of the three causal
processes with a prompt, denoted as causal prompts C1, C2, and C3.

##  1 Introduction

Most research on computational methods for NLP treats each task as input-
output pairs. Take sentiment classification as an example. The common approach
is to consider the review $X$ as input and the sentiment $Y$ as the output,
from which the model tries to learn an optimal prediction $P(Y|X)$ (see the
surveys Liu and Zhang, 2012; Ravi and Ravi, 2015; Poria et al., 2020), making
it a text classification task for which a standard text classifier can be
finetuned (Kim, 2014; Yin et al., 2019, inter alia).

However, there can be rich underlying causal structures between the input and
output variables. For example, for the Yelp review data (Zhang et al., 2015),
we list three different psychological processes that can lead to the review-
sentiment pairs in Figure 1: (C1) A user first makes a rating, and then self-
rationalizes their feeling in a review (where an example psychological process
is to justify their intuitive emotions), (C2) a user first describes their
experience in a review, and then decides the rating (where the rating is often
more rational, e.g., consistent with the facts in the review), and (C3) an
annotator infers the original rating of a user by theory of mind (ToM) Stich
(1983); Frith and Frith (2005), which is possible for text classification
tasks in general.

Figure 2:  Performance of GPT3-Instruct Ouyang et al. (2022) induced by C1
(left), 2 (middle) and 3 (right) in terms of accuracy, weighted F1, and the
distribution of information entropy for predictions across 5K English samples
in the Amazon review test set Keung et al. (2020).

Figure 3:  Performance of GPT3-Instruct Ouyang et al. (2022) induced by C1
(left), 2 (middle) and 3 (right) in terms of accuracy, weighted F1, and the
distribution of information entropy for predictions across 10K samples in the
Yelp-5 test set (Zhang et al., 2015).

Figure 4:  Performance of GPT3-Instruct Ouyang et al. (2022) induced by the C1
(left), 2 (middle) and 3 (right) in terms of accuracy, weighted F1, and the
distribution of information entropy for predictions across the rotten-tomatoes
dataset.

In this short paper, we aim to provide insights to the question: how do such
different psychological causal processes matter to NLP tasks? We use sentiment
classification as an example across this paper, for which we use psychology
theories from affective science, create different causal prompts that
correspond to different psychological processes, and investigate two
questions: (Q1) What different model behaviors do they elicit? And (Q2) What
do model predictions elicited by the different causal prompts tell us about
the nature of sentiment classification?

We show that the three causal prompts lead to comparable model performance,
but different certainty in their predictions. We further identify that in
sentiment classification data, longer, more emotionally explicit reviews lead
to more agreement across the causal prompts, whereas shorter, more implicit
reviews lead to diverse predictions by the different causal prompts, which
inspire us to reflect on the framing of the sentiment classification task,
especially for reviews with more nuanced sentiment that could change by the
underlying psychological process.

##  2 Interdisciplinary Inspiration

Insights from Affective Science. In the study of emotion, or affect science
Salovey and Mayer (2004); Barrett (2006); Feinstein (2013), the interaction
between language and affect has long been an important research area. For the
(1) causal process, it is rather intuitive how emotion influences language, as
the emotion people perceive influences how they communicate in the moment
Barrett (2006). For the (2) causal process, affect science also have
interesting discoveries of how language influences emotion. For example, the
act of self-reporting on an emotional state is shown to change the physical
reaction to an emotional situation Kassam and Mendes (2013). Such change from
language to emotions can also be observed in functional magnetic neuroimaging
Satpute et al. (2013). For the (3) causal process, how a person infers the
affect of another person falls into the study of ToM Stich (1983); Frith and
Frith (2005), which can be caused by both the stimulus and this person’s
capacity to infer the other person’s mental state, causing perhaps inter-
annotator variances from due to various understandings.

Causal and Anticausal Learning in NLP. Our work also connects to the
distinction of causal and anticausal learning in NLP Jin et al. (2021), which
distinguishes NLP tasks according to whether it learns to predict the cause
from the effect, or the effect from cause. Applying to our work, the nature of
the sentiment classification task changes if the underlying causal mechanisms
flip. For example, if the C1 is the ground truth on a certain dataset, then a
sentiment classifier conducts anticausal learning, whereas if C2 or C3 is the
ground truth, then the sentiment classification task becomes causal learning.

##  3 How Do Different Prompts Behave?

In this section, we first explore Q1, namely what different behaviors the
three different causal prompts can elicit.

####  3.0.0.0.1 Choosing the Causal Prompts

Based on the three different psychological processes, we want to design three
causal prompts for our follow-up analysis. We consider the following
desiderata when coming up with the prompts: (1) Each prompt should
unambiguously represent the causal process it correspond to. (2) The prompts
should look like natural language, but not broken erraneous sentences. (3) The
cross-causality comparison of the three prompts should be meaningful, which
means that we need to account for other types of noises through which a prompt
will lead to different responses of the LLMs. Following these principles, we
perform a careful prompt selection process detailed in Appendix A, with
awareness of a broad literature on prompt design, including the survey Liu et
al. (2021) and recent work Khashabi et al. (2022); Kojima et al. (2022).

The three causal prompts we adapt are shown in Figure 1 (denoted as C1, C2,
and C3), where we first ensure criteria (1) and (2), and then enforce
criterion (3) to control the three prompts in terms of the structure, word
choices, and lengths. To ensure that our three prompts have been well
controlled, we also conduct additional analysis in Appendix A, such as how
verbosity (i.e., keeping the meaning but changing the length) negatively
affect three prompts in a similar way, and how perturbation in the prompts
also uniformly decreases the performance of the three prompts. Our prompts
have relatively comparable length, word choice, and naturalness.

####  3.0.0.0.2 Comparing the Prediction Distributions

With the three carefully selected causal prompts, we observe their behavior on
two commonly used English sentiment classification dataset, Yelp-5 Zhang et
al. (2015)222https://huggingface.co/datasets/Yelp_review_full and Amazon
review Keung et al.
(2020).333https://huggingface.co/datasets/amazon_reviews_multi The dataset has
five labels corresponding to the 1 – 5 ratings. We run the best available
autoregressive LLM, GPT3-Instruct Ouyang et al. (2022), on this fine-grained
five-class classification, where which we can obtain richer information by
comparing the predicted distribution over all five labels induced by different
causal prompts. We report experimental details in Appendix B.

Reviews with High Agreement in Predictions
---
Review: Terrible service!! Rude manager! Lost a customer for life. And the
manager didn’t care.
Rating: C1:  1 star. C2: 1 star. C3:  1 star. GT:  1 star.
Review: OUTSTANDING COMMUNICATION, SERVICE AND QUALITY REPAIR!
I couldn’t be happier!
FREE loan car! No, or partial deductible!
Only 8 business days to make my 2006 Solara look like new! I HIGHLY recommend
them!
Tell ’em I sent you!!!
Rating: C1:  5 stars. C2:  5 stars. C3:  5 stars. GT:  5 stars.
Reviews with High Diversity in Predictions
Review: Confession…. I didn’t eat here.
Truth…. I did walk in on Bill Clinton eating in the private dining room and
was promptly told by the Secret Service to leave.
For the record… He looked like he was enjoying his meal.
Rating: C1:  1 star. C2:  1 star. C3:  5 stars. GT:  5 stars.
Review: The waiting is too long!! They should expand their patio, offer a
complementary drink if you wait is more then an hour! Who waits an hour to go
out to eat! This is the reason i dont come here. She wanted to come here!
Rating: C1:  1 star. C2:  2 stars. C3:  1 star. GT:  2 stars.
Table 1: Example reviews with high agreement or diversity in predictions by the three causal prompts (C1, C2, and C3). We also show the ground-truth label (GT). | # Samples | # Words/Sample | # Opinion Words | Labels (%) | Diversity
---|---|---|---|---|---
| Pos | Neg | P$+$N | 1 | 2 | 3 | 4 | 5
Overall | 10,000 | 177.06 | 6.33$\pm$5.54 | 3.12$\pm$3.59 |  9.44 | 20 | 20 | 20 | 20 | 20 |  0.2337
Random (n=500) | 500 | 177.14 | 6.28$\pm$5.31 | 3.08$\pm$3.58 |  9.36 | 19 | 22 | 16 | 22 | 19 |  0.2214
Same Correct: C1=C2=C3=GT | 5,994 | 184.17 | 6.51$\pm$5.59 | 3.28$\pm$3.68 |  9.79 | 25 | 17 | 17 | 16 | 23 |  0.1679
Same Incorrect: C1=C2=C3$\neq$GT | 2,052 | 163.31 | 6.04$\pm$5.28 | 2.84$\pm$3.44 |  8.89 | 11 | 23 | 24 | 25 | 15 |  0.2113
Diverse: Not (C1=C2=C3) | 1,954 | 169.68 | 6.04$\pm$5.63 | 2.90$\pm$3.43 |  8.94 | 12 | 24 | 22 | 24 | 15 |  0.4591
Same Correct |  |  |  |  |  |  |  |  |  |  |
10% Data with Lowest Diversity | 599 | 231.90 | 6.39$\pm$5.78 | 5.10$\pm$4.52 |  11.49 | 53 | 9 | 23 | 3 | 10 |  0.0167
10% Data with Highest Diversity | 599 | 170.47 | 5.68$\pm$5.21 | 3.01$\pm$3.39 |  8.69 | 23 | 29 | 14 | 23 | 9 |  0.4002
Same Incorrect |  |  |  |  |  |  |  |  |  |  |
10% Data with Lowest Diversity | 205 | 159.96 | 6.23$\pm$4.85 | 2.74$\pm$2.45 |  8.97 | 4 | 40 | 19 | 32 | 3 |  0.0503
10% Data with Highest Diversity | 205 | 178.51 | 5.74$\pm$5.93 | 3.25$\pm$3.74 |  8.99 | 17 | 24 | 30 | 11 | 16 |  0.4163
Diverse |  |  |  |  |  |  |  |  |  |  |
Where C1$\neq$GT | 972 | 160.63 | 5.69$\pm$5.58 | 2.80$\pm$3.30 |  8.49 | 16 | 21 | 23 | 22 | 15 |  0.4639
Where C2$\neq$GT | 1,067 | 165.22 | 5.85$\pm$5.43 | 2.76$\pm$3.27 |  8.61 | 11 | 24 | 22 | 26 | 15 |  0.4635
Where C3$\neq$GT | 1,013 | 167.27 | 5.91$\pm$5.61 | 2.81$\pm$3.38 |  8.72 | 10 | 25 | 23 | 22 | 18 |  0.4518
Table 2:  Statistics of subsets of data which induces different performance
properties of the three causal prompts. We include subsets of test samples
where all predictions all the same and correct (Same Correct), the same but
incorrect (Same Incorrect), and diverse. For the two subsets with the same
argmax predictions, we analyze the subsets that leads to the 10% least diverse
and most diverse model predictions. For the diverse argmax predictions, we
analyze subsets that make each of the causal prompts (C1, C2, and C3) fail.

We show the performance difference across the three prompts for the Amazon
dataset in Figure 4 and Yelp dataset in Figure 3. For each prompt, we first
report the 5-class accuracy and weighted F1 (on the top right corner of each
figure), where C1 is slightly better than the other two.

Then, we also compare the information entropy of the three auto-completion
process. Our intuition is that the entropy might show more subtle difference
across the causal prompts in the log probability. A hypothesis in a recent
work Jin et al. (2021) suggests that for review sentiment datasets, the users
usually first make a rating and then write about it. This corresponds to C1 in
our study, for which we observe noticeably more certainty in its predictions,
through its low information entropy of 0.3344 on average, which is almost half
of the other two causal prompts. Apart from the mean, we also calculate the
skewness, where we can see that C1 is also skewed more towards lower entropy,
with a skewness score of 0.6196.

##  4 What Do Causal Prompts Tell Us about Our Data?

After analyzing the properties of the LLM’s responses to different prompts, we
think from another angle – what do different causal prompts tell us about our
data?

We show our motivations in Table 1. We can see that the reviews with high
agreement across the three causal processes tend to have some explicit
signals, such as clear opinion words, whereas the reviews that lead to large
diversity in the predictions sometimes seem ambiguous or tricky for humans
too. For such nuanced reviews, the subtle difference among the different
psychological processes elicited by the causal prompts may be more pronounced.

As a potential way for us to understand the nature of different sentiment
classification data, e.g., where diversity might come from, we leverage
GPT3-Instruct model Ouyang et al. (2022) and explore Yelp review-sentiment
pairs where the responses by three causal prompts agree or diverge.

Quantifying the Diversity. For a given review $\bm{x}_{i}\in\bm{D}$, each
model predicts a probability distribution $P(y|\bm{x}_{i})$ of labels $y$
given the review. We quantify the diversity across the model responses to each
two different causal prompts by the normalized total variation de Séguier
(1892); Gibbs and Su (2002):

$\displaystyle
d(P_{1},P_{2})=\frac{1}{2}|P_{1}(y|\bm{x}_{i})-P_{2}(y|\bm{x}_{i})|~{},$ (1)

where $P_{1}$ and $P_{2}$ are the distributions of model prediction by two
causal prompts. The metric $d:\mathcal{P}\times\mathcal{P}\rightarrow[0,1]$
maps to a score between 0 and 1, where 0 is no diversity (complete agreement),
and 1 is complete diversity, where two distributions have no overlapped areas.

Quantifying Explicit Opinions. Then, we look into properties of review text
that might induce diverse opinions. One possible feature we can calculate is
the number of explicit opinion words. As a preliminary exploration, we use a
simple heuristic to match words in the review with the commonly used lists of
positive444https://ptrckprry.com/course/ssd/data/positive-words.txt and
negative555https://ptrckprry.com/course/ssd/data/negative-words.txt opinion
words provided by Hu and Liu (2004); Liu et al. (2005).

Findings. With the 10K test samples of Yelp-5, we analyze different subsets of
it according to the prediction diversity. Some interesting properties of the
data include the length of the text, the total number of opinion words, and
the label distribution. For example, text with higher agreement (i.e., low
diversity, the very light blue ones) tends to be those with more opinion
words, and label distributions concentrated at certain labels. In contrast,
text with higher prediction diversity (the darker blue ones) tends to have
fewer opinion words and a relatively even label distribution slightly
concentrated on ratings of 2 – 4, as the decisions over 2 – 4 are more
ambiguous than the two extreme ends 1 and 5.

Takeaways and Future Work. Findings of this analysis inspire us to reflect on
the framing of the sentiment classification task (and potentially many other
NLP tasks). The key question is, how well defined are our tasks? Many tasks,
although seemingly simple at first sight, might be subject to many subtle
conditions. Future work needs to take more into considerations studies in
other disciplines which provide rich insights on various potential causal
processes underlying the input and output variables.

##  5 Conclusion

In this work, we proposed the concept of causal prompts, and conducted
analysis to report their performance, and reflect on the nature of sentiment
classification. We identified that in sentiment classification data, there
exist many nuanced and ambiguous text that lead to diverse predictions that
might correspond to different psychological interpretation of affect. Our
findings raise an open question to the NLP community about how well defined
our tasks are.

## Limitations

The main limitation of this short paper is that we mainly aim at a proof of
concept, to make a suggestion that more care needs to be taken of the
different causal processes we induce in the model. We put hedging on the
experimental results as there could be other LLMs, other prompts and other
datasets to try. In the scope of this short paper, we do not do a grid search
over all combinations, but we encourage future work to explore with more solid
and comprehensive set of experiments. Another limitation is that LLMs are not
perfect. The way we are using LLMs, is based on the recent empirical success
Brown et al. (2020); Ouyang et al. (2022); Chowdhery (2022), and a possibility
that the correlations they capture can evoke psychologically-different causal
processes between the review and the sentiment, as such framing might also be
seen in its training data. In addition, these subtle differences that we
explore might also touch upon another well known phenomenon, the measurement
effect, where the act of measuring (reported sentiment) changes the properties
of the observed (the actual affect). We encourage future work to explore more
in depth. In addition, this study mainly covers the English language, and
perhaps there are different phenomena for different language and cultures.

## Ethical Considerations

For data concerns and user privacy, we use a public dataset, and also the
examples we quote are only for illustration purposes in this research paper.
We remove any mentions of private persons’ names in the examples.

For potential stakeholders and misuse, this study is in general to raise a new
angle of thought for the sentiment classification task. A potential negative
impact might be about the general task of sentiment classification, where a
more accurate model might be used to mine user information, some negative uses
of which include targetted marketing, surveilance, and fraud.

For the concerns over carbon footprints: Our study mainly uses the GPT-3 API.
We try to control our carbon cost by subsampling the originally large test set
to several thousand test samples, which are also relatively representative to
show the trends.

## Acknowledgment

We thank Prof Erik C. Nook at the Department of Psychology at Princeton
University for the discussions and references in affect science that helped us
frame the idea of the paper. 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, as well as the travel
support from ELISE (GA no 951847) for the ELLIS program. We also thank OpenAI
Researcher Access Program for granting our team credits to their API.

## Author Contributions

For the idea formulation, Zhijing Jin drew inspirations from her previous work
Jin et al. (2021) and proposed to extend it to large language models. During
the internship of Zhiheng Lyu at ETH Zürich mentored by Zhijing, he 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 Affective 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. He further proposed
many further explorations, such as the analysis of the cross entropy.

Zhijing Jin closely mentored the project and wrote the paper.

Professors Rada Mihalcea, Mrinmaya Sachan and Bernhard Schölkopf supervised
the project and improved the writing.

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##  Appendix A Design of Causal Prompt

###  A.1 Desiderata of a Good Set of Three Causal Prompts

Based on the three different psychological processes, we want to design three
causal prompts for our follow-up analysis. We consider the following
desiderata when coming up with the prompts: (1) Each prompt should
unambiguously represent the causal process it corresponds to. (2) The prompts
should be expressed in coherent natural language. (3) The cross-causality
comparison of the three prompts should be meaningful, which means that we need
to account for other types of noises through which a prompt will lead to
different responses of the LLMs.

###  A.2 Overall Prompt Selection Process

Following these three principles, we perform a careful prompt selection
process, with awareness of a broad literature on prompt design, including the
survey Liu et al. (2021) and recent work Khashabi et al. (2022); Kojima et al.
(2022).

There are various ways to design a prompt Liu et al. (2021). The two main ways
are discrete prompt design (e.g., Sorensen et al., 2022; Gao et al., 2021) and
continuous prompt design (e.g., Wu and Shi, 2022; Zhu et al., 2022). For
continuous prompt design, we cannot use it for this work, because we need to
keep the interpretability of our prompts so that humans can recognize and
check the causal process it represents (unless future work comes up with an
accurate embedding-to-causal process classifier). For discrete prompt design,
we empirically evaluate some machine-generated paraphrases of our causal
prompts, and find that purely machine-generated text often cannot fit criteria
(1) and (2) together very well. For pure manual design, although the prompts
can perfectly fit (1) and (2), some systematic human bias might be introduced
Cao et al. (2022); Kavumba et al. (2022); Elazar et al. (2021).

To take the best from both worlds, we choose to do LLM-assisted manual framing
of prompts. We use the most powerful autoregressive LLM that is available to
us, the recent GPT3-Instruct Ouyang et al. (2022), and we first describe the
causal scenarios to it (“here is the process how a person …”), and collect 10
different variations of how it rephrases each causal process, using the
default temperature of 0.7 in its response generation.

Then based on the 10 different variations for each prompt, we filter out the
ones that cannot meet criteria (1) and (2). Then, we reframe the candidates
with minimal content word change but trying to follow tips of how to frame
effective instructional prompts Khashabi et al. (2022). As in Figure 1, we
frame the prompts to be a reasonable instruction, while at the same time also
enforcing criterion (3), to control the three prompts as much as possible, in
terms of structure, word choices, and length.

###  A.3 Checking that the Prompts Are Well Controlled

Although our main interest is the differences among prompts of different
causal nature, there could be various noises in the prompt performance coming
from other confounders. For example, two significant factors that could also
affect the performance are the length of the prompt and paraphrases with
slight perturbations in words. Hence, in the following, we conduct some
additional analyses: (1) how verbosity (i.e., keeping the meaning but changing
the length of the prompt) negatively affect three prompts in a similar way,
and (2) how perturbation in the prompts also uniformly decreases the
performance of the three prompts.

Note that in the scope of our paper, we mainly consider these two sources of
noise, but future work can explore other factors.

####  A.3.0.0.1 Comparing the Lengths.

For each causal prompt, we make pairs of long and short variations of it whose
semantics and most word choices are the same, and only the verbosity is
varied, as shown in Figure 5. We plot the performance of all three pairs of
prompts in Figure 5, where we can see that shorter prompts tend to have higher
accuracy, and prompts of relatively the same lengths are comparable. Hence, we
confirm that all the three short prompts we take, as shown in Figure 1, make
up a good set of causal prompts that relatively meet the last criterion.

C1: Rating affects the review
---
Short: I just finished eating at a restaurant. Then I opened my Yelp app. I
first gave a rating, and then justified it by the following review: [review
text] The review explains why I gave it a rating of [Let GPTs complete]
Long: I just finished eating at a restaurant. Then I opened my Yelp app. I
first gave a rating in terms of 1 to 5 stars, and then explained why I gave
the rating by the following review: [review text] The review is an explanation
of why I rated it a [Let GPTs complete]
C2: Review affects rating
Short: I just finished eating at a restaurant. Then I opened my Yelp app. I
first wrote the following review: [review text] Then I read my review and
finally gave a rating of [Let GPTs complete]
Long: I just finished eating at a restaurant. Then I opened my Yelp app. I
first wrote the following review: [review text] Then based on the review, I
gave the rating in terms of 1 to 5 stars. I think this restaurant is worth a
rating of [Let GPTs complete]
C3: Another person guesses the rating from the review
Short: I opened my Yelp app, and started reading reviews of a restaurant. I
saw a user wrote this review: [review text] I think this user gave a rating of
[Let GPTs complete]
Long: I opened my Yelp app, and started to read some reviews of the restaurant
that I wanted to try. I saw a user wrote this review: [review text] I think
this user gave a rating (out of 1 to 5 stars) of [Let GPTs complete]
Table 3: Short and long prompts for each setup. Figure 5: We confirm that the
shorter version of the three causal prompts performs better than the long
version, and the three short versions in Figure 1 are the best, controlled
prompts we can come up with while fitting for the three desiderata.

####  A.3.0.0.2 Comparing with Slight Perturbations.

We also check whether our prompts are controlled in terms of their relative
performance to their variants with small perturbations. We generate a set of
ten slight perturbations of the prompts using the easy data augmentation Wei
and Zou (2019), based on synonym replacement, random insertion, random swap,
and random deletion. We use the implementation in the TextAttack Python
package Morris et al. (2020). We show their performance in 6, and confirm that
the perturbations decrease the performance. Thus, we confirm that, our prompts
are the best performing one that can meet all three criteria, among all
possible local perturbations with different perturbation strengths.

Figure 6: We confirm that our selection of prompts are relatively controlled
in the sense that they are better than their perturbed versions, while meeting
the three desiderata.

####  A.3.0.0.3 Deciding Final Prompts.

The three causal prompts we adapt are shown in Figure 1 (denoted as C1, C2,
and C3), where we first ensure criteria (1) and (2), and then enforce
criterion (3) to control the three prompts in terms of the structure, word
choices, and lengths. To ensure that our three prompts have been well
controlled, we also conduct additional analysis in Appendix A, such as how
verbosity (i.e., keeping the meaning but changing the length) negatively
affect three prompts in a similar way, and how perturbation in the prompts
also uniformly decreases the performance of the three prompts. Our prompts
have relatively comparable length, word choice, and naturalness.

##  Appendix B Experimental Details

###  B.1 Dataset

We run our experiments on the widely used Yelp sentiment classification
dataset Zhang et al. (2015). The original Yelp dataset has 650K samples in the
training set, and 50K samples in the test set. The dataset has the same number
of samples for each of the five labels 1 – 5.

To reduce the computational and carbon costs, we downsample the dataset while
keeping the label balance. We select a random subset of 10K test samples from
the original test set. We report all the performances across the paper using
these 10K test samples.

###  B.2 Model Details

For the use of GPT3-Instruct Ouyang et al. (2022), we use the API provided by
OpenAI,666https://openai.com/api/ and its newest model at the point we publish
this paper, “davinci-text-002” with 175B parameters.

To make our results reproducible, we set the temperature to be 0 for all uses
of OpenAI APIs. At inference time, we obtain the top 5 candidates with their
log probabilities. For the probability of each label 1, 2, …, 5, we add up the
log probabilities of their English word form (e.g., “one”) and the Arabic
number (e.g., “1”).

Since we do not finetune the model, but only call the API to run it in
inference mode, the energy cost is relatively low, with around 20-30K single
API calls, costing around 400 USD budget.

###  B.3 Implementation Details

Since models might have a different existing preference over different labels
(e.g., more like to predict a neutral score such as “3”), we use a small
portion, 1K samples, of the training set to learn an additional parameter
$\bm{\lambda}:=(\lambda_{1},\dots,\lambda_{5})$, which adjusts the model’s
prediction $P(Y=y_{i}|X)$ by a scalar $\lambda_{i}$. We learn $\bm{\lambda}$
by optimizing $L_{1}(P(\arg\max_{y}(\lambda_{y}P(Y=y|X=x))),P(Y))$, for a loss
function $\mathcal{L}$. We list the learned values of $\bm{\lambda}$ in Table
4.

Setup | Prior on Labels
---|---
$\lambda_{1}$ | $\lambda_{2}$ | $\lambda_{3}$ | $\lambda_{4}$ | $\lambda_{5}$
C1 | 0.73 | 0.22 | 0.03 | 0.004 | 0.01
C2 | 0.07 | 0.32 | 0.44 | 0.07 | 0.10
C3 | 0.13 | 0.39 | 0.09 | 0.08 | 0.31
Table 4:  The learned scaling factor for the prediction of the labels 1 – 5
for GPT3-Instruct.

###  B.4 Information Entropy

Given the predicted distribution $P(Y|X=x_{i})$ for each sample
$x_{i}\in\bm{D}$ in the test set $x_{i}$, we calculate The information entropy
of this distribution by

$\displaystyle H(X)=-\sum P(Y|x_{i})\log{P(Y|x_{i})}~{},$ (2)

which is an indicator of the model’s uncertainty over all possible labels.
Note that entropy is closely related to perplexity, which is calculated as
$\mathrm{PPL}(P)=2^{H(P)}$.

###  B.5 Explicit Opinion Word Count

A simple indicator for sentiment is the set of words that carry strong
opinions. To count the occurrences of opinion words, we first tokenize the
review by the GPT2-large tokenizer provided by the transformers library Wolf
et al. (2019), then we remove all symbols that are not regular English
letters, Arabic numbers, and common symbols such as “-,” before matching with
the given opinion word lists.

##  Appendix C More Examples of Reviews with Agreed or Diverse Predictions

See Table 5 for more examples of reviews with high agreement or diversity in
predictions.

Reviews with High Agreement in Predictions
---
Review: Terrible service!! Rude manager! Lost a customer for life. And the
manager didn’t care.
Rating: C1: 1 star. C2: 1 star. C3: 1 star. GT: 1 star.
Review: The first time couple of times I went there they were closer to four
stars, but this last trip put them at a three star. The combo Kung pao was
just average. I really hate meat that is mushy, and that’s what the beef was.
This is just your average Chinese joint with a nice decor.
Rating: C1: 3 stars. C2: 3 stars. C3: 3 stars. GT: 3 stars.
Review: OUTSTANDING COMMUNICATION, SERVICE AND QUALITY REPAIR!
I couldn’t be happier!
FREE loan car! No, or partial deductible!
Only 8 business days to make my 2006 Solara look like new! I HIGHLY recommend
them!
Tell ’em I sent you!!!
Rating: C1: 5 stars. C2: 5 stars. C3: 5 stars. GT: 5 stars.
Review: The sushi is really good, but it’s kinda pricy. The customer service
is kinda weird too. Overall it’s a decent place to go though.
Rating: C1: 3 stars. C2: 3 stars. C3: 3 stars. GT: 4 stars.
Reviews with High Diversity in Predictions
Review: Confession…. I didn’t eat here.
Truth…. I did walk in on Bill Clinton eating in the private dining room and
was promptly told by the Secret Service to leave.
For the record… He looked like he was enjoying his meal.
Rating: C1: 1 star. C2: 1 star. C3: 5 stars. GT: 5 stars.
Review: "Has the potential to be awesome, but unfortunately extremely
disappointing. Bartenders were awful, not nice, and had no idea what they were
doing. Medium rare burger came out well done, with cold fries. It wasn’t even
busy in there and the whole staff looked flustered. Will not be returning.
Rating: C1: 2 stars. C2: 1 star. C3: 1 star. GT: 1 star.
Review: The waiting is too long!! They should expand their patio, offer a
complementary drink if you wait is more then an hour! Who waits an hour to go
out to eat! This is the reason i dont come here. She wanted to come here!
Rating: C1: 1 star. C2: 2 stars. C3: 1 star. GT: 2 stars.
Table 5: Example reviews with high agreement or diversity in predictions by
the three causal prompts (C1, C2, and C3). We also show the ground-truth label
(GT).

##  Appendix D Extended Analysis

###  D.1 Complete Plot of Opinion Words and Diversity

For all the 10K test samples of Yelp, we plot all pairs of the total number of
opinion words and the prediction diversity among all three causal prompts) in
Figure 7.

Figure 7:  The joint distribution of the total number of opinion words and the
diversity in predictions.

###  D.2 LIWC Analysis of Different Subsets

We conduct use the LIWC word categories to analyze the linguistic features of
different subsets in Tables 6 and 7.

Category |  | Score (All) | Score (Subsets) |
---|---|---|---|---
|  |  | Same Correct | Same Incorrect | Diverse |
Word Count |  |  |  |  |  |
Summary Language Variables |  |  |  |
Words/Sentence |  | 138.33 $\pm$ 125.20 | 144.46 $\pm$ 126.86 | 126.85 $\pm$ 118.85 | 131.55 $\pm$ 125.40 |
Linguistic Dimensions |  |  |  |
Total Function Words | {the, of, and, a, to} | 59.92 $\pm$ 56.71 | 62.81 $\pm$ 57.73 | 54.66 $\pm$ 53.63 | 56.57 $\pm$ 56.09 |
Total Pronouns | {that, this, we, which, it} | 10.90 $\pm$ 12.20 | 11.61 $\pm$ 12.61 | 9.72 $\pm$ 11.45 | 9.96 $\pm$ 11.50 |
Personal Pronouns | {we, our, they, them, us} | 5.89 $\pm$ 7.49 | 6.34 $\pm$ 7.80 | 5.11 $\pm$ 7.01 | 5.32 $\pm$ 6.90 |
1st Person Singular | {i, mine, my, im, me} | 1.61 $\pm$ 2.78 | 1.75 $\pm$ 2.94 | 1.41 $\pm$ 2.60 | 1.42 $\pm$ 2.45 |
1st Person Plural | {we, our, us, lets, ourselves} | 1.37 $\pm$ 3.02 | 1.47 $\pm$ 3.13 | 1.17 $\pm$ 2.83 | 1.29 $\pm$ 2.86 |
2nd Person | {you, your, u, ya, ye} | 1.22 $\pm$ 2.11 | 1.27 $\pm$ 2.21 | 1.14 $\pm$ 1.96 | 1.12 $\pm$ 1.96 |
3rd Person Singular | {his, her, he, she, him} | 0.72 $\pm$ 2.12 | 0.83 $\pm$ 2.24 | 0.53 $\pm$ 2.02 | 0.61 $\pm$ 1.81 |
3rd Person Plural | {they, them, themselves, their, theirs} | 0.96 $\pm$ 1.63 | 1.02 $\pm$ 1.66 | 0.86 $\pm$ 1.54 | 0.88 $\pm$ 1.61 |
Impersonal Pronouns | {that, this, which, it, these} | 5.00 $\pm$ 5.54 | 5.26 $\pm$ 5.71 | 4.59 $\pm$ 5.21 | 4.63 $\pm$ 5.32 |
Articles | {the, a an} | 9.51 $\pm$ 9.48 | 9.85 $\pm$ 9.58 | 8.84 $\pm$ 9.08 | 9.18 $\pm$ 9.57 |
Prepositions | {of, to, in, for, with} | 15.35 $\pm$ 15.50 | 16.05 $\pm$ 15.73 | 13.98 $\pm$ 14.83 | 14.62 $\pm$ 15.36 |
Auxiliary Verbs | {is, are, be can, have} | 11.34 $\pm$ 10.45 | 11.85 $\pm$ 10.60 | 10.47 $\pm$ 9.89 | 10.67 $\pm$ 10.45 |
Common Adverbs | {such, also, when, only, where} | 6.19 $\pm$ 6.22 | 6.48 $\pm$ 6.36 | 5.64 $\pm$ 5.81 | 5.84 $\pm$ 6.13 |
Conjunctions | {and, as, or, also, but} | 8.25 $\pm$ 7.92 | 8.60 $\pm$ 8.03 | 7.55 $\pm$ 7.41 | 7.91 $\pm$ 8.05 |
Negations | {not, without, no, cannot, negative} | 2.33 $\pm$ 2.69 | 2.48 $\pm$ 2.78 | 2.06 $\pm$ 2.46 | 2.17 $\pm$ 2.62 |
Other Grammar |  |  |  |
Common Verbs | {is, are, be, using, based} | 19.85 $\pm$ 19.00 | 20.91 $\pm$ 19.39 | 17.97 $\pm$ 18.02 | 18.58 $\pm$ 18.53 |
Common Adjectives | {as, different, new, more, than} | 5.49 $\pm$ 5.30 | 5.67 $\pm$ 5.30 | 5.13 $\pm$ 5.13 | 5.33 $\pm$ 5.46 |
Comparisons | {as, different, more, than, most} | 2.68 $\pm$ 3.07 | 2.79 $\pm$ 3.10 | 2.40 $\pm$ 2.87 | 2.64 $\pm$ 3.14 |
Interrogatives | {which, when, where, how, whether} | 1.28 $\pm$ 1.80 | 1.35 $\pm$ 1.87 | 1.16 $\pm$ 1.66 | 1.21 $\pm$ 1.72 |
Numbers | {two, one, first, three, single} | 0.91 $\pm$ 1.45 | 0.96 $\pm$ 1.48 | 0.78 $\pm$ 1.36 | 0.87 $\pm$ 1.43 |
Quantifiers | {more, each, both, most, all} | 2.60 $\pm$ 2.97 | 2.67 $\pm$ 2.97 | 2.46 $\pm$ 2.89 | 2.54 $\pm$ 3.03 |
Psychological Processes |  |  |  |
Affective Processes | {well, important, problems, energy, problem} | 4.98 $\pm$ 4.58 | 5.19 $\pm$ 4.62 | 4.62 $\pm$ 4.32 | 4.71 $\pm$ 4.68 |
Positive Emotion | {well, important, energy, better, support} | 3.59 $\pm$ 3.53 | 3.71 $\pm$ 3.59 | 3.40 $\pm$ 3.31 | 3.45 $\pm$ 3.54 |
Negative Emotion | {problems, problem, low, critical, difficult} | 1.26 $\pm$ 1.85 | 1.35 $\pm$ 1.91 | 1.09 $\pm$ 1.73 | 1.15 $\pm$ 1.74 |
Anxiety | {uncertainty, pressure, uncertainties, risk, risks} | 0.14 $\pm$ 0.44 | 0.15 $\pm$ 0.45 | 0.12 $\pm$ 0.38 | 0.13 $\pm$ 0.46 |
Anger | {critical, attacks, argue, dominant, arguments} | 0.27 $\pm$ 0.69 | 0.29 $\pm$ 0.72 | 0.21 $\pm$ 0.66 | 0.25 $\pm$ 0.63 |
Sadness | {low, lower, failure, missing, suffer} | 0.27 $\pm$ 0.60 | 0.28 $\pm$ 0.62 | 0.25 $\pm$ 0.57 | 0.25 $\pm$ 0.57 |
Social Processes | {we, our, provide, they, provides} | 7.54 $\pm$ 9.52 | 8.14 $\pm$ 10.02 | 6.44 $\pm$ 8.62 | 6.86 $\pm$ 8.67 |
Family | {family, families, parents, pregnancy, son} | 0.15 $\pm$ 0.55 | 0.17 $\pm$ 0.60 | 0.12 $\pm$ 0.45 | 0.14 $\pm$ 0.49 |
Friends | {contact, neighborhood, neighboring, neighbors, date} | 0.33 $\pm$ 0.70 | 0.33 $\pm$ 0.70 | 0.31 $\pm$ 0.69 | 0.32 $\pm$ 0.70 |
Female References | {female, her, women, females, she} | 0.51 $\pm$ 1.75 | 0.58 $\pm$ 1.91 | 0.39 $\pm$ 1.51 | 0.42 $\pm$ 1.43 |
Male References | {his, male, he, men, son} | 0.48 $\pm$ 1.43 | 0.53 $\pm$ 1.51 | 0.38 $\pm$ 1.34 | 0.43 $\pm$ 1.25 |
Cognitive Processes | {using, based, or, used, results} | 11.93 $\pm$ 11.88 | 12.50 $\pm$ 12.14 | 10.82 $\pm$ 10.89 | 11.35 $\pm$ 11.93 |
Insight | {information, learning, analysis, knowledge, recognition} | 1.64 $\pm$ 2.20 | 1.72 $\pm$ 2.25 | 1.48 $\pm$ 2.09 | 1.53 $\pm$ 2.13 |
Causation | {using, based, used, results, use} | 1.30 $\pm$ 1.81 | 1.37 $\pm$ 1.83 | 1.16 $\pm$ 1.72 | 1.23 $\pm$ 1.80 |
Discrepancy | {problems, problem, need, could, if} | 1.80 $\pm$ 2.42 | 1.90 $\pm$ 2.52 | 1.60 $\pm$ 2.17 | 1.70 $\pm$ 2.32 |
Tentative | {or, most, may, some, any} | 2.77 $\pm$ 3.25 | 2.86 $\pm$ 3.30 | 2.58 $\pm$ 3.12 | 2.66 $\pm$ 3.22 |
Certainty | {all, accuracy, specific, accurate, total} | 1.51 $\pm$ 1.86 | 1.61 $\pm$ 1.91 | 1.32 $\pm$ 1.69 | 1.38 $\pm$ 1.85 |
Differentiation | {or, different, not, than, other} | 4.33 $\pm$ 4.58 | 4.51 $\pm$ 4.71 | 3.99 $\pm$ 4.18 | 4.16 $\pm$ 4.57 |
Perceptual Processes | {show, images, search, fuzzy, image} | 2.68 $\pm$ 3.26 | 2.79 $\pm$ 3.30 | 2.44 $\pm$ 3.12 | 2.59 $\pm$ 3.26 |
See | {show, images, search, image, shows} | 0.87 $\pm$ 1.46 | 0.90 $\pm$ 1.46 | 0.80 $\pm$ 1.46 | 0.88 $\pm$ 1.47 |
Hear | {noise, noisy, music, voice, speech} | 0.53 $\pm$ 1.14 | 0.57 $\pm$ 1.21 | 0.46 $\pm$ 0.97 | 0.49 $\pm$ 1.08 |
Feel | {fuzzy, flexible, weight, weighted, hand} | 0.57 $\pm$ 1.04 | 0.58 $\pm$ 1.05 | 0.53 $\pm$ 0.98 | 0.56 $\pm$ 1.07 |
Biological Processes | {clinical, expression, face, medical, physical} | 3.64 $\pm$ 4.27 | 3.68 $\pm$ 4.24 | 3.52 $\pm$ 4.24 | 3.63 $\pm$ 4.40 |
Body | {face, blood, hand, heart, neurons} | 0.32 $\pm$ 0.81 | 0.34 $\pm$ 0.85 | 0.29 $\pm$ 0.74 | 0.31 $\pm$ 0.77 |
Health | {clinical, medical, physical, health, diagnosis} | 0.24 $\pm$ 0.73 | 0.27 $\pm$ 0.79 | 0.21 $\pm$ 0.64 | 0.21 $\pm$ 0.57 |
Sexual | {prostate, pregnancy, sex, ovarian, arousal} | 0.03 $\pm$ 0.21 | 0.03 $\pm$ 0.22 | 0.02 $\pm$ 0.18 | 0.03 $\pm$ 0.22 |
Ingestion | {expression, water, weight, expressions, expressed} | 2.94 $\pm$ 3.85 | 2.95 $\pm$ 3.83 | 2.90 $\pm$ 3.86 | 2.98 $\pm$ 3.92 |
Drives | {we, approach, our, first, over} | 7.28 $\pm$ 7.69 | 7.68 $\pm$ 7.89 | 6.50 $\pm$ 7.03 | 6.87 $\pm$ 7.62 |
Affiliation | {we, our, social, communication, interaction} | 2.20 $\pm$ 3.67 | 2.33 $\pm$ 3.79 | 1.93 $\pm$ 3.38 | 2.08 $\pm$ 3.55 |
Achievement | {first, work, efficient, obtained, better} | 1.26 $\pm$ 1.61 | 1.35 $\pm$ 1.65 | 1.13 $\pm$ 1.48 | 1.15 $\pm$ 1.59 |
Power | {over, high, order, large, important} | 1.91 $\pm$ 2.41 | 2.02 $\pm$ 2.46 | 1.66 $\pm$ 2.17 | 1.84 $\pm$ 2.47 |
Reward | {approach, obtained, approaches, better, best} | 2.13 $\pm$ 2.27 | 2.23 $\pm$ 2.33 | 1.97 $\pm$ 2.12 | 1.99 $\pm$ 2.25 |
Risk | {problems, problem, security, difficult, lack} | 0.40 $\pm$ 0.78 | 0.43 $\pm$ 0.82 | 0.35 $\pm$ 0.71 | 0.36 $\pm$ 0.71 |
Time Orientations |  |  |  |  |  |
Past Focus | {used, was, been, were, obtained} | 8.20 $\pm$ 9.58 | 8.71 $\pm$ 9.79 | 7.33 $\pm$ 9.34 | 7.57 $\pm$ 9.06 |
Present Focus | {is, are, be, can, have} | 9.53 $\pm$ 9.41 | 9.96 $\pm$ 9.61 | 8.74 $\pm$ 8.60 | 9.06 $\pm$ 9.52 |
Future Focus | {may, then, will, prediction, future} | 1.01 $\pm$ 1.41 | 1.07 $\pm$ 1.45 | 0.94 $\pm$ 1.37 | 0.90 $\pm$ 1.35 |
Relativity | {in, on, at, approach, new} | 14.93 $\pm$ 15.16 | 15.69 $\pm$ 15.51 | 13.58 $\pm$ 14.37 | 14.01 $\pm$ 14.73 |
Motion | {approach, approaches, behavior, changes, increase} | 2.44 $\pm$ 2.94 | 2.59 $\pm$ 3.03 | 2.15 $\pm$ 2.73 | 2.29 $\pm$ 2.86 |
Space | {in, on, at, into, both} | 8.19 $\pm$ 8.54 | 8.56 $\pm$ 8.71 | 7.59 $\pm$ 8.32 | 7.69 $\pm$ 8.19 |
Time | {new, present, first, when, then} | 4.63 $\pm$ 5.43 | 4.91 $\pm$ 5.63 | 4.14 $\pm$ 4.99 | 4.30 $\pm$ 5.20 |
Personal Concerns |  |  |  |  |  |
Work | {performance, learning, analysis, paper, applications} | 1.40 $\pm$ 2.12 | 1.53 $\pm$ 2.27 | 1.20 $\pm$ 1.77 | 1.23 $\pm$ 1.97 |
Leisure | {novel, expression, channels, videos, play} | 1.43 $\pm$ 2.17 | 1.45 $\pm$ 2.22 | 1.45 $\pm$ 2.17 | 1.37 $\pm$ 2.03 |
Home | {address, family, home, neighborhood, neighboring} | 0.49 $\pm$ 1.21 | 0.51 $\pm$ 1.24 | 0.47 $\pm$ 1.23 | 0.45 $\pm$ 1.06 |
Money | {investigate, cost, investigated, free, economic} | 1.03 $\pm$ 1.69 | 1.08 $\pm$ 1.73 | 0.99 $\pm$ 1.71 | 0.94 $\pm$ 1.51 |
Religion | {beliefs, moral, sacrificing, monkeys, agnostic} | 0.04 $\pm$ 0.21 | 0.04 $\pm$ 0.22 | 0.03 $\pm$ 0.21 | 0.03 $\pm$ 0.19 |
Death | {mortality, die, mortality, deaths, death} | 0.04 $\pm$ 0.21 | 0.04 $\pm$ 0.20 | 0.03 $\pm$ 0.22 | 0.04 $\pm$ 0.22 |
Informal Language | {well, o, da, en, um} | 0.48 $\pm$ 0.92 | 0.48 $\pm$ 0.93 | 0.49 $\pm$ 0.91 | 0.49 $\pm$ 0.89 |
Swear Words | {retardation, dummy, screws, screw, retarded} | 0.09 $\pm$ 0.36 | 0.09 $\pm$ 0.38 | 0.08 $\pm$ 0.35 | 0.08 $\pm$ 0.34 |
Netspeak | {o, da, em, k, mm} | 0.12 $\pm$ 0.44 | 0.12 $\pm$ 0.45 | 0.13 $\pm$ 0.44 | 0.12 $\pm$ 0.41 |
Assent | {k, indeed, agree, absolutely, cool} | 0.14 $\pm$ 0.42 | 0.14 $\pm$ 0.42 | 0.13 $\pm$ 0.41 | 0.15 $\pm$ 0.42 |
Nonfluencies | {well, um, mm, er, ah} | 0.10 $\pm$ 0.35 | 0.10 $\pm$ 0.34 | 0.11 $\pm$ 0.37 | 0.10 $\pm$ 0.36 |
Fillers | {rrani, rranr} | 0.02 $\pm$ 0.14 | 0.02 $\pm$ 0.15 | 0.01 $\pm$ 0.12 | 0.02 $\pm$ 0.14 |

Table 6: LIWC analysis results on all Yelp data (Score (All)), and different
subsets (Same Correct, Same Incorrect, and Diverse).

Category |  | Score (All Diverse) | Score (Subsets) |
---|---|---|---|---
|  |  | C1$\neq$GT | C2$\neq$GT | C3$\neq$GT |
Word Count |  |  |  |  |  |
Summary Language Variables |  |  |  |
Words/Sentence |  | 131.55 $\pm$ 125.40 | 124.21 $\pm$ 121.95 | 128.16 $\pm$ 122.18 | 128.97 $\pm$ 127.07 |
Linguistic Dimensions |  |  |  |
Total Function Words | {the, of, and, a, to} | 56.57 $\pm$ 56.09 | 53.21 $\pm$ 54.10 | 55.27 $\pm$ 54.66 | 55.11 $\pm$ 56.56 |
Total Pronouns | {that, this, we, which, it} | 9.96 $\pm$ 11.50 | 9.39 $\pm$ 10.94 | 9.76 $\pm$ 11.35 | 9.71 $\pm$ 11.67 |
Personal Pronouns | {we, our, they, them, us} | 5.32 $\pm$ 6.90 | 5.01 $\pm$ 6.48 | 5.19 $\pm$ 6.80 | 5.19 $\pm$ 6.98 |
1st Person Singular | {i, mine, my, im, me} | 1.42 $\pm$ 2.45 | 1.33 $\pm$ 2.34 | 1.37 $\pm$ 2.41 | 1.40 $\pm$ 2.47 |
1st Person Plural | {we, our, us, lets, ourselves} | 1.29 $\pm$ 2.86 | 1.18 $\pm$ 2.74 | 1.29 $\pm$ 2.94 | 1.18 $\pm$ 2.72 |
2nd Person | {you, your, u, ya, ye} | 1.12 $\pm$ 1.96 | 1.12 $\pm$ 1.90 | 1.07 $\pm$ 1.89 | 1.14 $\pm$ 2.07 |
3rd Person Singular | {his, her, he, she, him} | 0.61 $\pm$ 1.81 | 0.58 $\pm$ 1.73 | 0.58 $\pm$ 1.75 | 0.63 $\pm$ 1.92 |
3rd Person Plural | {they, them, themselves, their, theirs} | 0.88 $\pm$ 1.61 | 0.79 $\pm$ 1.35 | 0.89 $\pm$ 1.68 | 0.84 $\pm$ 1.60 |
Impersonal Pronouns | {that, this, which, it, these} | 4.63 $\pm$ 5.32 | 4.36 $\pm$ 5.13 | 4.55 $\pm$ 5.33 | 4.51 $\pm$ 5.35 |
Articles | {the, a an} | 9.18 $\pm$ 9.57 | 8.60 $\pm$ 9.28 | 8.99 $\pm$ 9.34 | 9.05 $\pm$ 9.74 |
Prepositions | {of, to, in, for, with} | 14.62 $\pm$ 15.36 | 13.68 $\pm$ 14.63 | 14.37 $\pm$ 15.22 | 14.37 $\pm$ 15.54 |
Auxiliary Verbs | {is, are, be can, have} | 10.67 $\pm$ 10.45 | 10.15 $\pm$ 10.19 | 10.31 $\pm$ 10.08 | 10.28 $\pm$ 10.47 |
Common Adverbs | {such, also, when, only, where} | 5.84 $\pm$ 6.13 | 5.48 $\pm$ 5.85 | 5.76 $\pm$ 6.14 | 5.57 $\pm$ 5.82 |
Conjunctions | {and, as, or, also, but} | 7.91 $\pm$ 8.05 | 7.41 $\pm$ 7.89 | 7.69 $\pm$ 7.62 | 7.68 $\pm$ 8.04 |
Negations | {not, without, no, cannot, negative} | 2.17 $\pm$ 2.62 | 2.08 $\pm$ 2.59 | 2.11 $\pm$ 2.51 | 2.12 $\pm$ 2.66 |
Other Grammar |  |  |  |
Common Verbs | {is, are, be, using, based} | 18.58 $\pm$ 18.53 | 17.55 $\pm$ 17.90 | 18.08 $\pm$ 17.85 | 18.07 $\pm$ 18.93 |
Common Adjectives | {as, different, new, more, than} | 5.33 $\pm$ 5.46 | 4.99 $\pm$ 5.16 | 5.25 $\pm$ 5.50 | 5.20 $\pm$ 5.41 |
Comparisons | {as, different, more, than, most} | 2.64 $\pm$ 3.14 | 2.47 $\pm$ 3.01 | 2.64 $\pm$ 3.13 | 2.52 $\pm$ 3.08 |
Interrogatives | {which, when, where, how, whether} | 1.21 $\pm$ 1.72 | 1.12 $\pm$ 1.62 | 1.18 $\pm$ 1.76 | 1.20 $\pm$ 1.72 |
Numbers | {two, one, first, three, single} | 0.87 $\pm$ 1.43 | 0.85 $\pm$ 1.49 | 0.87 $\pm$ 1.42 | 0.86 $\pm$ 1.41 |
Quantifiers | {more, each, both, most, all} | 2.54 $\pm$ 3.03 | 2.44 $\pm$ 2.92 | 2.48 $\pm$ 3.05 | 2.42 $\pm$ 2.89 |
Psychological Processes |  |  |  |
Affective Processes | {well, important, problems, energy, problem} | 4.71 $\pm$ 4.68 | 4.35 $\pm$ 4.30 | 4.62 $\pm$ 4.64 | 4.54 $\pm$ 4.57 |
Positive Emotion | {well, important, energy, better, support} | 3.45 $\pm$ 3.54 | 3.14 $\pm$ 3.30 | 3.38 $\pm$ 3.52 | 3.33 $\pm$ 3.47 |
Negative Emotion | {problems, problem, low, critical, difficult} | 1.15 $\pm$ 1.74 | 1.10 $\pm$ 1.63 | 1.11 $\pm$ 1.65 | 1.10 $\pm$ 1.69 |
Anxiety | {uncertainty, pressure, uncertainties, risk, risks} | 0.13 $\pm$ 0.46 | 0.12 $\pm$ 0.40 | 0.12 $\pm$ 0.38 | 0.14 $\pm$ 0.50 |
Anger | {critical, attacks, argue, dominant, arguments} | 0.25 $\pm$ 0.63 | 0.23 $\pm$ 0.58 | 0.23 $\pm$ 0.59 | 0.22 $\pm$ 0.60 |
Sadness | {low, lower, failure, missing, suffer} | 0.25 $\pm$ 0.57 | 0.26 $\pm$ 0.60 | 0.23 $\pm$ 0.52 | 0.24 $\pm$ 0.57 |
Social Processes | {we, our, provide, they, provides} | 6.86 $\pm$ 8.67 | 6.55 $\pm$ 8.45 | 6.66 $\pm$ 8.34 | 6.79 $\pm$ 8.99 |
Family | {family, families, parents, pregnancy, son} | 0.14 $\pm$ 0.49 | 0.13 $\pm$ 0.44 | 0.13 $\pm$ 0.44 | 0.15 $\pm$ 0.54 |
Friends | {contact, neighborhood, neighboring, neighbors, date} | 0.32 $\pm$ 0.70 | 0.34 $\pm$ 0.73 | 0.30 $\pm$ 0.68 | 0.31 $\pm$ 0.69 |
Female References | {female, her, women, females, she} | 0.42 $\pm$ 1.43 | 0.40 $\pm$ 1.35 | 0.38 $\pm$ 1.39 | 0.43 $\pm$ 1.52 |
Male References | {his, male, he, men, son} | 0.43 $\pm$ 1.25 | 0.43 $\pm$ 1.20 | 0.41 $\pm$ 1.26 | 0.46 $\pm$ 1.34 |
Cognitive Processes | {using, based, or, used, results} | 11.35 $\pm$ 11.93 | 10.71 $\pm$ 11.39 | 11.09 $\pm$ 11.85 | 10.98 $\pm$ 11.73 |
Insight | {information, learning, analysis, knowledge, recognition} | 1.53 $\pm$ 2.13 | 1.40 $\pm$ 1.97 | 1.53 $\pm$ 2.09 | 1.50 $\pm$ 2.24 |
Causation | {using, based, used, results, use} | 1.23 $\pm$ 1.80 | 1.13 $\pm$ 1.69 | 1.21 $\pm$ 1.76 | 1.20 $\pm$ 1.76 |
Discrepancy | {problems, problem, need, could, if} | 1.70 $\pm$ 2.32 | 1.59 $\pm$ 2.22 | 1.66 $\pm$ 2.26 | 1.65 $\pm$ 2.25 |
Tentative | {or, most, may, some, any} | 2.66 $\pm$ 3.22 | 2.51 $\pm$ 3.09 | 2.60 $\pm$ 3.19 | 2.54 $\pm$ 3.11 |
Certainty | {all, accuracy, specific, accurate, total} | 1.38 $\pm$ 1.85 | 1.37 $\pm$ 1.82 | 1.32 $\pm$ 1.83 | 1.34 $\pm$ 1.80 |
Differentiation | {or, different, not, than, other} | 4.16 $\pm$ 4.57 | 3.93 $\pm$ 4.52 | 4.08 $\pm$ 4.48 | 4.02 $\pm$ 4.47 |
Perceptual Processes | {show, images, search, fuzzy, image} | 2.59 $\pm$ 3.26 | 2.41 $\pm$ 3.00 | 2.52 $\pm$ 3.27 | 2.52 $\pm$ 3.25 |
See | {show, images, search, image, shows} | 0.88 $\pm$ 1.47 | 0.82 $\pm$ 1.33 | 0.86 $\pm$ 1.54 | 0.85 $\pm$ 1.50 |
Hear | {noise, noisy, music, voice, speech} | 0.49 $\pm$ 1.08 | 0.46 $\pm$ 0.97 | 0.47 $\pm$ 1.14 | 0.49 $\pm$ 1.03 |
Feel | {fuzzy, flexible, weight, weighted, hand} | 0.56 $\pm$ 1.07 | 0.50 $\pm$ 0.91 | 0.55 $\pm$ 1.09 | 0.55 $\pm$ 1.12 |
Biological Processes | {clinical, expression, face, medical, physical} | 3.63 $\pm$ 4.40 | 3.36 $\pm$ 4.20 | 3.45 $\pm$ 4.22 | 3.49 $\pm$ 4.35 |
Body | {face, blood, hand, heart, neurons} | 0.31 $\pm$ 0.77 | 0.28 $\pm$ 0.75 | 0.28 $\pm$ 0.71 | 0.30 $\pm$ 0.76 |
Health | {clinical, medical, physical, health, diagnosis} | 0.21 $\pm$ 0.57 | 0.19 $\pm$ 0.52 | 0.21 $\pm$ 0.55 | 0.22 $\pm$ 0.58 |
Sexual | {prostate, pregnancy, sex, ovarian, arousal} | 0.03 $\pm$ 0.22 | 0.04 $\pm$ 0.27 | 0.03 $\pm$ 0.21 | 0.03 $\pm$ 0.25 |
Ingestion | {expression, water, weight, expressions, expressed} | 2.98 $\pm$ 3.92 | 2.76 $\pm$ 3.78 | 2.85 $\pm$ 3.79 | 2.83 $\pm$ 3.89 |
Drives | {we, approach, our, first, over} | 6.87 $\pm$ 7.62 | 6.38 $\pm$ 7.28 | 6.79 $\pm$ 7.59 | 6.75 $\pm$ 7.65 |
Affiliation | {we, our, social, communication, interaction} | 2.08 $\pm$ 3.55 | 1.94 $\pm$ 3.44 | 2.06 $\pm$ 3.57 | 1.97 $\pm$ 3.38 |
Achievement | {first, work, efficient, obtained, better} | 1.15 $\pm$ 1.59 | 1.07 $\pm$ 1.50 | 1.13 $\pm$ 1.56 | 1.14 $\pm$ 1.63 |
Power | {over, high, order, large, important} | 1.84 $\pm$ 2.47 | 1.65 $\pm$ 2.18 | 1.79 $\pm$ 2.49 | 1.90 $\pm$ 2.62 |
Reward | {approach, obtained, approaches, better, best} | 1.99 $\pm$ 2.25 | 1.87 $\pm$ 2.16 | 1.96 $\pm$ 2.24 | 1.92 $\pm$ 2.24 |
Risk | {problems, problem, security, difficult, lack} | 0.36 $\pm$ 0.71 | 0.36 $\pm$ 0.71 | 0.39 $\pm$ 0.74 | 0.37 $\pm$ 0.73 |
Time Orientations |  |  |  |  |  |
Past Focus | {used, was, been, were, obtained} | 7.57 $\pm$ 9.06 | 7.16 $\pm$ 8.95 | 7.28 $\pm$ 8.78 | 7.28 $\pm$ 9.12 |
Present Focus | {is, are, be, can, have} | 9.06 $\pm$ 9.52 | 8.56 $\pm$ 9.04 | 8.94 $\pm$ 8.98 | 8.87 $\pm$ 9.96 |
Future Focus | {may, then, will, prediction, future} | 0.90 $\pm$ 1.35 | 0.90 $\pm$ 1.34 | 0.89 $\pm$ 1.29 | 0.91 $\pm$ 1.41 |
Relativity | {in, on, at, approach, new} | 14.01 $\pm$ 14.73 | 13.21 $\pm$ 14.12 | 13.79 $\pm$ 14.58 | 13.78 $\pm$ 15.10 |
Motion | {approach, approaches, behavior, changes, increase} | 2.29 $\pm$ 2.86 | 2.22 $\pm$ 2.87 | 2.23 $\pm$ 2.77 | 2.29 $\pm$ 3.07 |
Space | {in, on, at, into, both} | 7.69 $\pm$ 8.19 | 7.20 $\pm$ 7.86 | 7.60 $\pm$ 8.24 | 7.52 $\pm$ 8.19 |
Time | {new, present, first, when, then} | 4.30 $\pm$ 5.20 | 4.05 $\pm$ 4.91 | 4.22 $\pm$ 4.97 | 4.23 $\pm$ 5.35 |
Personal Concerns |  |  |  |  |  |
Work | {performance, learning, analysis, paper, applications} | 1.23 $\pm$ 1.97 | 1.14 $\pm$ 1.85 | 1.19 $\pm$ 1.85 | 1.27 $\pm$ 2.15 |
Leisure | {novel, expression, channels, videos, play} | 1.37 $\pm$ 2.03 | 1.34 $\pm$ 1.99 | 1.32 $\pm$ 1.92 | 1.31 $\pm$ 1.97 |
Home | {address, family, home, neighborhood, neighboring} | 0.45 $\pm$ 1.06 | 0.42 $\pm$ 1.03 | 0.47 $\pm$ 1.09 | 0.43 $\pm$ 1.04 |
Money | {investigate, cost, investigated, free, economic} | 0.94 $\pm$ 1.51 | 0.88 $\pm$ 1.48 | 0.94 $\pm$ 1.49 | 0.89 $\pm$ 1.50 |
Religion | {beliefs, moral, sacrificing, monkeys, agnostic} | 0.03 $\pm$ 0.19 | 0.03 $\pm$ 0.21 | 0.03 $\pm$ 0.19 | 0.03 $\pm$ 0.19 |
Death | {mortality, die, mortality, deaths, death} | 0.04 $\pm$ 0.22 | 0.04 $\pm$ 0.22 | 0.04 $\pm$ 0.22 | 0.04 $\pm$ 0.24 |
Informal Language | {well, o, da, en, um} | 0.49 $\pm$ 0.89 | 0.45 $\pm$ 0.86 | 0.48 $\pm$ 0.90 | 0.48 $\pm$ 0.86 |
Swear Words | {retardation, dummy, screws, screw, retarded} | 0.08 $\pm$ 0.34 | 0.07 $\pm$ 0.30 | 0.08 $\pm$ 0.35 | 0.08 $\pm$ 0.33 |
Netspeak | {o, da, em, k, mm} | 0.12 $\pm$ 0.41 | 0.11 $\pm$ 0.40 | 0.11 $\pm$ 0.41 | 0.13 $\pm$ 0.43 |
Assent | {k, indeed, agree, absolutely, cool} | 0.15 $\pm$ 0.42 | 0.13 $\pm$ 0.42 | 0.15 $\pm$ 0.42 | 0.14 $\pm$ 0.39 |
Nonfluencies | {well, um, mm, er, ah} | 0.10 $\pm$ 0.36 | 0.09 $\pm$ 0.32 | 0.11 $\pm$ 0.37 | 0.10 $\pm$ 0.34 |
Fillers | {rrani, rranr} | 0.02 $\pm$ 0.14 | 0.02 $\pm$ 0.14 | 0.02 $\pm$ 0.14 | 0.02 $\pm$ 0.14 |

Table 7: LIWC analysis results on all diverse samples (Score (All Diverse))
copied from Table 6, and three types of subsets in the diverse set
(C1$\neq$GT, C2$\neq$GT, and C3$\neq$GT).