# CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting
for Complex Problem Solving

Pei Chen1 Boran Han2 Shuai Zhang2
1 Department of Computer Science and Engineering, Texas A&M University
2 Amazon Web Services
<EMAIL_ADDRESS>
{boranhan<EMAIL_ADDRESS>
Work done as an intern at Amazon Web Services. Corresponding author.

###### Abstract

Large Language Models (LLMs) have shown great ability in solving traditional
natural language tasks and elementary reasoning tasks with appropriate
prompting techniques. However, their ability is still limited in solving
complicated science problems. In this work, we aim to push the upper bound of
the reasoning capability of LLMs by proposing a collaborative multi-agent,
multi-reasoning-path (CoMM) prompting framework. Specifically, we prompt LLMs
to play different roles in a problem-solving team, and encourage different
role-play agents to collaboratively solve the target task. In particular, we
discover that applying different reasoning paths for different roles is an
effective strategy to implement few-shot prompting approaches in the multi-
agent scenarios. Empirical results demonstrate the effectiveness of the
proposed methods on two college-level science problems over competitive
baselines. Our further analysis shows the necessity of prompting LLMs to play
different roles or experts independently. We release the code at:
https://github.com/amazon-science/comm-prompt.

##  1 Introduction

Large Language Models (LLMs) such as GPT Brown et al. (2020); OpenAI (2023),
LLaMA Touvron et al. (2023a, b) and PaLM Chowdhery et al. (2022), have shown
remarkable proficiency in solving many downstream tasks Liu et al. (2021),
without furthering fine-tuning the model parameters. However, their ability is
limited to solving reasoning and mathematical problems Wei et al. (2022b),
especially complicated science problems Ma et al. (2023); Xu et al. (2023);
Ling et al. (2023a). In consideration of this limitation, and the costly fine-
tuning overhead of the LLMs with billion-level parameters, many prompting
methods emerge, i.e., the process of carefully crafting input queries to
effectively communicate with LLMs and obtain desired outputs. Apart from the
benefit of exempting from manipulating the parameters of the LLMs, these
prompting methods seamlessly integrate the pre-trained models into downstream
tasks by eliciting desired model behaviors Sahoo et al. (2024).

Among these endeavored prompting approaches, some of them prompt LLMs to
reason with multiple middle-steps or subproblems for reasoning tasks Wei et
al. (2022b); Wang et al. (2023b); Yao et al. (2023); Hao et al. (2023); Zhou
et al. (2023), some of them prompt LLMs to take advantage of external tools
Gao et al. (2023); Chen et al. (2023). Despite that these methods have further
pushed the upper-bound reasoning and math abilities of LLMs, their potential
for complicated science problems is still awaiting further improvement. Take
the college-level physical problem in Figure 1 as an example, we can observe
that the Chain-of-thought prompting method still suffers from knowledge and
factual errors (incorrect equations used for the given problem) and computing
errors (incorrect calculations).

Figure 1: Chain-of-thought still makes Knowledge and Computing Errors in
Solving the Complex Science Problem.

Recently, agent-based prompting methods that prompt an LLM to play a specific
role or act as an intelligent agent further unlock the ability of LLMs to
solve complicated problems. For example, Xu et al. (2023) prompt an LLM to
play as a domain expert and successfully elicits the LLM to answer domain
questions. Huang et al. (2022); Shinn et al. (2023); Madaan et al. (2023)
prompt LLMs to do self-reflection or self-refinement to correct the mistakes.
Wang et al. (2023a); Sun et al. (2023) prompt LLMs to do planning before
solving a specific task. Wang et al. (2023c) prompts a single agent to play
multiple roles with different personas, and Liang et al. (2023); Chan et al.
(2023); Du et al. (2023) prompt LLMs to play different roles in debating for
problem-solving.

Following these works, we propose a collaborative multi-agent framework (CoMM)
that prompts LLMs to play different roles with different domain knowledge or
task-solving duties for problem-solving. In particular, we propose a multi-
path reasoning method that enables few-shot learning in the multi-agent
framework. Empirical results on multiple complicated college-level science
problems show that our method significantly outperforms strong baselines. Our
further analysis shows that it is beneficial to include multiple agents for
the collaboration, instead of prompting one agent to play multiple roles
altogether.

Figure 2: Overall Framework of CoMM: An Example from College Physics with the
Few-shot Setting.

##  2 Related Work

LLMs have shown remarkable proficiency in solving many downstream tasks Qu et
al. (2020b); Chen et al. (2021); Xu et al. (2024c, b), paving the way towards
Artificial General Intelligence. With the advent of GPT-3 Brown et al. (2020)
and its emergent abilities Wei et al. (2022a) in solving downstream tasks on
both zero-shot and few-shot settings, many decoder-only LLMs follow Ling et
al. (2023b), such as PaLM Chowdhery et al. (2022), LLaMA Brown et al. (2020);
OpenAI (2023), BLOOM Workshop et al. (2023), Claude Bai et al. (2022), OPT
Zhang et al. (2022), Mistral Jiang et al. (2023), Falcon Penedo et al. (2023)
etc. Considering the inference speed and economic expenditure, we choose
GPT-3.5 as the backbone model for all the baselines and our CoMM approach.

In order to unlock the potential of the LLMs in solving downstream tasks Yi
and Qu (2022); Chen et al. (2022); Qu et al. (2020a); Zhang et al. (2023); Yu
et al. (2024); Xu et al. (2024a), many prompting approaches arise, exempting
from manipulating the billion-level parameters Li et al. (2023c). Among these
prompting methods, ordinary prompting methods follow Brown et al. (2020) and
employ task descriptions and sample demonstrations (few-shot) as the prompts
for downstream tasks. To alleviate the difficulty of directly outputting the
answer for LLMs, many prompting methods simplify the process by predicting the
middle reasoning steps (chain-of-thought Wei et al. (2022b)) or answering the
decomposed sub-problems first Wang et al. (2023b); Yao et al. (2023); Hao et
al. (2023); Zhou et al. (2023); Ling et al. (2024). To overcome the lack of
computing ability and outdated knowledge base, some work prompt LLMs to
utilize external tools Gao et al. (2023); Chen et al. (2023).

To further unlock the ability of LLMs in solving complicated problems, agent-
based methods that prompt LLMs to play specific roles trend. Among them,
singe-agent methods only use one instance of LLMs. ExpertPrompt Xu et al.
(2023) prompts an LLM to play as a domain expert and successfully elicits the
LLM to answer domain questions. EmotionPrompt Li et al. (2023a) improves the
performance of agents with emotional prompts. Huang et al. (2022); Shinn et
al. (2023); Madaan et al. (2023) prompts LLMs to do self-reflection or self-
refinement to correct the mistakes. Wang et al. (2023a); Sun et al. (2023)
prompts LLMs to do planning before solving a specific task. Wang et al.
(2023c) prompts a single agent to play multiple roles with different personas.

Another branch of agent-based approaches are with multi-agents. For example,
Liang et al. (2023); Chan et al. (2023); Du et al. (2023) prompt LLMs to play
different roles in debating for problem-solving. ChatEval Chan et al. (2023)
uses multiple agents debating for automatic LLM evaluation. MathChat Wu et al.
(2023b) proposed a conversational framework to solve math problems with the
user and LLM agent’s interactions. Park et al. (2023) and Li et al. (2023b)
prompts LLMs to play as different agents for simulating human behaviors. Our
work is closely related to these works, but our aim is to prompt LLMs to play
different domain experts in a collaborative framework on complicated reasoning
problems, and how to embed the few-shot examples into the multi-agent
framework.

Along with the agent-based prompting methods, many open-sourced applications
come out. For example, AutoGPT Wu et al. (2023a) plays as AI agents that will
attempt to achieve a given goal by breaking it into sub-tasks and using the
internet and other tools in an automatic loop. AutoGen Wu et al. (2023a)
designs a framework for building LLM applications based on multi-agent
conversations. MetaGPT Hong et al. (2023) prompts multi-agent to play product
managers, architects, project managers, and engineers for a software project.
SkyAGI Park et al. (2023) emerges human-behavior simulation capability in LLM.
While sharing the same multi-agent framework, our work focuses on exploring
the effectiveness of the framework, i.e., we aim to answer whether multi-agent
is necessary and how to prompt multiple agents to work collaboratively.

##  3 Methods

In this section, we first formally define the single-agent prompting
framework, and then introduce the formal definition of the multi-agent
prompting framework, and its adaptions to both zero-shot and few-shot settings
(CoMM).

####  Single-agent Prompting

Given a language model $P(\theta)$ and input text $x$, single-agent prompting
takes a function that is applied to the input text $x^{\prime}=f_{prompt}(x)$
(usually defines the target problem or task) and then predict the answer $y$
by the language model that plays as a single problem-solving agent
$P(y|x^{\prime};\theta)$. In the zero-shot setting, the prompting function $f$
does not contain any demonstration examples, while in the few-shot setting,
the prompting function contains a few examples.

####  Multi-agent Prompting

For multi-agent prompting, we will have $n$ language models
$P_{1}(\theta_{1})$, $P_{2}(\theta_{2})$, … , $P_{n}(\theta_{n})$ that play
different agents or roles in the framework. These language models can be the
same ($\theta_{1}=\theta_{2}...=\theta_{n}$) or different
($\theta_{1}!=\theta_{2}...!=\theta_{n}$). For input text $x$, each agent $i$
will have its own prompting functions $f_{prompt}^{i}(x)$ that formats the
input task or problem for the agent. We define the interactions of these
agents as a non-parametric function $\phi(y|g_{1},g_{2},...,g_{n})$ where
$g_{i}=P_{i}(y_{i}|f_{prompt}^{i}(x);\theta_{i})$ and $y_{i}$ is the output
from agent $i$ and $y$ is the final answer.

Prompting Methods | Zero-shot | Few-shot
---|---|---
Moral Scenarios | College Physics | Moral Scenarios | College Physics
Standard Brown et al. (2020) | 38.65 | 44.12 | 38.21 | 48.04
CoT Wei et al. (2022b) | 45.58 | 50.00 | 64.92 | 56.86
Thought Ma et al. (2023) | 49.39 | - | 56.42 | -
CoMM |  52.17 (+ 2.78) |  54.90 (+ 4.90) |  65.03 (+ 0.11) |  64.71 (+ 7.85)
Table 1: Main Test Results (Accuracy, %). Numbers in the parentheses are
performance gains of the CoMM over previous state-of-the-art.

####  Collaborative Zero-shot Scenario

In our collaborative multi-agent setting, we restrict the multiple agents to
inherit from the same language models and the count of agents to be three.
Then we have three language models $P_{1}(\theta),P_{2}(\theta),P_{3}(\theta)$
as the agents: $P_{1}(\theta)$ and $P_{2}(\theta)$ as the problem-solving
experts and $P_{3}(\theta)$ as the summarizar, as shown in Figure 2.

Specifically, for a given input problem $x$, we use a prompt function to turn
it into a system message that defines the collaborative team-working
environment $x_{s}=f_{s}(x)$. For each agent, we define prompting functions to
characterize its role and prompt it to give its solution accordingly. In
particular, for the first expert agent, the prompting function formats the
problem and the system message as $x_{1}=f_{1}(x,x_{s})$, and then gives its
output $P_{1}(y_{1}|x_{1};\theta)$. For the second expert agent, the prompting
function formats the problem, the system message, and the output from $y_{1}$
as $x_{2}=f_{2}(x,x_{s},y_{1})$, and then give its output
$P_{1}(y_{1}|x_{1};\theta)$. For the third summarizer, the prompting function
will also consider the outputs from the two experts
$x_{3}=f_{3}(x,x_{s},y_{1},y_{2})$ and then the agent gives the final answer
$P_{3}(y|x_{3};\theta)$.

For certain specific input tasks, multi-turn discussions are necessary. In
this case, the output of the second expert agent will circulate back to the
first agent as the input prompt again, and then repeat the afore-mentioned
discussions, as demonstrated in the Figure 2.

####  Collaborative Few-shot Scenario

In a multi-agent setting, it is not trivial to add the few-shot examples to
the various agents. Which agent should we give the few-shot examples? We adopt
a multi-path reasoning approach that gives the few-shot examples to the
different agents. In particular, different agents will have their own
expertise-based reasoning path in the few-shot demonstrations. Formally, the
two expert prompting functions $x_{1}=f_{1}(x,x_{s},e_{1})$ and
$x_{2}=f_{2}(x,x_{s},e_{2},y_{1})$ will take exemplars $e_{1}$ and $e_{2}$ as
inputs. Take Figure 2 as an example, the few-shot examples will be added to
both the physicist and the mathematician agents, but with different reasoning
paths. More details can be found in the Appendix A.

##  4 Experiments

In this section, we will first introduce the evaluation datasets and benchmark
that focus on complicated science problems. After that, we introduce the
strong baseline prompting methods for comparison. At last, we introduce the
results of our methods and the baselines on the benchmark.

###  4.1 Datasets

####  College Physics

is a dataset from Massive Multitask Language Understanding (MMLU), which
covers 57 subjects across different domain knowledge. It focuses on college-
level physics problems. These problems are still very challenging and far from
satisfying performance with large language models. Like the example from
Figure 3, LLMs are still suffering from the lack of knowledge and computing
ability.

####  Moral Scenarios

is aother dataset from MMLU Hendrycks et al. (2020). Moral Scenarios focus on
advanced professional-level social science problems that are yet challenging
for large language models, which is among the worst performing tasks for many
language models Ma et al. (2023).

Both datasets are multiple choice questions, and we use the correct rate
(Accuracy) as the metric for comparison.

###  4.2 Baselines

####  Standard

Brown et al. (2020) is the first work that introduced performing tasks without
any task-specific training or examples, relying solely on its general pre-
training with prompting. In this work, we format each problem as "Q:
{question} A:" at zero-shot settings, and as "Q: {question example 1)} A:
{answer example 1} … Q: {question example $n$} A: {answer example $n$} Q:
{question)} A:" for the few-shot setting with $n$ demonstration examples.

####  Chain-of-thought (CoT)

Wei et al. (2022b) improves the Standard prompting approaches by introducing a
series of intermediate natural language reasoning steps that lead to the final
output (chain of thought). It hypothesize that giving the LLMs longer
predicting window, they have better chance to reach the answer, in comparison
with directly requiring them to output the answer. For zero-shot
implementation, we follow the Zero-shot-CoT proposed by Wang et al. (2023a),
and add "Let’s think step by step" prompt before the answer, i.e., "Q:
{question} A: Let’s think step by step.". As for the few-shot implementation,
we follow the indigenous settings from Wei et al. (2022b), i.e., "Q: {question
example 1} A: Let’s think step by step. {answer example 1 with chain of
thought} … Q: {question example $n$} A: Let’s think step by step. {answer
example $n$ with chain of thought} Q: {question} A: Let’s think step by step."
for the few-shot setting with $n$ demonstration examples.

####  Thought Experiment (Thought)

Ma et al. (2023) is a reasoning framework that is specialized in better moral
reasoning by using counterfactual reasoning. It is a multi-agent framework
with multi-step prompting, and each step involves prompting the LLMs to solve
a specific task. Specifically, this method involves employing counterfactual
thinking to envision various, often hypothetical, situations, and then
deliberating on the consequences of these imagined circumstances. By
processing these scenarios, it aids in consolidating intermediate reflections,
thereby leading to a deeper comprehension of the issue at hand and guiding
towards the most appropriate solution. We adopt the same settings for both
zero-shot and few-shot as provided by the Ma et al. (2023).

Benchmark | Settings | Single Agent | Multiple Agents
---|---|---|---
Moral Scenarios | Zero-shot | 27.71 |  52.17 (+24.46)
Few-shot | 42.68 |  65.03 (+22.35)
College Physics | Zero-shot | 42.16 |  54.90 (+12.74)
Few-shot | 56.86 |  64.71 (+07.85)
Table 2: Single Agent v.s. Multiple Agents, (Accuracy, %). Numbers in the parentheses are the performance gains. Settings | Zero-shot | Few-shot
---|---|---
CoT Wei et al. (2022b) | 50.00 | 56.86
One Physicist Only | 47.06 | 44.12
One Mathematician Only | 42.16 | 58.82
Two Physicists | 47.05 | 50.98
Two Mathematicians | 52.94 | 59.80
Both Experts (CoMM) |  54.90 (+1.96) |  64.71 (+4.91)
Table 3: Single Expert v.s. Multiple Experts on College Physics, (Accuracy, %). Numbers in the parentheses are the performance gains. Benchmark | Settings | One Turn (Acc%) | Two Turns (Acc%)
---|---|---|---
Moral Scenarios | Zero-shot | 48.27 | 52.17
Few-shot | 64.92 | 65.03
College Physics | Zero-shot | 54.90 | 45.09
Few-shot | 64.71 | 55.88
Table 4: Single Turn v.s. Multiple Turns.

###  4.3 Settings

#### Backbone Model

For a fair comparison, we use gpt-3.5-turbo-0613111https://openai.com/ as the
backbone model, and set the temperature to be $0$ in all our experiments.

#### Settings for College Physics

We prompt the first agent $P_{1}(\theta)$ to be a physicist, the second agent
$P_{2}(\theta)$ to be a mathematician, and the third agent $P_{3}(\theta)$ to
be the summarizer. In the zero-shot setting, we do not provide demonstration
examples, while in the few-shot setting, we give the same 5 examples for the
two experts, but with different reasoning paths, i.e., the reasoning path of a
physicist role and the reasoning path of a mathematician role individually. We
only prompt the group to discuss once for this benchmark. More details can be
found in the Appendix A.

#### Settings for Moreal Scenarios

In the zero-shot setting, we prompt the first agent $P_{1}(\theta)$ to be a
task decomposer, the second agent $P_{2}(\theta)$ to be a sub-problem solver,
and the third agent $P_{3}(\theta)$ to be the summarize. In the few-shot
setting, we also give each expert $5$ examples, and we prompt the first agent
$P_{1}(\theta)$ to be a chain-of-thought reasoner with CoT reasoning path, the
second agent $P_{2}(\theta)$ to be a Thought reasoner with thought experiment
path, and the third agent $P_{3}(\theta)$ to be the summarize. We prompt the
group to discuss twice for this benchmark. More details can be found in the
Appendix A.

###  4.4 Main Results

The main experimental results are shown in Table 1. It is saliently observable
that the proposed CoMM approach can outperform the state-of-the-art baselines
on both zero-shot and few-shot settings. In detail, it improves with absolute
average improvements of 3.84% at zero-shot setting and 8.23% at few-shot
setting. CoMM improves more in few-shot settings, further demonstrating the
effectiveness of applying the multi-path reasoning approaches in the multi-
agent framework. Also, CoMM improves more on the complicated College Physics
dataset that requires more domain knowledge, further showcasing the efficacy
of CoMM in solving complex problems.

##  5 Analysis

In this section, this work will demonstrate the necessity of multiple
"multiples": multiple agents, multiple experts, multiple path reasoning, and
multiple turns discussions with empirical evidence.

###  5.1 Are Multiple Independent Agents Necessary?

Our proposed CoMM approach prompts multiple instances of LLMs to play
different agents. But why not prompt one single instance of LLMs to play
different roles altogether to solve the target problem? This is similar to the
multi-agent framework proposed by Wang et al. (2023c).

We experiment with the same prompting text of CoMM using a single instance of
LLMs, and the results are shown in Table 2. Apparently, the performance of
multiple agents (CoMM) significantly outperforms the single-agent approach,
across all benchmarks and settings. We hypothesize the possible reason is that
a single instance of LLMs tends to be self-consistent, and prompting it to
switch among different roles confuses the model to make the right predictions.
Our results are in line with the findings from Xu et al. (2023).

###  5.2 Are Multiple Domain Experts Necessary?

In the benchmark of College Physics, we prompt the LLMs to play two experts:
one physicist and one mathematician, aiming at utilizing their domain
knowledge independently in solving the problem collaboratively and
complementarily. We hope the physicist agent can elicit the domain knowledge
of physics and the mathematician agent can overcome the computing errors. Here
we empirically demonstrate whether the multiple domain experts are
collaborating. As shown in Table 3, the single-expert approach shows poor
performance, and could not beat the CoT benchmark. Furthermore, we prompt the
LLMs to play multiple experts but with the same expertise. The results shown
in Table 3 demonstrate that such settings will improve over single-expert
cases, but still under-perform over the multiple different experts settings.
Overall, the results empirically demonstrate the necessity and efficacy of the
multiple-expert collaborative framework.

###  5.3 Are Multiple Turns Discussions Necessary?

As mentioned in Section 3, our proposed CoMM framework supports multiple turns
discussion, which means that the agents can discuss multiple times to reach a
final answer. So are multiple-turn discussions necessary? We experiment on the
benchmark with one-turn discussions and two-turn discussions, as shown in
Table 4.

It turns out that the turns of discussions depend on the benchmark or dataset.
For the Moral Scenarios dataset, two-turn discussions perform better, while
the one-turn discussion performs better on the College Physics dataset. We
hypothesize that the possible rationale could be that the rigorous science
problems in College Physics require the reasoning paths to be concise and
clear. A plethora of discussions may bring confusion and hallucinations that
are not beneficial to such problems. However, the social science problems from
the Moral Scenarios benchmark naturally benefit from thorough discussions, as
many of the truths from social science originated from human discussions to
reach a consensus.

Figure 3: Example results of CoMM on College Physics (the same problem as in
Figure 1).

##  6 Case Study

In this Section, we provide case study of CoMM on different benchmarks.

In Figure 3, we show the reasoning results of the same problem from Figure 1
on College Physics, in which the CoT approach is suffering from incorrect
knowledge and incorrect calculations. In our CoMM framework, the physicist
agent also makes calculation errors. However, the error is corrected by the
mathematician, and finally the team reaches a correct answer. We can observe
that our framework successfully elicit the domain physics knowledge from the
physicist agent and the calculation ability from the mathematician agent.
Also, the three agents closely collaborated with each other, and the physicist
provide domain knowledge, the mathematician take advantage of the knowledge
and correct the calculations, and the summerizer gives the final answer based
on the discussions.

In Figure 4, we delve into a detailed case study conducted on the Moral
Scenarios, specifically focusing on a few-shot learning setting. Notably, the
CoT baseline encounters difficulties in correctly identifying the moral
correctness of the action "trash dog’s poop while walking." This limitation
highlights the challenges faced by LLMs in understanding and judging moral
scenarios. Our model, the CoMM, introduces a more layered approach to this
issue. In the initial phase of our study, both the CoT reasoner and the
Thought reasoner within CoMM failed to accurately assess the moral aspect of
the scenario. This initial failure underscores the intricacies of moral
reasoning and the need for more advanced reasoning capabilities in the models.
However, the scenario undergoes a significant shift following the first round
of discussion within CoMM. After this collaborative reflection, both reasoners
– the CoT and the Thought – adjust their initial assessments and correctly
identify the scenario as morally correct. Additionally, the summarizer in CoMM
plays a crucial role at this point, affirming the correct conclusion reached
by the reasoners and providing a definitive final answer that aligns with
their revised understanding. This case study effectively showcases the initial
challenges and the eventual success of CoMM in moral reasoning, emphasizing
the value of iterative and collaborative reasoning LLM agents.

Figure 4: Example results of CoMM on Moral Scenarios with few-shot setting.

##  7 Conclusion

This study underscores the significant strides made in enhancing the reasoning
capabilities of LLMs through the innovative CoMM prompting framework. By
leveraging a multi-agent, multi-reasoning-path approach, we successfully
prompted LLMs to assume varied roles within a problem-solving team, fostering
a collaborative environment crucial for tackling complex science problems. The
empirical results obtained from two college-level science tasks not only
validate the efficacy of our method but also highlight the potential of few-
shot prompting in multi-agent contexts. More importantly, our analysis reveals
the indispensable role of distinct role-play in achieving more nuanced and
sophisticated problem-solving strategies. This research paves the way for
future explorations into the realm of advanced AI reasoning, particularly in
the application of LLMs to intricate and specialized tasks.

##  8 Limitations

Even though our proposed CoMM framework has further pushed the upper bound of
the reasoning ability of LLMs, the framework is still suffering from
limitations. The proposed CoMM framework still requires task-specific design
to define the experts and reasoning examples. However, this is a common
limitation for all the CoT-style Wei et al. (2022b) approaches. For example,
the CoT approach needs specific designs for the few-shot examples with the
chain of reasoning steps; the Thought baseline Ma et al. (2023) requires
specific thought experiment designs, and it only works on one specific
benchmark (the Moral Scenario from MMLU Hendrycks et al. (2020)). We leave the
automatic prompting design for the CoMM framework as future work.

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##  Appendix A Appendix

Here are the prompts we use for all the settings and benchmarks.

Figure 5: Prompts for College Physics. Figure 6: Prompts for Moral Scenarios.
Figure 7: Few Shot Examples for Physicist. Figure 8: Few Shot Examples for
Mathematician. Figure 9: Few Shot Examples for CoT on Moral Scenarios. Figure
10: Few Shot Examples for Thought on Moral Scenarios.