Large language models (LLMs) have shown strong arithmetic reasoning
capabilities when prompted with Chain-of-Thought (CoT) prompts. However, we
have only a limited understanding of how they are processed by LLMs. To
demystify it, prior work has primarily focused on ablating different components
in the CoT prompt and empirically observing their resulting LLM performance
change. Yet, the reason why these components are important to LLM reasoning is
not explored. To fill this gap, in this work, we investigate ``neuron
activation'' as a lens to provide a unified explanation to observations made by
prior work. Specifically, we look into neurons within the feed-forward layers
of LLMs that may have activated their arithmetic reasoning capabilities, using
Llama2 as an example. To facilitate this investigation, we also propose an
approach based on GPT-4 to automatically identify neurons that imply arithmetic
reasoning. Our analyses revealed that the activation of reasoning neurons in
the feed-forward layers of an LLM can explain the importance of various
components in a CoT prompt, and future research can extend it for a more
complete understanding.

通过研究神经元激活，我们探索了大型语言模型中算术推理能力的重要性，以及神经元激活对 CoT 提示的 components 的影响，并提出了一个基于 GPT-4 的方法来自动识别涉及算术推理的神经元。

研究神经元激活作为统一视角来解释 LLM 的引发思维链的算术推理

An Investigation of Neuron Activation as a Unified Lens to Explain  Chain-of-Thought Eliciting Arithmetic Reasoning of LLMs

The ability to revise in response to feedback is critical to students'
writing success. In the case of argument writing in specific, identifying
whether an argument revision (AR) is successful or not is a complex problem
because AR quality is dependent on the overall content of an argument. For
example, adding the same evidence sentence could strengthen or weaken existing
claims in different argument contexts (ACs). To address this issue we developed
Chain-of-Thought prompts to facilitate ChatGPT-generated ACs for AR quality
predictions. The experiments on two corpora, our annotated elementary essays
and existing college essays benchmark, demonstrate the superiority of the
proposed ACs over baselines.

针对提高学生论述写作水平和有效判断其修改文章的质量的问题，本研究提出了基于 Chain-of-Thought 提示的 ChatGPT 生成论点基础，用于预测其修改后的质量，并在初中和大学学生论文数据集中实验证明了其优越性。

预测争论性写作修改的质量

Predicting the Quality of Revisions in Argumentative Writing

Large language models that are capable of zero or few-shot prompting
approaches have given rise to the new research area of prompt engineering.
Recent advances showed that for example Chain-of-Thought (CoT) prompts can
improve arithmetic or common sense tasks significantly. We explore how such
approaches fare with legal reasoning tasks and take the COLIEE entailment task
based on the Japanese Bar exam for testing zero-shot/few-shot and fine-tuning
approaches. Our findings show that while CoT prompting and fine-tuning with
explanations approaches show improvements, the best results are produced by
prompts that are derived from specific legal reasoning techniques such as IRAC
(Issue, Rule, Application, Conclusion). Based on our experiments we improve the
2021 best result from 0.7037 accuracy to 0.8148 accuracy and beat the 2022 best
system of 0.6789 accuracy with an accuracy of 0.7431.

本文探讨了零样本 / 小样本以及微调方法在法律推理任务中的表现，结果发现，源于特定法律推理技术的提示能够获得最佳结果。实验最终将最佳结果从 0.7037 提升至 0.8148，并且准确性高于 2022 最佳系统的 0.6789，为 0.7431。

合法提示：教授语言模型像律师那样思考

Legal Prompting: Teaching a Language Model to Think Like a Lawyer

Probabilistic models of language understanding are interpretable and
structured, for instance models of metaphor understanding describe inference
about latent topics and features. However, these models are manually designed
for a specific task. Large language models (LLMs) can perform many tasks
through in-context learning, but they lack the clear structure of probabilistic
models. In this paper, we use chain-of-thought prompts to introduce structures
from probabilistic models into LLMs. These prompts lead the model to infer
latent variables and reason about their relationships to choose appropriate
paraphrases for metaphors. The latent variables and relationships chosen are
informed by theories of metaphor understanding from cognitive psychology. We
apply these prompts to the two largest versions of GPT-3 and show that they can
improve paraphrase selection.

本文使用思维导向提示将概率模型结构引入了大型语言模型的上下文学习中，以推断潜在变量并选择合适的隐喻释义，从而改进了模型的模仿能力。