Results of the summarizing process for the arXiv paper: 2305.02897v1

In recent years, large language models (LLMs) have shown remarkable performance in natural language processing tasks. However, their lack of explainability and interpretability has raised concerns about their reliability and trustworthiness. Emergent chain-of-thought (CoT) reasoning capabilities promise to address these issues by improving the performance and explainability of LLMs. To evaluate whether a CoT prompt that was previously discovered through automated prompt discovery could show robust performance across experimental conditions and produce the best results when applied to state-of-the-art models, a small-scale study was conducted to compare the performance of a range of zero-shot prompts for inducing CoT reasoning across six recently released LLMs: davinci-002, davinci-003, GPT-3.5-turbo, GPT-4, Flan-T5-xxl, and Cohere command xlarge on a mixture of six question answering datasets from various domains such as science and medicine. The LangChain framework was used to access several APIs for the experiments. The results showed that the CoT prompt discovered through automated prompt discovery demonstrated robust performance across all experimental conditions and produced the best results when applied to GPT 4. This finding suggests that this CoT prompt can generalize well across novel models and datasets. The study also included descriptions of various datasets used in the experiments such as StrategyQA, WorldTree v2, OpenBookQA, MedQA, MedMCQA which require implicit reasoning and multi step answer strategies based on prior knowledge or domain specific knowledge. Additionally critiques strategy requiring initial answers followed by critique then revised response were also included in the experiment. The study concludes that further research is needed to evaluate the performance of CoT prompts on different models and datasets. However this study provides evidence that automated prompt discovery can be a useful tool for developing CoT prompts that generalize well across novel models and datasets.

• Large language models (LLMs) have shown remarkable performance in natural language processing tasks.
• However, their lack of explainability and interpretability has raised concerns about their reliability and trustworthiness.
• Emergent chain-of-thought (CoT) reasoning capabilities promise to address these issues by improving the performance and explainability of LLMs.
• A small-scale study was conducted to compare the performance of a range of zero-shot prompts for inducing CoT reasoning across six recently released LLMs: davinci-002, davinci-003, GPT-3.5-turbo, GPT-4, Flan-T5-xxl, and Cohere command xlarge on a mixture of six question answering datasets from various domains such as science and medicine.
• The CoT prompt discovered through automated prompt discovery demonstrated robust performance across all experimental conditions and produced the best results when applied to GPT 4.
• The study also included descriptions of various datasets used in the experiments such as StrategyQA, WorldTree v2, OpenBookQA, MedQA, MedMCQA which require implicit reasoning and multi step answer strategies based on prior knowledge or domain specific knowledge. Additionally critiques strategy requiring initial answers followed by critique then revised response were also included in the experiment.
• The study concludes that further research is needed to evaluate the performance of CoT prompts on different models and datasets. However this study provides evidence that automated prompt discovery can be a useful tool for developing CoT prompts that generalize well across novel models and datasets.

Large language models (LLMs) are computer programs that can understand and use human language. They are really good at their job, but some people worry that they might not always be reliable or trustworthy because we don't always understand how they work. To make LLMs better, scientists are working on something called "emergent chain-of-thought reasoning" (CoT), which helps the computer explain how it came up with an answer. Scientists did a small experiment to see which CoT prompts worked best with different LLMs when answering questions about science and medicine. They found that one prompt worked well with all of the LLMs, especially GPT-4. The scientists say more research is needed to make sure this works well in other situations too. Definitions: - Large language models (LLMs): Computer programs that can understand and use human language. - Explainability: Being able to explain how something works or why it gave a certain answer. - Interpretability: Being able to understand what something means or how it relates to other things. - Chain-of-thought reasoning (CoT): A way for computers to explain how they came up with an answer by showing the steps they took. - Datasets: Collections of information used for testing and training computer programs.

Exploring the Potential of Emergent Chain-of-Thought Reasoning for Large Language Models

In recent years, large language models (LLMs) have become increasingly popular in natural language processing tasks. However, their lack of explainability and interpretability has raised concerns about their reliability and trustworthiness. To address these issues, researchers have proposed emergent chain-of-thought (CoT) reasoning capabilities as a potential solution to improve the performance and explainability of LLMs. In this article, we will explore a small-scale study conducted to evaluate whether a CoT prompt that was previously discovered through automated prompt discovery could show robust performance across experimental conditions and produce the best results when applied to state-of-the-art models.

Background

The LangChain framework was used to access several APIs for the experiments which included six recently released LLMs: davinci-002, davinci-003, GPT 3.5 turbo, GPT 4, Flan T5 xxl, and Cohere command xlarge on a mixture of six question answering datasets from various domains such as science and medicine. The datasets used in the experiments were StrategyQA, WorldTree v2 , OpenBookQA , MedQA , MedMCQA which require implicit reasoning and multi step answer strategies based on prior knowledge or domain specific knowledge; additionally critiques strategy requiring initial answers followed by critique then revised response were also included in the experiment.

Results

The results showed that the CoT prompt discovered through automated prompt discovery demonstrated robust performance across all experimental conditions and produced the best results when applied to GPT 4. This finding suggests that this CoT prompt can generalize well across novel models and datasets.

Conclusion

This study provides evidence that automated prompt discovery can be a useful tool for developing CoT prompts that generalize well across novel models and datasets; however further research is needed to evaluate its performance on different models and datasets before it can be widely adopted in natural language processing tasks.