Results of the summarizing process for the arXiv paper: 2304.03262v1

The use of Chain-of-Thought (CoT) prompting has been shown to effectively elicit complex multi-step reasoning from Large Language Models (LLMs). For instance, adding the CoT instruction "Let's think step-by-step" to each input query of the MultiArith dataset improved GPT-3's accuracy from 17.7% to 78.7%. However, it is unclear whether CoT is still effective on more recent instruction finetuned (IFT) LLMs such as ChatGPT. Surprisingly, on ChatGPT, CoT is no longer effective for certain tasks like arithmetic reasoning while still being effective on other reasoning tasks. Moreover, ChatGPT usually achieves the best performance on these former tasks and can generate CoT even without being instructed to do so. This suggests that ChatGPT may have already been trained on these tasks with CoT and thus memorized the instruction. The authors' analysis highlights a potential risk of overfitting/bias toward instructions introduced in IFT, which is becoming more common in training LLMs. Additionally, it indicates possible leakage of the pretraining recipe where one can verify whether a dataset and instruction were used in training ChatGPT. The experiments conducted report new baseline results of ChatGPT on various reasoning tasks and provide novel insights into LLM's profiling, instruction memorization, and pretraining dataset leakage. Further investigation reveals that ChatGPT demonstrates strong reasoning capability without the guidance of CoT prompting for arithmetic reasoning tasks. In contrast to these arithmetic tasks, CoT prompting is still required to elicit the reasoning ability in ChatGPT for other reasoning tasks. These observations suggest that some arithmetic datasets may be included in the pre-training mix, leading to ChatGPT memorizing instructions and autonomously generating rationales. The authors' analyses underscore several fundamental challenges that need addressing in future research such as how the pre-training recipe affects the performance of the inference stage and how to effectively identify whether certain datasets were employed for pre-training or detect data leakage issues. Overall, this study sheds light on the importance of understanding LLMs' behavior in reasoning tasks and highlights potential risks associated with IFT and pretraining dataset leakage.

• Chain-of-Thought (CoT) prompting is effective in eliciting complex multi-step reasoning from Large Language Models (LLMs)
• Adding the CoT instruction "Let's think step-by-step" improved GPT-3's accuracy from 17.7% to 78.7%
• CoT is no longer effective for certain tasks like arithmetic reasoning on ChatGPT, but still effective on other reasoning tasks
• ChatGPT may have already been trained on these tasks with CoT and thus memorized the instruction, highlighting a potential risk of overfitting/bias toward instructions introduced in IFT
• ChatGPT demonstrates strong reasoning capability without the guidance of CoT prompting for arithmetic reasoning tasks, suggesting that some arithmetic datasets may be included in the pre-training mix
• The study sheds light on the importance of understanding LLMs' behavior in reasoning tasks and highlights potential risks associated with IFT and pretraining dataset leakage

Exploring the Effects of Chain-of-Thought Prompting on Large Language Models

Large language models (LLMs) have become increasingly popular in recent years due to their ability to generate complex multi-step reasoning. One technique used to elicit this type of reasoning is known as Chain-of-Thought (CoT) prompting, which involves adding instructions such as "Let's think step-by-step" to each input query. This approach has been shown to be effective for certain LLMs such as GPT-3, where it improved accuracy from 17.7% to 78.7% on the MultiArith dataset. However, it remains unclear whether CoT is still effective on more recent instruction finetuned (IFT) LLMs such as ChatGPT.

Analyzing the Effectiveness of CoT Prompting with ChatGPT

Surprisingly, when tested with ChatGPT, CoT was no longer effective for certain tasks like arithmetic reasoning while still being effective on other types of reasoning tasks. In fact, ChatGPT usually achieved its best performance without any prompting at all and could even generate rationales autonomously without being instructed to do so – suggesting that it had already been trained on these tasks with CoT and thus memorized the instruction.

These observations indicate that some arithmetic datasets may have been included in the pre-training mix for ChatGPT – leading not only to memorization but also potential data leakage issues if one were able to verify which datasets were used in training a particular model or detect data leakage issues within an IFT process.

New Baseline Results & Novel Insights into LLM Behavior

The experiments conducted by this study report new baseline results of ChatGPT on various reasoning tasks and provide novel insights into LLM profiling, instruction memorization, and pretraining dataset leakage risks associated with IFT processes. It was found that while CoT prompting is still required for certain types of reasoning tasks when using ChatGPT, strong reasoning capability can be demonstrated without any guidance at all when dealing with arithmetic problems – indicating that these datasets may have been included in the pre-training mix for this model and leading one to suspect possible data leakage issues within an IFT process or even just general overfitting/bias toward instructions introduced during training sessions.

Conclusion & Future Research Directions

Overall, this study sheds light on the importance of understanding LLMs' behavior in different types of reasoning tasks and highlights potential risks associated with IFT processes and pretraining dataset leakage issues that need addressing in future research directions – such as how exactly does a pre-training recipe affect a model's performance during inference stages? How can we effectively identify whether certain datasets were employed for pre-training? And how can we detect data leakage issues? Answering these questions will help us better understand how large language models work and ensure they are used responsibly moving forward!