Paper: Teach Large Language Models the Concept of Meta-cognition to Reduce Hallucination Text Generation (ICLR-2024)

Reviewer comment (Theory):
The paper proposes several hypotheses that may be ungrounded. For example, in the introduction part they claim that "RLHF is the main source of hallucination of recent LLMs", but MLE-trained models can also hallucinate and RLHF can help with reducing hallucinations [1]. There are also other kinds of similar claims in the introduction section such as "LLMs are not enough to fit the world’s model."

Author response:
Thank you for your thoughtful review and valuable suggestions. The preparation time for this paper was indeed relatively tight. I will carefully refine it in accordance with your advice.

Regarding the statement "RLHF is the main source of hallucination of recent LLMs," I want to clarify that I am referring to the RLHF performed by mainstream AI research institutes. In order to make Language Models (LLMs) useful, the reward model is more likely to favor a formally good answer rather than rejecting an answer. I think that may lead hallucinations.  These intuitive guesses indeed lack theoretical and experimental evidence. You rightly point out that if the reward model is factually augmented, the results could be substantially different. I appreciate you bringing this to my attention and providing such insightful information.

Reviewer comment (Writing):
There are unpaired quotation marks in the manuscript (e.g., Section 1: ”hallucination”).

Author response:
Thank you for carefully reading my work and providing valuable feedback. I acknowledge that the preparation time for this paper was relatively tight, leading to some formatting errors. Additionally, I recognize that the scale and types of experiments were somewhat limited, and the analysis of experimental data could be more robust. I sincerely apologize for these shortcomings.

In the upcoming revision, I will diligently rectify the formatting errors and strive to enhance both the scale and diversity of my experiments. I also aim to strengthen the analysis of experimental data. Your guidance is greatly appreciated, as it will contribute to the improvement and overall quality of the paper.

Once again, thank you for your review and valuable suggestions.

Reviewer comment (Presentation):
Table 5 and tables in the appendix extend beyond the page width.

Author response:
Thank you for carefully reading my work and providing valuable feedback. I acknowledge that the preparation time for this paper was relatively tight, leading to some formatting errors. Additionally, I recognize that the scale and types of experiments were somewhat limited, and the analysis of experimental data could be more robust. I sincerely apologize for these shortcomings.

In the upcoming revision, I will diligently rectify the formatting errors and strive to enhance both the scale and diversity of my experiments. I also aim to strengthen the analysis of experimental data. Your guidance is greatly appreciated, as it will contribute to the improvement and overall quality of the paper.

Once again, thank you for your review and valuable suggestions.

Reviewer comment (Experiments):
This article seriously lacks quantitative experimental results to verify the effectiveness of the method. The claim of the entire paper does not have sufficient experimental support.

Author response:
Thank you for reviewing and giving good advices. The preparation time for this paper was relatively tight, leading to a lack of experiments and theoretical discussions. I'll refine it later.

May all go well with you!

Reviewer comment (Theory):
There is also a lack of theoretical discussion further validating the method's effectiveness.

Author response:
Thank you for reviewing and giving good advices. The preparation time for this paper was relatively tight, leading to a lack of experiments and theoretical discussions. I'll refine it later.

May all go well with you!