Can Large Language Models Explain Themselves?

Large language models (LLMs) need accurate self-explanations to ensure AI safety.

Summary:

The academic article explores the interpretability-faithfulness of self-explanations provided by large language models (LLMs) and evaluates the effectiveness of redacted explanations in natural language processing tasks. It introduces self-consistency checks as a measure of faithfulness and discusses the task-dependent nature of faithfulness in LLMs. The article also presents a series of sessions involving multi-choice classification tasks and consistency checks to assess the interpretability-faithfulness of explanations. The findings indicate that LLMs do not generally provide faithful explanations, and redacted explanations may not accurately capture the sentiment of the text.

Major Findings:

1. The faithfulness of self-explanations provided by LLMs is task-dependent.
2. LLMs do not generally provide faithful explanations, raising concerns about their reliability.
3. Redacted explanations may not accurately capture the sentiment of the text, indicating potential limitations in their use for sentiment analysis.

Analysis and Critique:

The article provides valuable insights into the challenges of evaluating the faithfulness of self-explanations and the limitations of LLMs and redacted explanations. However, it also highlights the need for further research to address these limitations and improve the faithfulness of self-explanations. The study’s findings have implications for the trustworthiness and reliability of LLMs and the development of effective explanations in natural language processing tasks.

Appendix

Model	gpt-3.5-turbo-1106
Date Generated	2024-02-26
Abstract	https://arxiv.org/abs/2401.07927v1
HTML	https://browse.arxiv.org/html/2401.07927v1
Truncated	True
Word Count	42693