Summary of Faithlm: Towards Faithful Explanations For Large Language Models, by Yu-neng Chuang et al.
FaithLM: Towards Faithful Explanations for Large Language Models
by Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, Ruixiang Tang, Shaochen Zhong, Fan Yang, Mengnan Du, Xuanting Cai, Xia Hu
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces FaithLM, a method for evaluating and improving the fidelity of natural language (NL) explanations generated by Large Language Models (LLMs). These LLMs are capable of addressing complex tasks through their internal knowledge and reasoning capabilities. However, the black-box nature of these models makes it challenging to explain their decision-making processes. FaithLM aims to address this issue by designing a method for evaluating the fidelity of NL explanations and iteratively improving them. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FaithLM is a new way to make Large Language Models (LLMs) give better reasons for their answers. These models are very good at doing complex tasks, but we don’t always know why they made a certain choice. FaithLM helps us figure out if the model’s explanation is accurate by showing it different situations and seeing how it responds. |
