Summary of Don’t Just Say “i Don’t Know”! Self-aligning Large Language Models For Responding to Unknown Questions with Explanations, by Yang Deng et al.
Don’t Just Say “I don’t know”! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations
by Yang Deng, Yong Zhao, Moxin Li, See-Kiong Ng, Tat-Seng Chua
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper addresses a critical issue with Large Language Models (LLMs): their tendency to provide overconfident and sometimes inaccurate answers, even when the question has no definitive answer. To mitigate this problem, researchers typically focus on developing methods for LLMs to refuse answering unknown questions. However, this work takes a novel approach by proposing a self-alignment method that leverages the LLM itself to enhance its response-ability to different types of unknown questions. This includes not only refusing to answer but also providing explanations for unanswerable questions. The Self-Align method involves two stages: class-aware self-augmentation and disparity-driven self-curation, which enables fine-tuning the LLM for desired responses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are very good at answering questions, but sometimes they can be too confident even when there is no clear answer. To fix this problem, researchers usually try to teach the model not to answer these kinds of questions. But what if we could make the model itself better at figuring out which questions it doesn’t know? That’s what this paper proposes: a new way for LLMs to understand when they don’t have an answer and explain why. |
Keywords
* Artificial intelligence * Alignment * Fine tuning
