Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912054652174336 |
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| author | Deng, Yang Zhao, Yong Li, Moxin Ng, See-Kiong Chua, Tat-Seng |
| author_facet | Deng, Yang Zhao, Yong Li, Moxin Ng, See-Kiong Chua, Tat-Seng |
| contents | Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have a definitive answer. To avoid providing hallucinated answers to these unknown questions, existing studies typically investigate approaches to refusing to answer these questions. In this work, we propose a novel and scalable self-alignment method to utilize the LLM itself to enhance its response-ability to different types of unknown questions, being capable of not only refusing to answer but also providing explanation to the unanswerability of unknown questions. Specifically, the Self-Align method first employ a two-stage class-aware self-augmentation approach to generate a large amount of unknown question-response data. Then we conduct disparity-driven self-curation to select qualified data for fine-tuning the LLM itself for aligning the responses to unknown questions as desired. Experimental results on two datasets across four types of unknown questions validate the superiority of the Self-Align method over existing baselines in terms of three types of task formulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15062 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations Deng, Yang Zhao, Yong Li, Moxin Ng, See-Kiong Chua, Tat-Seng Computation and Language Machine Learning Despite the remarkable abilities of Large Language Models (LLMs) to answer questions, they often display a considerable level of overconfidence even when the question does not have a definitive answer. To avoid providing hallucinated answers to these unknown questions, existing studies typically investigate approaches to refusing to answer these questions. In this work, we propose a novel and scalable self-alignment method to utilize the LLM itself to enhance its response-ability to different types of unknown questions, being capable of not only refusing to answer but also providing explanation to the unanswerability of unknown questions. Specifically, the Self-Align method first employ a two-stage class-aware self-augmentation approach to generate a large amount of unknown question-response data. Then we conduct disparity-driven self-curation to select qualified data for fine-tuning the LLM itself for aligning the responses to unknown questions as desired. Experimental results on two datasets across four types of unknown questions validate the superiority of the Self-Align method over existing baselines in terms of three types of task formulation. |
| title | Don't Just Say "I don't know"! Self-aligning Large Language Models for Responding to Unknown Questions with Explanations |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2402.15062 |