Gotcha! Don't trick me with unanswerable questions! Self-aligning Large Language Models for Proactively Responding to Unknown Questions

ACL ARR 2024 April Submission586 Authors

Abstract: 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 just refusing to answer but further proactively providing explanations 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-Aligned method over existing baselines in terms of three types of task formulation.

Paper Type: Long

Research Area: Question Answering

Research Area Keywords: interpretability

Contribution Types: NLP engineering experiment

Languages Studied: English

Submission Number: 586