Despite efforts to expand the knowledge of large language models (LLMs), knowledge gaps -- missing or outdated information in LLMs -- might always persist given the evolving nature of knowledge. In this work, we study approaches to identify LLM knowledge gaps and abstain from answering questions when knowledge gaps are present. We first adapt existing approaches to model calibration or adaptation through fine-tuning/prompting and analyze their ability to abstain from generating low-confidence outputs. Motivated by their failures in self-reflection and over-reliance on held-out sets, we propose two novel approaches that are based on model collaboration, i.e., LLMs probing other LLMs for knowledge gaps, either cooperatively or competitively. Extensive experiments with three LLMs on four QA tasks featuring diverse knowledge domains demonstrate that both cooperative and competitive approaches to unveiling LLM knowledge gaps achieve up to 19.3% improvements on abstain accuracy against the strongest baseline. Further analysis reveals that our proposed mechanisms could help identify failure cases in retrieval augmentation and pinpoint knowledge gaps in multi-hop reasoning.

翻译：尽管人们不断努力扩展大语言模型（LLM）的知识体系，但鉴于知识本身的动态演化特性，知识空白——即LLM中缺失或过时的信息——可能始终存在。本研究致力于探索识别LLM知识空白的方法，并在存在知识空白时主动放弃回答问题。我们首先通过微调/提示策略对现有模型校准或自适应方法进行适应性改造，并分析其放弃生成低置信度输出的能力。鉴于这些方法在自我反思方面的不足以及对预留数据集的过度依赖，我们提出了两种基于模型协作的新方法：即通过合作或竞争的方式，让LLM相互探查对方的知识空白。在涵盖多知识领域的四个问答任务上，对三种LLM进行的广泛实验表明，无论是合作式还是竞争式的知识空白揭示方法，在弃答准确率上均比最强基线提升了最高达19.3%。进一步分析表明，我们提出的机制有助于识别检索增强中的失败案例，并精确定位多跳推理中的知识空白。