Despite efforts to expand the knowledge of large language models (LLMs), knowledge gaps -- missing or outdated information in LLMs -- might always persist given the evolving nature of knowledge.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：尽管努力扩展大语言模型（LLM）的知识，但由于知识的不断演变，大语言模型中的知识空白（即缺失或过时的信息）可能始终存在。在本工作中，我们研究了识别大语言模型知识空白的方法，并在存在知识空白时放弃回答问题。我们首先将现有方法应用于模型校准或通过微调/提示的适应，分析其在生成低置信度输出时放弃回答的能力。基于它们在自我反思中的失败以及对保留集合的过度依赖，我们提出了两种基于模型协作的新方法，即LLM相互探测知识空白——通过合作性或竞争性方式。在覆盖不同知识领域的四个问答任务上，使用三个LLM进行的大量实验表明，合作性和竞争性揭示LLM知识空白的方法在放弃准确率上相比最强基线可实现高达19.3%的提升。进一步分析显示，我们提出的机制有助于识别检索增强中的失败案例，并定位多跳推理中的知识空白。