Large language models (LLMs) have shown remarkable performance in various tasks but often fail to handle queries that exceed their knowledge and capabilities, leading to incorrect or fabricated responses. This paper addresses the need for LLMs to recognize and refuse infeasible tasks due to the required skills surpassing their capabilities. We first systematically conceptualize infeasible tasks for LLMs, providing formal definitions and categorizations that cover a spectrum of related hallucinations. We develop and benchmark a new dataset comprising diverse infeasible and feasible tasks to test multiple LLMs' abilities on task feasibility. Furthermore, we explore the potential of training enhancements to increase LLMs' refusal capabilities with fine-tuning. Experiments validate the effectiveness of our methods, offering promising directions for refining the operational boundaries of LLMs in real applications.

翻译：大型语言模型（LLMs）在多种任务中展现出卓越性能，但在处理超出其知识与能力范围的查询时往往表现不佳，导致错误或虚构的回应。本文针对LLMs需要识别并拒绝因所需技能超越其能力而不可行任务的需求展开研究。我们首先系统性地对LLMs不可行任务进行概念化，提供涵盖相关幻觉光谱的形式化定义与分类体系。我们开发并基准测试了一个包含多样化不可行与可行任务的新数据集，用以检验多个LLMs在任务可行性判断上的能力。此外，我们通过微调探索了训练增强技术提升LLMs拒绝能力的潜力。实验验证了我们方法的有效性，为优化LLMs在实际应用中的操作边界提供了可行方向。