Knowing What LLMs DO NOT Know: A Simple Yet Effective Self-Detection Method

arXiv:2310.17918v2 Announce Type: replace
Abstract: Large Language Models (LLMs) have shown great potential in Natural Language Processing (NLP) tasks. However, recent literature reveals that LLMs generate nonfactual responses intermittently, which impedes the LLMs' reliability for further utilization. In this paper, we propose a novel self-detection method to detect which questions that a LLM does not know that are prone to generate nonfactual results. Specifically, we first diversify the textual expressions for a given question and collect the corresponding answers. Then …

abstract arxiv cs.ai cs.cl detection generate however language language models language processing large language large language models literature llms natural natural language natural language processing nlp novel paper processing reliability responses simple tasks type