A review of faithfulness metrics for hallucination assessment in Large Language Models

Abstract

This review examines the means with which faithfulness has been evaluated across open-ended summarization, question answering and machine translation tasks. We find that the use of Large Language Models (LLMs) as a faithfulness evaluator is commonly the metric that is most highly correlated with human judgement. The means with which other studies have mitigated hallucinations is discussed, with both retrieval augmented generation (RAG) and prompting framework approaches having been linked with superior faithfulness, whilst other recommendations for mitigation are provided. Research into faithfulness is integral to the continued widespread use of LLMs, as unfaithful responses can pose major risks to many areas whereby LLMs would otherwise be suitable. Furthermore, evaluating open-ended generation provides a more comprehensive measure of LLM performance than commonly used multiplechoice benchmarking, which can help in advancing the trust that can be placed within LLMs.