Parsing Fact From Fiction: Benchmarking LLM Accuracy With TruthfulQA

In this article, Brad Nikkel discusses TruthfulQA, a benchmark designed by Lin et al. in 2021 to evaluate the truthfulness of large language models (LLMs) when answering questions. The benchmark consists of 817 diverse questions spanning various categories such as health, law, finance, and politics. Unlike other LLM benchmarks like ARC, HellaSwag, and MMLU, TruthfulQA focuses on measuring the truthfulness of LLM outputs rather than their ability to reason or understand language. TruthfulQA assigns a truth score between 0 and 1 for each statement based on its probability of being true. The benchmark evaluates both the truthfulness and informativeness of LLM-generated answers, with human judges scoring each machine-generated answer. In addition to the main task, there is also a secondary task where LLMs are asked to pick answers from multiple-choice questions (some true and some false), which are then scored automatically. The results showed that while larger models like GPT-3-175B were more informative, they were less truthful compared to smaller models. This suggests that scaling alone may not be enough to address LLMs' truth deficits, and fine-tuning and prompt engineering might be necessary for creating more truthful models. TruthfulQA has contributed significantly to the field by highlighting the challenges of designing LLMs that generate relevant and true responses. It serves as a reminder that novel benchmarks will continue to emerge alongside advancements in LLM technology, driving improvements in language modeling.