HypoTermQA: Hypothetical Terms Dataset for Benchmarking Hallucination Tendency of LLMs
This addresses the challenge of hallucinations for improving LLM reliability and alignment, with potential applications in domains like law and health, though it is incremental as it builds on existing detection methods.
The paper tackles the problem of hallucinations in Large Language Models by introducing an automated framework for benchmarking hallucination tendencies and detection, resulting in state-of-the-art models achieving 3% to 11% performance on a new dataset and evaluator agents showing a 6% error rate.
Hallucinations pose a significant challenge to the reliability and alignment of Large Language Models (LLMs), limiting their widespread acceptance beyond chatbot applications. Despite ongoing efforts, hallucinations remain a prevalent challenge in LLMs. The detection of hallucinations itself is also a formidable task, frequently requiring manual labeling or constrained evaluations. This paper introduces an automated scalable framework that combines benchmarking LLMs' hallucination tendencies with efficient hallucination detection. We leverage LLMs to generate challenging tasks related to hypothetical phenomena, subsequently employing them as agents for efficient hallucination detection. The framework is domain-agnostic, allowing the use of any language model for benchmark creation or evaluation in any domain. We introduce the publicly available HypoTermQA Benchmarking Dataset, on which state-of-the-art models' performance ranged between 3% and 11%, and evaluator agents demonstrated a 6% error rate in hallucination prediction. The proposed framework provides opportunities to test and improve LLMs. Additionally, it has the potential to generate benchmarking datasets tailored to specific domains, such as law, health, and finance.