Target-Aware Curation of Hallucination Stress Tests for Large Language Models
We develop a procedure that iteratively edits source-grounded seed prompts to expose hallucinations in a target model.
ProofPaper ↗
Key points
- Hallucination stress tests can yield similar average scores across language models even when different prompts elicit hallucinations in each model.
- Experiments cover 500 seeds from seven datasets and ten topics, with intrinsic and extrinsic hallucinations evaluated across open-weight and closed-source models.
- Among 219 prompts that pass model-assisted source-validity checks, 89 elicit responses containing unsupported claims.
- Curated prompts can be reused across models, but their diagnostic value depends on source validity and the failures they expose in each model.
Sources (1)
- [1]Target-Aware Curation of Hallucination Stress Tests for Large Language ModelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 08:20 PM
We develop a procedure that iteratively edits source-grounded seed prompts to expose hallucinations in a target model.
Hallucination stress tests can yield similar average scores across language models even when different prompts elicit hallucinations in each model.
Extractive summary: sentences quoted from the sources.
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