Title: ClashEval: Quantifying the tug-of-war between an LLM√¢‚Ç¨‚Ñ¢s internal prior and external evidence
Benchmark: ClashEval
Summary: Retrieval-augmented generation (RAG) aims to reduce hallucinations and update knowledge in large language models (LLMs). A study with over 1,200 questions across six domains examines how LLMs handle correctly and incorrectly retrieved content. Findings show LLMs often adopt wrong retrieved information, especially if they lack confidence in their initial response, but are less likely to accept highly unrealistic content, presenting a significant challenge and benchmark for improving LLM accuracy when faced with conflicting information.
Phenomenon type: General Capability (A broadly useful ability, which could be relevant to multiple applications)
Target phenomenon: Conflict of internal knowledge and external evidence
Phenomenon defined by authors: Yes
Phenomenon definition: Conflict between internal pre-training knowledge and context, and conflict resolution ability of LLMs
Definition scope: Comprehensive
Definition integrity: Single cohesive phenomenon
Definition integrity detail: No
Phenomenon contested: Widely-agreed
Task definition: RAG question-answering across 6 different domains
Task definition detail: Question, domain, answer, score
Task source: Crowd-sourced task examples (e.g. Prolific-created tasks), Modified from another benchmark (e.g. translation into another language)
Task ecology: Representative task (e.g. answering medical licensing exam questions)
Dataset size: 1278
Dataset size detail: 3 or 10 perturbations per question
Dataset metadata available: Yes
Dataset metadata detail: domain
Sampling method: Targeted items (creators defined a task space and chose tasks within it strategically)
Split reviewed: Test
Response format: Short free response (e.g. single word or number)
Metric definition: Exact Match (accuracy, F1, precision, recall)
Metric aggregation: Simple Mean
Metric statistics: simple mean/sum
Metric subscores: Yes
Metric subscores detail: domains
Metric access: Outputs alone
Few-shot evaluation: No
Authorship: Academia
Benchmark availability: Yes
Task face validity: Yes
Metric face validity: Yes
Results interpreted by authors: Yes
Results compared: No
Results comparison explanation: No comparisons made
Results realism: The benchmark is itself realistic
Human baseline: No
Authors discuss validity: No
Taxonomy: Knowledge / Conflicts
New taxonomy: General Application / Knowledge Conflicts