Evaluating Long-Context Question & Answer Systems
eugeneyan.com 研究 深度 Impact: 7/10
A comprehensive guide to evaluating long-context Q&A systems covering metrics, dataset construction, and benchmark reviews across narrative and technical domains.
Key Points
- Long contexts amplify four challenges: information overload, positional bias, multi-hop reasoning, and hallucination
- Evaluation must extend beyond exact match to include faithfulness and information density
- Multiple benchmarks cover narratives, technical documents, and multi-document scenarios
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