Evaluating Long-Context Question & Answer Systems
Eugene Yan
Researchers outline methods for evaluating question-and-answer systems designed for long documents, identifying challenges like information overload and multi-hop reasoning that complicate performance assessment. Key evaluation approaches include measuring faithfulness (whether answers rely only on source material) and helpfulness (relevance, comprehensiveness, and conciseness), with benchmarks like NarrativeQA and QASPER providing reference standards. Effective evaluation requires diverse datasets with multiple question types, human annotations to establish ground truth, and assessment strategies that account for evidence location throughout documents to prevent systems from returning hallucinated or incomplete answers.
Why it matters
Evaluation metrics, how to build eval datasets, eval methodology, and a review of several benchmarks.
