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Evaluating Long-Context Question and Answer Systems
Evaluation metrics, how to build eval datasets, eval methodology, and a review of several benchmarks.
Staying grounded in the source text (faithful) Directly addressing the user’s question (relevant) Providing sufficient detail and context (comprehensive) Presenting information clearly and succinctly (concise) For example, “What did Gandalf do in the final battle at Hogwarts?” or “What is the penalty for trademark infringement in this residential lease agreement?” A faithful Q&A system should recognize that the required information isn’t present and respond accordingly instead of making up an answer. Additionally, the benchmark demonstrated that using LLMs as evaluators in pairwise comparisons provides superior alignment with human assessments compared to traditional metrics, highlighting clear distinctions in model strengths for closed-ended versus open-ended tasks.
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