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If you were building a Q&A feature (or chatbot) based on very long documents (like books), what evals would you focus on?

1. Two metrics that come to mind • Faithfulness: Grounding of answers in document's content. Not to be confused with correctness—an answer can be correct (based on updated information) but not faithful to the document. Sub-metric: Precision of citations

• Helpfulness: Usefulness (directly addresses the question with enough detail and explanation) and completeness (does not omit important details); an answer can be faithful but not helpful if too brief or doesn't answer the question

• Evaluate separately: Faithfulness = binary label -> LLM-evaluator; Helpfulness = pairwise comparisons -> reward model

2. How to build robust evals • Use LLMs to generate questions from the text • Evals should evaluate positional robustness (i.e., have questions at the beginning, middle, and end of text)

3. Potential challenges • Open-ended questions may have no single correct answer, making reference-based evals trickly. For example: What is the theme of this novel? • Questions should be representative of prod traffic, with a mix of factual, inferential, summarization, definitional questions

4. Benchmark Datasets: • NarrativeQA: Questions based on entire movie scripts or novels. Includes reference answers useful for LLM-eval comparisons • NovelQA: Q&A over full novels; includes both MCQ and free-form responses, and includes references

• Qasper: Similar to NarrativeQA, but with academic documents that are 5-10k tokens, and includes evaluation of answer spans • LongBench: Average of 6.7k words across fiction and technical docs • LongBench v2: Extension of LongBench, but evals are MCQ only

• MultiDoc2Dial: Modeling dialogues grounded in multiple documents. Evaluates ability to integrate info over multiple docs • Frustratingly Hard Evidence Retrieval for QA Over Books: Reframed NarrativeQA as open-domain task where book text must be retrieved