Evaluating Long-Context Question and Answer Systems

Summary

While evaluating Q&A systems is straightforward with short paragraphs, complexity increases as documents grow larger. For example, technical documentation, novels and movies, as well as multi-document scenarios. Although some of these evaluation challenges also appear in shorter contexts, long-context evaluation amplifies issues such as: Information overload: Irrelevant details in large documents obscure relevant facts, making it harder for retrievers and models to locate the right evidence for the answer. Positional variance: Evidence may appear at the beginning, middle, or end of documents, making it a challenge for models with limited effective context or those susceptible to the “lost in the middle” problem. Multi-hop reasoning: The correct answer depends on synthesizing several distinct pieces of evidence scattered throughout the text(s), challenging the model’s ability to retain and integrate information that is far apart. Hallucinations at scale: Larger contexts increase the risk of models returning plausible yet incorrect responses due to poor retrieval or limited effective context. Open-ended questions: Queries on broad themes or interpretative topics rarely have a single definitive answer, especially for large documents or corpora. In this write-up, we’ll explore key evaluation metrics, how to build evaluation datasets, and methods to assess Q&A performance through human annotations and LLM-evaluators. We’ll also review several benchmarks across narrative stories, technical and academic texts, and very long-context, multi-document situations. Finally, we’ll wrap up with advice for evaluating long-context Q&A on our specific use cases. An overview of what we'll cover in this writeup By the way, if you want to learn more about evals, my friends Hamel and Shreya are hosting their final cohort of “AI Evals for Engineers and PMs” in July. Here’s a 35% discount code. Key Evaluation Metrics Evaluating Q&A systems goes beyond just checking for factual accuracy. Sp...