RAG for Long-Form QA
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Explores retrieval-augmented LMs specifically on long-form question answering, where RAG failures are more subtle.
Retrieval is necessary: Confirms that retrieval is an important component for long-form QA, but that evidence documents must be carefully curated and ordered.
Attribution errors: Documents attribution errors - where the model cites passages that don't actually support its claims - and shows these spike when retrieved docs lack sufficient evidence.
Document ordering: Demonstrates that document order within the context substantially affects long-form QA attribution accuracy.
Practical guidelines: Offers concrete guidelines for document selection, ordering, and prompting to reduce hallucination in long-form RAG outputs.
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