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This paper introduces comprehensive taxonomies for analyzing failures in retrieval-based factuality evaluation in medical AI, revealing fundamental limitations of the retrieve-then-verify paradigm that persist despite model scaling and fine-tuning.
This paper introduces two taxonomies—decomposing retrieval-stage errors into five quality dimensions and verifier-reasoning errors into six steps—induced automatically via LLM-as-Judge to diagnose where retrieval-based medical factuality evaluation fails. Stress-tests across 4 retrieval methods and 6 frontier verifiers show these failure modes are fundamental limitations of the retrieve-then-verify paradigm, not artifacts of outdated systems.
The paper proposes EvoTree, a framework for automatically generating scientific evolution trees from citation graphs, achieving state-of-the-art performance on a new annotated benchmark across AI subfields.
Proposes a term-centric framework for inducing hierarchical taxonomies from heterogeneous text sources, enabling cross-source alignment and interpretable hierarchies. Experiments on a multi-source benchmark demonstrate improved coherence and quality over text- and summary-based baselines.