HKVM-RAG uses key-value-separated hypergraphs to organize LLM evidence tuples into answer-path hyperedges, yielding F1 gains over KG-PPR on two multi-hop QA benchmarks and further gains when combined with dense retrievers.
ColBERTv2: Effective and efficient retrieval via lightweight late inter- action,
3 Pith papers cite this work. Polarity classification is still indexing.
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A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.
Strict generation directly from Task-Method-Knowledge models yields 96.5% grounded and 92.6% usable QA pairs across 23 topics, outperforming transcript-first and TMK-aware alternatives on representational grounding.
citing papers explorer
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The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation
A literature review concludes that pursuing consensus in data annotation creates biased AI by dismissing subjective disagreements and enforcing geographic hegemony, and proposes mapping diversity instead.
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Constructing Evaluation Datasets for Procedural Reasoning: Balancing Naturalness, Grounding, and Multi-Hop Coverage
Strict generation directly from Task-Method-Knowledge models yields 96.5% grounded and 92.6% usable QA pairs across 23 topics, outperforming transcript-first and TMK-aware alternatives on representational grounding.