Thought templates derived from training traces and refined via natural-language feedback improve multi-hop reasoning performance in long-context LMs across benchmarks and can be distilled into smaller models.
InProceedings of The Third Text REtrieval Conference, TREC 1994, Gaithers- burg, Maryland, USA, November 2-4, 1994, volume 500-225 ofNIST Special Publication, pages 109–
2 Pith papers cite this work. Polarity classification is still indexing.
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Semantic and hybrid document retrieval methods provide reliable, efficient selection for query-focused text analyses like LDA and BERTopic, outperforming random or keyword-only approaches.
citing papers explorer
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When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs
Thought templates derived from training traces and refined via natural-language feedback improve multi-hop reasoning performance in long-context LMs across benchmarks and can be distilled into smaller models.
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The Effect of Document Selection on Query-focused Text Analysis
Semantic and hybrid document retrieval methods provide reliable, efficient selection for query-focused text analyses like LDA and BERTopic, outperforming random or keyword-only approaches.