4 articles tagged with #query-expansion. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers introduce STORM, a self-supervised framework that optimizes lexical query expansion for information retrieval by using BM25 reward signals during generation. The approach enables smaller language models (0.6B-8B parameters) to match larger proprietary rewriters while maintaining BM25's speed efficiency, and demonstrates zero-shot transfer across 18 languages.
Researchers introduce CoHyDE, an iterative co-training method that jointly optimizes a dense encoder and LLM rewriter to improve tool retrieval for AI agents. The approach outperforms single-component baselines by 2.5-8 percentage points on standard and vague queries, addressing the fundamental challenge of bridging colloquial user language with technical API vocabularies.
Researchers developed a framework to make large language model-based query expansion more efficient by distilling knowledge from powerful teacher models into compact student models. The approach uses retrieval feedback and preference alignment to maintain 97% of the original performance while dramatically reducing inference costs.
Researchers developed an automated query expansion framework using multiple large language models that constructs domain-specific examples without manual intervention. The system uses a two-LLM ensemble approach where different models generate expansions that are then refined by a third LLM, showing significant improvements over traditional methods across multiple datasets.
