#query-rewriting News & Analysis

4 articles tagged with #query-rewriting. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

4 articles

AIBullisharXiv – CS AI · Jun 57/10

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Data Flow Control: Data Safety Policies for AI Agents

Researchers introduce Data Flow Control (DFC), a framework that enforces data safety policies within database management systems to prevent AI agents from executing semantically correct but policy-violating queries. The open-source solution, called Passant, achieves near-zero overhead across five major DBMS engines while outperforming alternatives by orders of magnitude, moving data governance from application prompts into infrastructure.

AINeutralarXiv – CS AI · Jun 46/10

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Need to Know: Contextual-Integrity-Grounded Query Rewriting for Privacy-Conscious LLM Delegation

Researchers introduce DelegateCI-Bench, a privacy-focused benchmark for query rewriting in LLM delegation, combined with a reinforcement learning framework that selectively redacts sensitive information while preserving task-critical content. The approach achieves superior privacy-utility tradeoffs compared to existing type-based PII redaction methods, addressing growing concerns about sensitive data exposure in cloud-hosted AI systems.

AINeutralarXiv – CS AI · Mar 176/10

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Not All Queries Need Rewriting: When Prompt-Only LLM Refinement Helps and Hurts Dense Retrieval

Research reveals that LLM query rewriting in RAG systems shows highly domain-dependent performance, degrading retrieval effectiveness by 9% in financial domains while improving it by 5.1% in scientific contexts. The study identifies that effectiveness depends on whether rewriting improves or worsens lexical alignment between queries and domain-specific terminology.

AINeutralarXiv – CS AI · Apr 105/10

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Multi-Faceted Self-Consistent Preference Alignment for Query Rewriting in Conversational Search

Researchers introduce MSPA-CQR, a machine learning approach that improves conversational query rewriting by aligning preferences across three dimensions: query rewriting, passage retrieval, and response generation. The method uses self-consistent preference data and direct preference optimization to generate more diverse and effective rewritten queries in conversational search systems.