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University of Toronto
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LLM-powered query reformulation, a hot topic in IR, often fails to translate gains from lexical to neural retrieval, and bigger models don't always help.
Current LLM detection methods in peer review are fooled by hybrid human-AI workflows, mistaking AI-written text for AI-originated ideas.
Stop prompting LLMs to blindly rewrite queries – ReFormeR distills query transformations into reusable patterns that actually improve retrieval.
Denoising diffusion models can significantly outperform discriminative methods in learning-to-rank, suggesting a new path for improving information retrieval.
