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The TREC Million LLM Track operationalizes a retrieval-based paradigm in which an assistant agent infers expertise dynamically by examining models'observable behavior, providing the first large-scale benchmark for expertise retrieval in agentic AI.
A novel evaluation framework called Image2Text2Image is proposed, which leverages diffusion models, such as Stable Diffusion or DALL-E, for text-to-image generation, and does not rely on human-annotated reference cap-tions, making it a valuable tool for assessing image captioning models.
This work extends the existing Exquisitor URF system with conversational search, and the user interface has been completely redesigned to prepare for future integration with conversational search and better support novice users.
This work investigates whether Learned Sparse Retrieval (LSR) can mitigate this backward compatibility issue, and explores lightweight query adaptation methods including ranking fusion, representation fusion, and minimal-training adapters to further improve compatibility.
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