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Harbin Institute of Technology (Shenzhen);
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Achieving state-of-the-art reranking performance with a model as small as 0.27B parameters, KaLM-Reranker-V1 challenges the notion that bigger models are always better.
LLMs can now navigate the ever-expanding universe of external tools with significantly improved accuracy and generalization, thanks to a new agentic framework that proactively retrieves and grounds tool execution.
