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ToolSense uncovers a surprising 50-64% drop in retrieval performance for LLMs when faced with realistic, ambiguous queries, challenging assumptions about their tool understanding.
LLMs can retrieve tools far more effectively when their query rewriting and the retrieval encoder are trained together, iteratively, than when trained separately.
