Researchers have developed PORTS, a new method for training retrievers to better select external tools for large language models (LLMs). This approach uses a preference optimization technique that aligns the retriever with the LLM's needs by optimizing the correlation between tool selection probabilities and downstream performance. PORTS also enforces a contrastive semantic loss between documentation strings, improving the retriever's ability to find helpful tools. Experiments across multiple datasets, encoder models, and LLMs demonstrate PORTS's versatility and significant improvement in tool selection accuracy, with low computational demands allowing for generalization to new queries and tools. AI
IMPACT Enhances LLM capabilities by improving the selection of external tools, potentially leading to more efficient and accurate task completion.
RANK_REASON The cluster contains an academic paper detailing a new method for improving LLM tool selection. [lever_c_demoted from research: ic=1 ai=1.0]
- data set
- documentation strings
- encoder models
- large-language models
- PORTS
- Queries
- The Retrievers
- Tools
AI-generated summary · Google Gemini · from 2 sources. How we write summaries →
