Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use
Recent advancements in large language models (LLMs) have led to significant improvements in agent performance; however, the effectiveness of these agents is increasingly hampered by the quality of tool interfaces they utilize. A paper published on arXiv, titled “Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use,” highlights the critical need for enhancing the clarity and utility of tool descriptions that agents rely on to perform tasks.
The paper emphasizes that while existing efforts to enhance LLM-based tool-using agents typically focus on refining the agents themselves—through larger models, improved prompting techniques, or fine-tuning—these enhancements often reach a performance plateau. This stagnation is largely attributable to the inherent ambiguity present in tool descriptions, which are primarily designed for human developers rather than for machine consumption. The challenge intensifies as the number of available tools continues to grow, making it even more crucial to develop effective and clear tool descriptions.
Challenges in Current Approaches
The authors identify two main challenges with existing methodologies aimed at improving tool interfaces:
- Multi-Stage Pipelines: Current methods typically necessitate re-running a complex multi-stage pipeline for each tool. This includes synthesizing queries, executing agents to gather trajectories, annotating these trajectories, and repeatedly prompting a strong LLM for every API added to the catalog.
- Independent Optimization: Most approaches optimize each tool in isolation, which restricts their scalability and limits the models’ ability to generalize to unseen tools.
Introducing Trace-Free+
To address these challenges, the authors propose a novel framework called Trace-Free+. This curriculum learning approach is designed to progressively shift supervision from environments rich in trace data to settings that are trace-free, enabling the model to internalize reusable patterns that define effective tool descriptions. The focus is on creating a more coherent and unambiguous set of tool interfaces that can be utilized by LLMs without the need for extensive retraining.
Dataset Construction
Supporting the Trace-Free+ framework is a newly constructed large-scale dataset that encompasses high-quality tool interfaces derived from real-world APIs. This dataset is created through a principled data synthesis workflow, ensuring that the tool descriptions are not only relevant but also robust. The authors’ rigorous methodology aims to ensure that the generated tool descriptions meet the standards required for effective use by LLMs.
Experimental Results
The paper presents compelling experimental results that validate the effectiveness of the Trace-Free+ approach. Key findings include:
- Robustness with Scaling: As the tool catalog expands to over 150 candidates, the Trace-Free+ framework demonstrates a reduction in accuracy degradation by 29.23%.
- Improved Success Rate: The average query-level success rate improved by 60.89% on the StableToolBench benchmark, showcasing the framework’s ability to enhance performance.
- Generalization Across Domains: The approach exhibits strong generalization capabilities without the necessity for retraining, highlighting its potential for wide applicability.
- Complementary Gains: The framework provides additional performance improvements on top of existing agent fine-tuning methods, indicating the effectiveness of integrating Trace-Free+ into current workflows.
In conclusion, the findings from this research underscore the importance of clear and effective tool descriptions in enhancing the performance of LLM-based agents. By introducing the Trace-Free+ framework, the authors contribute significantly to the ongoing efforts to improve tool interfaces, paving the way for more reliable and efficient LLM-agent interactions in the future.
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