Summary of Efficient and Scalable Estimation Of Tool Representations in Vector Space, by Suhong Moon et al.
Efficient and Scalable Estimation of Tool Representations in Vector Space
by Suhong Moon, Siddharth Jha, Lutfi Eren Erdogan, Sehoon Kim, Woosang Lim, Kurt Keutzer, Amir Gholami
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Recent advancements in function calling and tool use have significantly enhanced the capabilities of large language models (LLMs), enabling them to interact with external information sources and execute complex tasks. However, managing prompt length and maintaining accuracy when a large number of tools are available remains a challenge. To address this, we propose a novel framework for generating synthetic data for tool retrieval applications and an efficient data-driven tool retrieval strategy using small encoder models. We also introduce ToolBank, a new dataset reflecting real human user usages. Furthermore, we present three novel approaches: Tool2Vec, ToolRefiner, and MLC. Experimental results show improvements of up to 27.28 in Recall@K on the ToolBench dataset and 30.5 in Recall@K on ToolBank. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recent advancements have made large language models better at interacting with external information sources and doing complex tasks. However, this has also created a problem: when there are many tools available, it’s hard to keep track of them all. We propose new ways to make tool retrieval more efficient using synthetic data and small encoder models. We also introduce a new dataset called ToolBank that reflects real human user behavior. Our methods include three new approaches: Tool2Vec, ToolRefiner, and MLC. These methods can improve tool retrieval accuracy by up to 27.28 on one dataset and 30.5 on another. |
Keywords
» Artificial intelligence » Encoder » Prompt » Recall » Synthetic data
