Summary of Toolnet: Connecting Large Language Models with Massive Tools Via Tool Graph, by Xukun Liu et al.
ToolNet: Connecting Large Language Models with Massive Tools via Tool Graph
by Xukun Liu, Zhiyuan Peng, Xiaoyuan Yi, Xing Xie, Lirong Xiang, Yuchen Liu, Dongkuan Xu
First submitted to arxiv on: 29 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 A recent breakthrough in large language models (LLMs) has enabled them to tackle a wide range of tasks with remarkable success. However, despite their capabilities, LLMs remain severely limited when utilizing massive external tools. Current approaches to in-context learning simply list tool descriptions as plain text and input them into the LLM, which then generates step-by-step sequences of tool calls to solve problems. This paradigm ignores intrinsic tool dependencies and offloads all reasoning tasks to the LLM, restricting it to a limited set of specifically designed tools. As a result, LLMs struggle to operate on vast libraries of tools, hindering their performance in real-world scenarios. To address this limitation, researchers propose ToolNet, a plug-and-play framework that scales up tool usage to thousands with moderate token consumption increases. ToolNet organizes tools into a directed graph, where each node represents a tool and weighted edges denote transition between tools. By iteratively choosing the next tool from its successors, an LLM navigates the graph until task resolution. Extensive experiments demonstrate impressive results on challenging multi-hop tool learning datasets and resilience to tool failures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making large language models (LLMs) better at using lots of tools. Right now, they can do some things really well, but when it comes to using many different tools, they struggle. The current way of teaching LLMs new tools involves listing the tool’s description and then having the model figure out how to use each one in order. This method doesn’t account for how tools are connected and relies heavily on the model making good decisions. As a result, LLMs can only work with a limited number of tools and have trouble solving real-world problems. To solve this issue, researchers created ToolNet, a new way to teach LLMs about tools. It’s like a map that shows how different tools are connected and allows the model to navigate through them to find the right tool for each task. The results show that ToolNet can help LLMs do better on complex tasks and handle it when some tools don’t work. |
Keywords
» Artificial intelligence » Token
