Summary of Llm with Tools: a Survey, by Zhuocheng Shen
LLM With Tools: A Survey
by Zhuocheng Shen
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper presents a novel approach to enhancing large language models (LLMs) by integrating external tools to improve efficiency and accuracy in handling complex tasks. The authors introduce a standardized paradigm for tool integration, which involves mapping user instructions to actionable plans and their execution. The study highlights the importance of understanding user intent, selecting suitable tools, and dynamically adjusting plans. Challenges such as tool invocation timing, selection accuracy, and robust reasoning processes are addressed through techniques like fine-tuning and in-context learning. Innovative approaches are proposed to ensure diversity, augment datasets, and improve generalization. Furthermore, the paper investigates enabling LLMs to autonomously create tools, which may redefine their role from mere users to creators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores a new way to make language models better by combining them with external tools. The goal is to help these models do specific tasks more efficiently and accurately. To achieve this, the authors developed a framework that lets users tell the model what to do and how to do it. This involves understanding what the user wants, choosing the right tool, and adjusting plans as needed. The study found some challenges, like timing and accuracy issues with tool selection. To overcome these, they used techniques like fine-tuning and learning in context. They also proposed new ways to increase diversity and improve how well the model generalizes. |
Keywords
» Artificial intelligence » Fine tuning » Generalization
