elder-plinius/CL4R1T4S
CL4R1T4S is a project that provides transparency by collecting and sharing the hidden system prompts and guidelines used by major AI models and agents to promote trust and understanding of AI behavior.
Awesome AI Agents › LLM Research Repositories
quchangle1/LLM-Tool-Survey
This repository, titled 'LLM-Tool-Survey' by quchangle1, serves as a comprehensive survey and collection of research papers focused on the emerging paradigm of tool learning with large language models (LLMs). Tool learning with LLMs is a promising approach to enhance the capabilities of these models to solve complex problems by integrating external tools. The repository organizes a wide range of papers according to the survey paper "Tool Learning with Large Language Models: A Survey," which has been accepted by the journal Frontiers of Computer Science (FCS). The survey addresses two main questions: why tool learning is beneficial and how it is implemented. It reviews the benefits of tool integration, such as knowledge acquisition through search engines, databases, knowledge graphs, and specialized tools for tasks like weather or map information retrieval. It also covers expertise enhancement through mathematical tools and symbolic reasoning. The implementation aspect is systematically reviewed through a taxonomy of four key stages in the tool learning workflow: task planning, tool selection, tool calling, and response generation. The repository includes a detailed list of papers categorized by their focus areas, such as benefits of tools, task planning, benchmarks, evaluation methods, challenges, and future directions. It also provides links to Chinese introductions and encourages contributions from the community. The survey aims to provide a structured and accessible overview of the fragmented literature in this field, making it easier for newcomers and researchers to understand and explore tool learning with LLMs. Overall, this repository is a valuable resource for anyone interested in the intersection of large language models and tool integration, offering a curated and well-organized collection of academic work and insights into current challenges and future research directions.
https://github.com/quchangle1/LLM-Tool-Survey
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