ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs — interactive visual explainer | Rudrite Research

Researchers at ICLR 2024 introduced ToolLLM, a framework enabling large language models to use over 16,000 real-world APIs. The system includes ToolBench, a dataset built from RapidAPI without human labels, a depth-first search decision tree (DFSDT) for backtracking, an API retriever, and ToolEval for evaluation. This work advances LLMs' ability to interact with external tools and APIs autonomously.

ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs Teaching an open model to drive 16,464 real REST APIs: ToolBench built from RapidAPI with no human labels, a depth-first search that lets the model back out of dead ends DFSDT , an API retriever, and ToolEval to grade it all. Qin et al. · ICLR 2024 · Reasoning & RL. Read the paper ↗ https://arxiv.org/abs/2307.16789 A free, interactive, animated visual explainer of ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs — every exhibit computed from the real formulas, with verbatim quotes from the source. Questions - What is ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs? - Teaching an open model to drive 16,464 real REST APIs: ToolBench built from RapidAPI with no human labels, a depth-first search that lets the model back out of dead ends DFSDT , an API retriever, and ToolEval to grade it all. - Who published ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs, and where? - Qin et al. — ICLR 2024 arXiv:2307.16789 . - Where can I find a visual explainer of ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs? - Right here — a free, interactive, animated walkthrough of the whole paper, with exhibits computed from the real formulas and verbatim quotes from the source. Related explainers DeepSeek-R1 /deepseek-r1 Chain-of-Thought Prompting Elicits Reasoning in Large Language Models /chain-of-thought Training language models to follow instructions with human feedback /instructgpt Direct Preference Optimization: Your Language Model is Secretly a Reward Model /dpo DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models /deepseekmath Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters /test-time-compute Constitutional AI: Harmlessness from AI Feedback /constitutional-ai DAPO: An Open-Source LLM Reinforcement Learning System at Scale /dapo