# ToolBridge: An Open-Source Dataset to Equip LLMs with External Tool Capabilities

arXiv: https://arxiv.org/abs/2410.10872 · HTML: https://mlsanity.com/paper/2410.10872/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 31 references that are in the map.
- First posted on arXiv 8 Oct 2024.

## Builds on (most cited first, 23 of 23)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs](https://mlsanity.com/paper/2307.16789/) (2023)
- [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [PAL: Program-aided Language Models](https://mlsanity.com/paper/2211.10435/) (2022)
- [ToolQA: A Dataset for LLM Question Answering with External Tools](https://mlsanity.com/paper/2306.13304/) (2023)
- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [Internet-Augmented Dialogue Generation](https://mlsanity.com/paper/2107.07566/) (2021)
- [MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use](https://mlsanity.com/paper/2310.03128/) (2023)
- [GSM-Plus: A Comprehensive Benchmark for Evaluating the Robustness of LLMs as Mathematical Problem Solvers](https://mlsanity.com/paper/2402.19255/) (2024)
- [A Dataset for Answering Time-Sensitive Questions](https://mlsanity.com/paper/2108.06314/) (2021)
- [On the Tool Manipulation Capability of Open-source Large Language Models](https://mlsanity.com/paper/2305.16504/) (2023)
- [MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark](https://mlsanity.com/paper/2405.12209/) (2024)
- [PEER: A Collaborative Language Model](https://mlsanity.com/paper/2208.11663/) (2022)
- [Let Me Do It For You: Towards LLM Empowered Recommendation via Tool Learning](https://mlsanity.com/paper/2405.15114/) (2024)

## Built on by (most cited first, 5 of 5)

- [Simulating Environments with Reasoning Models for Agent Training](https://mlsanity.com/paper/2511.01824/) (2025)
- [Terminal-World: Scaling Terminal-Agent Environments via Agent Skills](https://mlsanity.com/paper/2605.20876/) (2026)
- [ToolHaystack: Stress-Testing Tool-Augmented Language Models in Realistic Long-Term Interactions](https://mlsanity.com/paper/2505.23662/) (2025)
- [What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents](https://mlsanity.com/paper/2608.27260/) (2026)
- [VehicleWorld: A Highly Integrated Multi-Device Environment for Intelligent Vehicle Interaction](https://mlsanity.com/paper/2509.06736/) (2025)

## Abstract (arXiv)

Through the integration of external tools, large language models (LLMs) such as GPT-4o and Llama 3.1 significantly expand their functional capabilities, evolving from elementary conversational agents to general-purpose assistants. We argue that the primary drivers of these advancements are the quality and diversity of the training data. However, the existing LLMs with external tool integration provide only limited transparency regarding their datasets and data collection methods, which has led to the initiation of this research. Specifically, in this paper, our objective is to elucidate the detailed process involved in constructing datasets that empower LLMs to effectively learn how to utilize external tools and make this information available to the public through the introduction of ToolBridge. ToolBridge proposes to employ a collection of general open-access datasets as its raw dataset pool and applies a series of strategies to identify appropriate data entries from the pool for external tool API insertions. By supervised fine-tuning on these curated data entries, LLMs can invoke external tools in appropriate contexts to boost their predictive accuracy, particularly for basic functions including data processing, numerical computation, and factual retrieval. Our experiments rigorously isolates model architectures and training configurations, focusing exclusively on the role of data. The experimental results indicate that LLMs trained on ToolBridge demonstrate consistent performance improvements on both standard benchmarks and custom evaluation datasets. All the associated code and data will be open-source at https://github.com/CharlesPikachu/ToolBridge, promoting transparency and facilitating the broader community to explore approaches for equipping LLMs with external tools capabilities.