ToolWeave: Structured Synthesis of Complex Multi-Turn Tool-Calling Dialogues
- URL: http://arxiv.org/abs/2605.12521v1
- Date: Fri, 03 Apr 2026 17:02:20 GMT
- Title: ToolWeave: Structured Synthesis of Complex Multi-Turn Tool-Calling Dialogues
- Abstract summary: Multi-turn tool calling is essential for LLMs to function as autonomous agents.<n>Existing synthetic data generation pipelines produce unrealistic dialogues.<n>We introduce ToolWeave, a structured framework for synthesizing realistic multi-turn tool-calling.
- Score: 17.032111974825185
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Multi-turn tool calling is essential for LLMs to function as autonomous agents, yet synthesizing the training data required for these capabilities remains a fundamental challenge. Existing synthetic data generation pipelines often produce unrealistic dialogues for two reasons: they chain tools that are only superficially compatible rather than aligned with meaningful user tasks, and they generate dialogues in one shot, which often introduces arguments that were neither provided by the user nor produced by prior tool calls. These issues also lead to a severe underrepresentation of multi-step tool interactions. We introduce ToolWeave, a structured framework for synthesizing realistic multi-turn tool-calling dialogues. ToolWeave support realistic multi-step workflows (or tool sequences) by constructing tools with built-in dependencies and filters the workflows based on alignment with user goals. It reduces parameter hallucination by using a fine-grained planning stage that explicitly tracks parameter provenance. As a result, ToolWeave-generated synthetic dialogues contain more multi-step tool interactions (45%) and fewer hallucinations in parameters and tool names. Consequently, LLMs fine-tuned on ToolWeave consistently outperform those fine-tuned on prior datasets across three public benchmarks. Notably, Llama-3.1-70B fine-tuned on ToolWeave achieves 39.75% on BFCL-V3 multi-turn, compared to 23.50% when fine-tuned on SOTA ToolFlow data.
Related papers
- ParaTool: Shifting Tool Representations from Context to Parameters [21.55999060277199]
ParaTool is a framework that projects each tool into a dedicated, loadable set of parameters.<n>Our approach consists of three stages: (1) parametric tool pre-training encapsulates the knowledge of different tools into independent parameter modules; (2) soft tool selection employs a gating network to dynamically weigh and aggregate relevant tool parameters; and (3) parametric tool fine-tuning jointly updates tool parameters to align the training and inference processes.
arXiv Detail & Related papers (2026-05-28T08:14:07Z) - FinToolSyn: A forward synthesis Framework for Financial Tool-Use Dialogue Data with Dynamic Tool Retrieval [59.67674731525128]
FinToolSyn is a forward synthesis framework designed to generate high-quality financial dialogues.<n>Our pipeline constructs a repository of 43,066 tools and synthesizes over 148k dialogue instances.<n>Extensive experiments demonstrate that models trained on FinToolSyn achieve a 21.06% improvement.
arXiv Detail & Related papers (2026-03-25T08:00:26Z) - Unlocking Implicit Experience: Synthesizing Tool-Use Trajectories from Text [48.25052564552558]
We introduce GEM, a data synthesis pipeline that enables the generation and extraction of multi-turn tool-use trajectories from text corpora.<n>To reduce the computational cost, we further train a specialized Trajectory Synthesizer via supervised fine-tuning.<n>Experiments demonstrate that our GEM-32B achieve a 16.5% improvement on the BFCL V3 Multi-turn benchmark.
arXiv Detail & Related papers (2026-01-15T12:58:46Z) - ToolForge: A Data Synthesis Pipeline for Multi-Hop Search without Real-World APIs [40.70833390513187]
We introduce ToolForge, an automated synthesis framework that achieves strong real-world tool-calling performance.<n>ToolForge synthesizes large-scale tool-learning data specifically designed for multi-hop search scenarios.<n> Empirical results show that a model with only 8B parameters, when trained on our synthesized data, outperforms GPT-4o on multiple benchmarks.
arXiv Detail & Related papers (2025-12-18T04:06:26Z) - Rethinking Stateful Tool Use in Multi-Turn Dialogues: Benchmarks and Challenges [30.68589269821412]
Existing benchmarks that assess Language Models (LMs) as Language Agents (LAs) for tool use primarily focus on single-turn interactions.<n>We propose textttDialogTool, a multi-turn dialogue dataset with stateful tool interactions considering the whole life cycle of tool use.
arXiv Detail & Related papers (2025-05-19T16:36:13Z) - Advancing and Benchmarking Personalized Tool Invocation for LLMs [66.39214525683425]
We introduce the concept of Personalized Tool Invocation and define two key tasks: Tool Preference and Profile-dependent Query.<n>To tackle these challenges, we propose PTool, a data synthesis framework designed for personalized tool invocation.<n>We construct textbfPTBench, the first benchmark for evaluating personalized tool invocation.
arXiv Detail & Related papers (2025-05-07T02:25:20Z) - ToolFlow: Boosting LLM Tool-Calling Through Natural and Coherent Dialogue Synthesis [80.34000499166648]
We propose a Graph-based Sampling strategy to sample more relevant tool combinations, and a Planned-generation strategy to create plans that guide the synthesis of coherent dialogues.<n>We apply SFT on LLaMA-3.1-8B using 8,000 synthetic dialogues generated with ToolFlow.<n>Results show that the model achieves tool-calling performance comparable to or even surpassing GPT-4, while maintaining strong general capabilities.
arXiv Detail & Related papers (2024-10-24T05:45:04Z) - Towards Completeness-Oriented Tool Retrieval for Large Language Models [60.733557487886635]
Real-world systems often incorporate a wide array of tools, making it impractical to input all tools into Large Language Models.
Existing tool retrieval methods primarily focus on semantic matching between user queries and tool descriptions.
We propose a novel modelagnostic COllaborative Learning-based Tool Retrieval approach, COLT, which captures not only the semantic similarities between user queries and tool descriptions but also takes into account the collaborative information of tools.
arXiv Detail & Related papers (2024-05-25T06:41:23Z) - ControlLLM: Augment Language Models with Tools by Searching on Graphs [97.62758830255002]
We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving real-world tasks.
Our framework comprises three key components: (1) a textittask decomposer that breaks down a complex task into clear subtasks with well-defined inputs and outputs; (2) a textitThoughts-on-Graph (ToG) paradigm that searches the optimal solution path on a pre-built tool graph; and (3) an textitexecution engine with a rich toolbox that interprets the solution path and runs the
arXiv Detail & Related papers (2023-10-26T21:57:21Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.