# ToolDial: Multi-turn Dialogue Generation Method for Tool-Augmented Language Models

arXiv: https://arxiv.org/abs/2503.00564 · HTML: https://mlsanity.com/paper/2503.00564/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Placed in Tool learning and function calling, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 19 references that are in the map.
- First posted on arXiv 1 Mar 2025.

## Placed in

- LLM agents and tool use / [Tool learning and function calling](https://mlsanity.com/agents/tool-learning-and-function-calling/)

## Builds on (most cited first, 11 of 11)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Gorilla: Large Language Model Connected with Massive APIs](https://mlsanity.com/paper/2305.15334/) (2023)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://mlsanity.com/paper/2304.08244/) (2023)
- [Towards Scalable Multi-domain Conversational Agents: The Schema-Guided Dialogue Dataset](https://mlsanity.com/paper/1909.05855/) (2019)
- [ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases](https://mlsanity.com/paper/2306.05301/) (2023)
- [StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models](https://mlsanity.com/paper/2403.07714/) (2024)
- [ToolNet: Connecting Large Language Models with Massive Tools via Tool Graph](https://mlsanity.com/paper/2403.00839/) (2024)
- [ToolTalk: Evaluating Tool-Usage in a Conversational Setting](https://mlsanity.com/paper/2311.10775/) (2023)
- ["Do you follow me?": A Survey of Recent Approaches in Dialogue State Tracking](https://mlsanity.com/paper/2207.14627/) (2022)
- [Interpreting User Requests in the Context of Natural Language Standing Instructions](https://mlsanity.com/paper/2311.09796/) (2023)

## Built on by (most cited first, 30 of 34)

- [APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay](https://mlsanity.com/paper/2504.03601/) (2025)
- [Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey](https://mlsanity.com/paper/2503.22458/) (2025)
- [Goal Alignment in LLM-Based User Simulators for Conversational AI](https://mlsanity.com/paper/2507.20152/) (2025)
- [Non-Collaborative User Simulators for Tool Agents](https://mlsanity.com/paper/2509.23124/) (2025)
- [AskToAct: Enhancing LLMs Tool Use via Self-Correcting Clarification](https://mlsanity.com/paper/2503.01940/) (2025)
- [Towards Reliable Benchmarking: A Contamination Free, Controllable Evaluation Framework for Multi-step LLM Function Calling](https://mlsanity.com/paper/2509.26553/) (2025)
- [ToolHaystack: Stress-Testing Tool-Augmented Language Models in Realistic Long-Term Interactions](https://mlsanity.com/paper/2505.23662/) (2025)
- [AURA: Agent for Understanding, Reasoning, and Automated Tool Use in Voice-Driven Tasks](https://mlsanity.com/paper/2506.23049/) (2025)
- [Your Language Model is Its Own Critic: Reinforcement Learning with Value Estimation from Actor's Internal States](https://mlsanity.com/paper/2605.07579/) (2026)
- [ECG-Agent: On-Device Tool-Calling Agent for ECG Multi-Turn Dialogue](https://mlsanity.com/paper/2601.20323/) (2026)
- [Simulating Complex Multi-Turn Tool Calling Interactions in Stateless Execution Environments](https://mlsanity.com/paper/2601.19914/) (2026)
- [CXReasonAgent: Evidence-Grounded Diagnostic Reasoning Agent for Chest X-rays](https://mlsanity.com/paper/2602.23276/) (2026)
- [TurnWise: The Gap between Single- and Multi-turn Language Model Capabilities](https://mlsanity.com/paper/2603.16759/) (2026)
- [UniToolCall: Unifying Tool-Use Representation, Data, and Evaluation for LLM Agents](https://mlsanity.com/paper/2604.11557/) (2026)
- [What Makes Good Agentic Data? An ACE Lens on Data Generation for LLM Agents](https://mlsanity.com/paper/2608.27260/) (2026)
- [Quantitative Certification of Agentic Tool Selection](https://mlsanity.com/paper/2510.03992/) (2025)
- [ARTIS: Agentic Risk-Aware Test-Time Scaling via Iterative Simulation](https://mlsanity.com/paper/2602.01709/) (2026)
- [MUSE: Multi-Domain Chinese User Simulation via Self-Evolving Profiles and Rubric-Guided Alignment](https://mlsanity.com/paper/2604.13828/) (2026)
- [Latent Preference Modeling for Multi-Session Personalized Tool Calling](https://mlsanity.com/paper/2604.17886/) (2026)
- [Building Resource-Constrained Language Agents: A Korean Case Study on Chemical Toxicity Information](https://mlsanity.com/paper/2503.17753/) (2025)
- [In-N-Out: A Parameter-Level API Graph Dataset for Tool Agents](https://mlsanity.com/paper/2509.01560/) (2025)
- [GOAT: A Training Framework for Goal-Oriented Agent with Tools](https://mlsanity.com/paper/2510.12218/) (2025)
- [AWARE-US: Preference-Aware Infeasibility Resolution in Tool-Calling Agents](https://mlsanity.com/paper/2601.02643/) (2026)
- [ContiguousKV: Accelerating LLM Prefill with Granularity-Aligned KV Cache Management](https://mlsanity.com/paper/2601.13631/) (2026)
- [INSURE-Dial: A Phase-Aware Conversational Dataset & Benchmark for Compliance Verification and Phase Detection](https://mlsanity.com/paper/2602.18448/) (2026)
- [ToolWeave: Structured Synthesis of Complex Multi-Turn Tool-Calling Dialogues](https://mlsanity.com/paper/2605.12521/) (2026)
- [Multi-turn Conversational AI from Text to Multimodal Interaction: Data, Models, Evaluation, and Open Challenges](https://mlsanity.com/paper/2608.17605/) (2026)
- [From State to Action: OODA-Tool for Reliable Multi-Turn Tool Use](https://mlsanity.com/paper/2608.24368/) (2026)
- [SAP: State-Guided Data Synthesis with Argument Provenance for Multi-Turn Tool Use](https://mlsanity.com/paper/2609.06124/) (2026)
- [Reflective Personalization Optimization: A Post-hoc Rewriting Framework for Black-Box Large Language Models](https://mlsanity.com/paper/2511.05286/) (2025)

## Abstract (arXiv)

Tool-Augmented Language Models (TALMs) leverage external APIs to answer user queries across various domains. However, existing benchmark datasets for TALM research often feature simplistic dialogues that do not reflect real-world scenarios, such as the need for models to ask clarifying questions or proactively call additional APIs when essential information is missing. To address these limitations, we construct and release ToolDial, a dataset comprising 11,111 multi-turn dialogues, with an average of 8.95 turns per dialogue, based on APIs from RapidAPI. ToolDial has two key characteristics. First, the dialogues incorporate 16 user and system actions (e.g., "Request", "Clarify", "Fail inform") to capture the rich dynamics of real-world interactions. Second, we simulate dialogues where the system requests necessary information from the user based on API documentation and seeks additional APIs if the user fails to provide the required information. To facilitate this process, we introduce a method for generating an API graph that represents input and output compatibility between APIs. Using ToolDial, we evaluate a suite of language models on their ability to predict correct actions and extract input parameter values for API calls from the dialogue history. Modern language models achieve accuracy scores below 70%, indicating substantial room for improvement. We release our dataset and code at https://github.com/holi-lab/ToolDial.