# APIDA-Chat: Structured Synthesis of API Search Dialogues to Bootstrap Conversational Agents

arXiv: https://arxiv.org/abs/2510.03743 · HTML: https://mlsanity.com/paper/2510.03743/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 10 of its 12 references that are in the map.
- First posted on arXiv 4 Oct 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)
- Synthetic data and self-improvement for LLMs / [Code and instruction data synthesis](https://mlsanity.com/synth/code-and-instruction-data-synthesis/)

## Builds on (most cited first, 10 of 10)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Synthetic Data Generation Using Large Language Models: Advances in Text and Code](https://mlsanity.com/paper/2503.14023/) (2025)
- [Fine Tuning vs. Retrieval Augmented Generation for Less Popular Knowledge](https://mlsanity.com/paper/2403.01432/) (2024)
- [Using AI-Based Coding Assistants in Practice: State of Affairs, Perceptions, and Ways Forward](https://mlsanity.com/paper/2406.07765/) (2024)
- [Ada-Instruct: Adapting Instruction Generators for Complex Reasoning](https://mlsanity.com/paper/2310.04484/) (2023)
- [Wizard of Shopping: Target-Oriented E-commerce Dialogue Generation with Decision Tree Branching](https://mlsanity.com/paper/2502.00969/) (2025)
- [CoPrUS: Consistency Preserving Utterance Synthesis towards more realistic benchmark dialogues](https://mlsanity.com/paper/2412.07515/) (2024)
- [Single- vs. Dual-Prompt Dialogue Generation with LLMs for Job Interviews in Human Resources](https://mlsanity.com/paper/2502.18650/) (2025)
- [Dialogue Management for Interactive API Search](https://mlsanity.com/paper/2107.12317/) (2021)

## Abstract (arXiv)

Large-language-model assistants are suitable for explaining popular APIs, yet they falter on niche or proprietary libraries because the multi-turn dialogue data needed for fine-tuning are scarce. We present APIDA-Chat, an open-source pipeline that converts symbolic dialogue-act "scripts" into realistic, domain-grounded API Search conversations using a lightweight model for inexpensive training data generation. Phase I pairs a legacy dialogue planner with a high-capability teacher LLM (o4-mini) to synthesize a "gold set" of realized dialogues; then, a smaller Llama 3.2 3B student model is fine-tuned on this corpus. Phase II drops the teacher and reuses the same planner with the fine-tuned model, allowing rapid, low-cost synthesis of new dialogues without exposing source code to external services. The fine-tuned student improves BLEU from 0.38 to 0.50 and BERTScore from 0.88 to 0.91 versus the base model while running entirely on a single consumer GPU. All components are modular and publicly released to serve as a conservative baseline for future work. APIDA-Chat is open-sourced at https://github.com/Zeberhart/apida-chat and a video demo is available at https://youtu.be/YqmZBHyGbPs .