APIDA-Chat: Structured Synthesis of API Search Dialogues to Bootstrap Conversational Agents
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| Format: | Preprint |
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2025
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| _version_ | 1866915533551566848 |
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| author | Eberhart, Zachary McMillan, Collin |
| author_facet | Eberhart, Zachary McMillan, Collin |
| contents | 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 . |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2510_03743 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | APIDA-Chat: Structured Synthesis of API Search Dialogues to Bootstrap Conversational Agents Eberhart, Zachary McMillan, Collin Software Engineering 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 . |
| title | APIDA-Chat: Structured Synthesis of API Search Dialogues to Bootstrap Conversational Agents |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2510.03743 |