Think Less, Label Better: Multi-Stage Domain-Grounded Synthetic Data Generation for Fine-Tuning Large Language Models in Telecommunications

• URL: http://arxiv.org/abs/2509.25736v1
• Date: Tue, 30 Sep 2025 03:49:57 GMT
• Title: Think Less, Label Better: Multi-Stage Domain-Grounded Synthetic Data Generation for Fine-Tuning Large Language Models in Telecommunications
• Authors: Chenhua Shi, Gregor Macdonald, Bhavika Jalli, Wanlu Lei, John Zou, Mridul Jain, Joji Philip,
• Abstract summary: We present a retrieval-augmented pipeline for generating synthetic question-answer pairs grounded in structured domain knowledge.<n>Our framework integrates a retriever, base generator, and refinement model to synthesize and enhance QA pairs.<n>We demonstrate our approach in a real-world telecom scenario focused on radio access network (RAN) troubleshooting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The success of large language models (LLMs) depends heavily on large-scale, high-quality instruction-following and reinforcement datasets. However, generating such data through human annotation is prohibitively time-consuming particularly for domain-specific tasks like telecom network troubleshooting, where accurate responses require deep technical expertise and contextual understanding. In this paper, we present a fully automated, retrieval-augmented pipeline for generating synthetic question-answer (QA) pairs grounded in structured domain knowledge. Our multi-stage framework integrates a retriever, base generator, and refinement model to synthesize and enhance QA pairs using documents retrieved from a domain-specific knowledge graph. To ensure data quality, we employ customized RAGAS-based scoring to filter low-quality samples, producing a high-quality dataset suitable for reinforcement fine-tuning (RFT). We demonstrate our approach in a real-world telecom scenario focused on radio access network (RAN) troubleshooting. The resulting pipeline generates complex, context-rich troubleshooting solution plans without human intervention. This work offers a scalable solution for building instruction and reinforcement datasets in specialized domains, significantly reducing dependence on manual labeling while maintaining high technical fidelity.

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