Error-Aware Knowledge Distillation via Targeted Revision for Customer-Service Summarization
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866915820803719168 |
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| author | Lee, Hee-Jin Guo, Zhen Jin, Luchao Goudarzi, Morteza Moazami |
| author_facet | Lee, Hee-Jin Guo, Zhen Jin, Luchao Goudarzi, Morteza Moazami |
| contents | We introduce an Analyze-Revise-Finetune (ARF) pipeline that enables smaller open-source language models (LLMs) to surpass substantially larger proprietary models in customer service summarization tasks. The pipeline first analyzes and categorizes common errors in summaries produced by a teacher model (GPT-3.5), then performs a targeted revision using a compact editor model (Llama 3.1 70B) to generate high-quality, refined training data. Fine-tuning smaller student models (e.g., Llama 3.1 8B, QWen3 4B) on this refined data resulted in superior summarization performance compared to GPT-3.5. The ARF pipeline improves cost efficiency and data privacy while maintaining competitive accuracy, illustrating a generalizable framework for enhancing open-source LLMs across diverse downstream applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_03005 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Error-Aware Knowledge Distillation via Targeted Revision for Customer-Service Summarization Lee, Hee-Jin Guo, Zhen Jin, Luchao Goudarzi, Morteza Moazami Computation and Language We introduce an Analyze-Revise-Finetune (ARF) pipeline that enables smaller open-source language models (LLMs) to surpass substantially larger proprietary models in customer service summarization tasks. The pipeline first analyzes and categorizes common errors in summaries produced by a teacher model (GPT-3.5), then performs a targeted revision using a compact editor model (Llama 3.1 70B) to generate high-quality, refined training data. Fine-tuning smaller student models (e.g., Llama 3.1 8B, QWen3 4B) on this refined data resulted in superior summarization performance compared to GPT-3.5. The ARF pipeline improves cost efficiency and data privacy while maintaining competitive accuracy, illustrating a generalizable framework for enhancing open-source LLMs across diverse downstream applications. |
| title | Error-Aware Knowledge Distillation via Targeted Revision for Customer-Service Summarization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2511.03005 |