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LLM-SLM Collaborative Framework of Idiomatic Expression Generation
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 22:02
摘要
Idiomatic Expression Generation, which aims to produce idiomatic text from plain text, is a valuable yet challenging NLP task. However, existing methods suffer from the scarcity of parallel data and dependence on high-quality manual annotations. To address this, we propose an iterative LLM-SLM (Large Language Model-Small Language Model) collaborative framework — Auto-IDEA, that replaces human supervision for idiomatic expression data generation. In this self-improving cycle, the LLM constructs parallel corpora (idiomatic and plain text) via bidirectional semantic reconstruction, automatically generating “Locate-Then-Polish” (LTP) annotations; the SLM filters low-quality corpora while continuously enhancing its verification ability through incremental learning. We instantiate Auto-IDEA for Chinese Idiom Polishing (CIP), constructing CIP-200K, a large-scale dataset of 206K parallel sentences with LTP annotations. The Qwen3-8B fine-tuned on CIP-200K achieves a 25.2% absolute Idiom Polishing Accuracy (IPA) improvement over a supervised fine-tuning (SFT) baseline, outperforming DeepSeek-R1 by 6.2%. Extensive experiments (e.g., Chinese idiom cloze tests and English idiom generation tasks) and human evaluations verify the generalization and effectiveness of Auto-IDEA, demonstrating a new pathway for high-quality, annotation-free data generation through LLM-SLM collaboration.
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记录 #1 · 2026-07 · 最新观测版本
LLM-SLM Collaborative Framework of Idiomatic Expression Generation
Idiomatic Expression Generation, which aims to produce idiomatic text from plain text, is a valuable yet challenging NLP task. However, existing methods suffer from the scarcity of parallel data and dependence on high-quality manual annotations. To address this, we propose an iterative LLM-SLM (Large Language Model-Small Language Model) collaborative framework — Auto-IDEA, that replaces human supervision for idiomatic expression data generation. In this self-improving cycle, the LLM constructs parallel corpora (idiomatic and plain text) via bidirectional semantic reconstruction, automatically generating “Locate-Then-Polish” (LTP) annotations; the SLM filters low-quality corpora while continuously enhancing its verification ability through incremental learning. We instantiate Auto-IDEA for Chinese Idiom Polishing (CIP), constructing CIP-200K, a large-scale dataset of 206K parallel sentences with LTP annotations. The Qwen3-8B fine-tuned on CIP-200K achieves a 25.2% absolute Idiom Polishing Accuracy (IPA) improvement over a supervised fine-tuning (SFT) baseline, outperforming DeepSeek-R1 by 6.2%. Extensive experiments (e.g., Chinese idiom cloze tests and English idiom generation tasks) and human evaluations verify the generalization and effectiveness of Auto-IDEA, demonstrating a new pathway for high-quality, annotation-free data generation through LLM-SLM collaboration.
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