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.

The rise of large language models (LLMs) has fundamentally reshaped the technological paradigm of rumor detection, offering transformative opportunities to construct adaptive detection systems while simultaneously ushering in new threats, such as “logically perfect rumors”. This paper aims to unify existing methods in the field of rumor detection and reveal the logical mechanisms behind them. From the perspective of complex systems, we innovatively propose a Cognition-Interaction-Behavior (CIB) tri-level framework for rumor detection based on collective intelligence and explore the synergistic relationship between LLMs and collective intelligence in rumor governance. We identify promising future research directions, including advancing agent-based modeling to capture complex rumor dynamics, addressing emerging challenges unique to the LLM era, and interdisciplinary perspectives. We hope this work lays a theoretical foundation for next-generation rumor detection paradigms and offers valuable insights for advancing the field.