Papers by Lixin Zou
CHIFRAUD: A Long-term Web Text Dataset for Chinese Fraud Detection (2025.coling-main)
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| Challenge: | Detecting fraudulent online text is essential as they exploit human greed and deceive individuals. |
| Approach: | They propose to build a long-term dataset of Chinese fraudulent texts collected over 12 months. |
| Outcome: | The proposed dataset includes 59,106 entries extracted from billions of web pages and includes large language model-based detectors and pre-trained language model approaches. |
CharacterCraft: Bridging the Literature-Reality Dialogue Gap for Practical Role-Playing Agents (2025.findings-emnlp)
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| Challenge: | Existing dialogue datasets have a bias between query distributions and real-world user language usage. |
| Approach: | They propose a framework for Chinese role-playing and a robust evaluation method . they propose specialized Chinese dialogue extraction model and specialized memory retrieval module . |
| Outcome: | The proposed framework extracts character dialogue from novels and ensures high data quality. |
Weak-to-Strong Honesty Alignment via Learning-to-Rank Supervision (2025.findings-acl)
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| Challenge: | Existing approaches to enhance honesty with prompt engineering and fine-tuning are limited by annotated data. |
| Approach: | They propose a framework that enhances honesty through weak-to-strong generalization by training weak LLMs under weak supervision to improve their honesty. |
| Outcome: | The proposed framework improves honesty in large models even with limited label data. |
Learning to Translate by Translating: Stabilizing the Dual Loop via Semantic-Aware Self-Evolution (2026.findings-acl)
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Kui Liu, Mingming Yin, Zuoli Tang, Zihao Li, Chilin Fu, Xiaolu Zhang, Jun Zhou, Lixin Zou, Chenliang Li
| Challenge: | Large Language Models (LLMs) have been successful in machine translation, but lack of high-quality parallel corpora and cost constrain scalability. |
| Approach: | They propose an LLM-driven dual-learning framework that enables autonomous translation . they employ a robust semantic-aware reward function that balances adequacy with reconstruction fidelity . |
| Outcome: | The proposed model outperforms larger models on benchmarks and achieves parity with state-of-the-art supervised baselines on mainstream benchmarks. |
AIGuard: A Benchmark and Lightweight Detection for E-commerce AIGC Risks (2025.findings-acl)
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Wenhua Zhang, Weicheng Li, Xuanrong Rao, Lixin Zou, Xiangyang Luo, Chubin Zhuang, Yongjie Hong, Zhen Qin, Hengyu Chang, Chenliang Li, Bo Zheng
| Challenge: | Existing detection methods lack real-world scenarios and corresponding risk datasets . current MLLMs lack knowledge and have limited capability to detect the risk of AIGC content. |
| Approach: | They propose a benchmark for AIGC risk detection in real-world e-commerce . it includes 253,420 image-text pairs across four critical categories . |
| Outcome: | The proposed method achieves 9.68% higher recall than leading multimodal models while using only 25% of training resources. |
META-LORA: Memory-Efficient Sample Reweighting for Fine-Tuning Large Language Models (2025.coling-main)
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| Challenge: | Supervised fine-tuning (SFT) is widely adopted for tailoring large language models (LLMs) to specific downstream tasks. |
| Approach: | They propose a memory-efficient method for automatic sample reweighting that learns to re-weight fine-tuning samples by minimizing the loss on a small, high-quality validation set. |
| Outcome: | Meta-LoRA learns to reweight fine-tuning samples by minimizing the loss on a small, high-quality validation set through an end-to-end bi-level optimization framework based on meta-learning. |
Mitigating Language Confusion through Inference-time Intervention (2025.coling-main)
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| Challenge: | Existing methods to address the problem of language confusion are incontext learning and supervised fine-tuning (SFT) however, they consume context window space and require extensive data collection. |
| Approach: | They propose a language-sensitive intervention that detects and assesses language confusion without additional complex mechanisms. |
| Outcome: | The proposed method detects language confusion and assesses content quality without additional complex mechanisms. |
Efficient Sparse Attention needs Adaptive Token Release (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide array of text-centric tasks, however, their ‘large’ scale introduces significant computational and storage challenges, particularly in managing the key-value states of the transformer, which limits their wider applicability. |
| Approach: | They propose to release resources from caches and rebuild key-value states by a lightweight controller module to approximate an ideal top-K sparse attention. |
| Outcome: | The proposed method achieves a significant throughput improvement of 221.8% over full attention and a model with 7 billion tokens. |
Token-level Preference Self-Alignment Optimization for Multi-style Outline Controllable Generation (2025.findings-acl)
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| Challenge: | Existing attempts to outline generation are limited by response pair requirements and substantial computation costs. |
| Approach: | They propose a token-level preference self-alignment optimization for outline controllable generation that extends the Bradley-Terry model from pair-wise to list-wise comparison. |
| Outcome: | The proposed method outperforms existing methods by 19.28% in performance while requiring only 56.25% training time. |