Papers by Xingzhou Chen
The “Knowledge–Behavior Gap” in Cultural Taboo Safety of Large Language Models (2026.acl-long)
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Ying He, Sihang Jiang, Xingzhou Chen, Zhouhong Gu, Yiwei Gu, Minggui HE, Shimin Tao, null Mahongxia, Yanghua Xiao
| Challenge: | Existing cultural benchmarks assess cultural knowledge or values biases, but ignore cultural taboos. |
| Approach: | They propose a benchmark to evaluate and improve the cultural taboo safety of large language models. |
| Outcome: | The proposed benchmark spans 77 countries and regions, and includes over 2,020 taboos. |
GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization (2025.acl-long)
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Zhouhong Gu, Xingzhou Chen, Xiaoran Shi, Tao Wang, Suhang Zheng, Tianyu Li, Hongwei Feng, Yanghua Xiao
| Challenge: | Existing methods for achieving this require a limited understanding of constraints and can be hallucinating or brittle. |
| Approach: | They propose a framework that combines adversarial training dynamics with an encoder-only reward model to progressively learn and adapt to increasingly complex constraints. |
| Outcome: | Extensive experiments show that GAPO significantly outperforms existing methods like PPO, DPO, and KTO in fine-grained constraints. |
From Coarse to Fine: Benchmarking and Reward Modeling for Writing-Centric Generation Tasks (2026.findings-acl)
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| Challenge: | Existing evaluation benchmarks for writing reward models are coarse-grained. |
| Approach: | They propose a benchmark and a fine-grained training framework to evaluate writing reward models. |
| Outcome: | The proposed model improves on various writing benchmarks and exhibits strong generalization. |
StrucText-Eval: Evaluating Large Language Model’s Reasoning Ability in Structure-Rich Text (2025.acl-long)
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| Challenge: | Structured data has been central to corporate data strategies for decades . however, with the advancement of large language models (LLMs), there has been a significant shift towards the effective utilization of unstructured data. |
| Approach: | They propose an automatic evaluation data generation method to assess LLMs’ reasoning capabilities on structure-rich text. |
| Outcome: | The proposed method supports 8 structured languages and 29 tasks, generating data with adjustable complexity through controllable nesting and structural width. |