Papers by Zhou Ziheng
Simple Role Assignment is Extraordinarily Effective for Safety Alignment (2026.findings-acl)
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Zhou Ziheng, Jiakun Ding, Zhaowei Zhang, Ruosen Gao, Ying Nian Wu, Demetri Terzopoulos, Yipeng Kang, Fangwei Zhong, Junqi Wang
| Challenge: | a new study proposes a role-conditioned pipeline for value alignment . principles alone are incomplete, and they provide little guidance on when and how a value applies in context. |
| Approach: | They propose a role-conditioned pipeline with role-based critics and a model-free approach that is based on role conditioning. |
| Outcome: | The proposed approach outperforms principle-based, Chain-of-Thought and other benchmarks. |
IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions (2023.emnlp-main)
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| Challenge: | Prior work on IE comprehension has focused on detecting idiomaticity, but this fails to account for IEs' non-compositionality. |
| Approach: | They construct a commonsense knowledge graph for figurative interpretations of IEs that can be used to convert PTLMs into knowledge models that encode and infer commonsensical knowledge related to IE use. |
| Outcome: | The proposed model can generalize to IEs unseen during training. |
DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check (2025.acl-long)
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Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang
| Challenge: | Chinese spelling check (CSC) tasks require that incorrect characters are usually similar to the correct ones in either phonetics or glyph. |
| Approach: | They propose a plug-and-play decoding intervention with similarity of characters module for Chinese spelling check (CSC) they propose to incorporate phonetic and glyph similarities only during the inference phase. |
| Outcome: | The proposed method significantly improves Chinese spelling check models on benchmarks and on benchmark datasets. |
Non-compositional Expression Generation Based on Curriculum Learning and Continual Learning (2023.findings-emnlp)
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| Challenge: | Non-compositional expressions are a classic ‘pain in the neck’ for NLP systems because of their non-composibility and limited data resources. |
| Approach: | They propose a dynamic curriculum learning framework which learns training examples from easy ones to harder ones but suffers from the forgetting problem. |
| Outcome: | The proposed framework improves on idiomatic expression generation and metaphor generation. |
CLASP: Cross-modal Alignment Using Pre-trained Unimodal Models (2024.findings-acl)
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| Challenge: | Recent advances in speech-text pretraining rely on parallel speech- text data . however, data accessibility is a challenge due to the limited data available. |
| Approach: | They propose a framework for jointly performing speech and text processing without parallel corpora during pre-training but only downstream. |
| Outcome: | The proposed framework extracts distinct representations for speech and text, aligning them effectively in a newly defined space using a multi-level contrastive learning mechanism. |
How do Role Models Shape Collective Morality? Exemplar-Driven Moral Learning in Multi-Agent Simulation (2026.acl-long)
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| Challenge: | Existing studies show that role models influence morality, but they are not uniformly interpreted and appropriated in groups with heterogeneous motivations. |
| Approach: | They build a multi-agent simulation where agents with diverse intrinsic drives interact and adapt through a four-stage cognitive loop. |
| Outcome: | The proposed model can significantly reshape morality of agents with diverse intrinsic drives . the simulations show that identity-driven conformity can substantially reshaped initial dispositions . |
Why Are We Moral? An LLM-based Agent Simulation Approach to the Study of Moral Evolution (2026.acl-long)
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Zhou Ziheng, Huacong Tang, Mingjie Bi, Wanying He, Fang Sun, Yizhou Sun, Ying Nian Wu, Demetri Terzopoulos, Yipeng Kang, Fangwei Zhong
| Challenge: | Existing models of moral evolution must abstract away cognitive processes . et al. (2017): evolution of morality presents a puzzle: natural selection favors selfish . |
| Approach: | They propose an LLM-based agent simulation framework that manipulates cognitive factors to understand moral evolution. |
| Outcome: | The proposed model exploits cognitive realism to explore moral evolution in a hunter-gatherer society. |
CLCL: Non-compositional Expression Detection with Contrastive Learning and Curriculum Learning (2023.acl-long)
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| Challenge: | Non-compositional expressions are a substantial challenge for natural language processing systems, necessitating more intricate processing compared to general language tasks. |
| Approach: | They propose a dynamic curriculum learning framework specifically designed to take advantage of scarce available training data for modeling non-compositionality. |
| Outcome: | The proposed framework improves on idiom usage recognition and metaphor detection tasks. |
Mixture of Small and Large Models for Chinese Spelling Check (2025.acl-long)
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| Challenge: | Chinese Spelling Check (CSC) tasks have been developed to correct spelling errors in given sentences . fine-tuned BERT-based models show excellent performance but suffer from edit pattern overfitting . a novel mixture approach that effectively combines small models and LLMs during beam search decoding phase improves accuracy and fluency of LLM. |
| Approach: | They propose a dynamic mixture approach that effectively combines small models and LLMs during beam search decoding phase. |
| Outcome: | The proposed method significantly boosts error correction capabilities, achieving state-of-the-art results across multiple datasets. |