Papers by Ziheng Wu
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. |
Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding (2024.findings-emnlp)
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Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
| Challenge: | Medical Information Extraction (MIE) tasks are a fundamental component of medical NLP. |
| Approach: | They propose an alternative adaptive constraint strategy to adjust the scale and scope of contrastive tokens. |
| Outcome: | The proposed approach selectively enhances the identification and classification capabilities while minimizing the influence of other inherent abilities in LLMs. |
Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed (2023.acl-industry)
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Bingyan Liu, Weifeng Lin, Zhongjie Duan, Chengyu Wang, Wu Ziheng, Zhang Zipeng, Kui Jia, Lianwen Jin, Cen Chen, Jun Huang
| Challenge: | Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs . but, current diffusion-based models lack entity knowledge and low inference speed . |
| Approach: | They propose a framework for training and deploying latent diffusion models with rich entity knowledge injected and optimized networks. |
| Outcome: | The proposed framework improves image quality and inference speed and can be used in industrial applications. |
Relation-aware Ensemble Learning for Knowledge Graph Embedding (2023.emnlp-main)
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| Challenge: | Existing methods to explore semantics of knowledge graphs have been proposed to explore these semantics in distinct ways. |
| Approach: | They propose to leverage existing methods in relation-aware manner to learn an ensemble by leveraging existing methods. |
| Outcome: | The proposed method has the same computation cost as general ensemble methods but with much better performance on benchmark datasets. |
Multi-modal Contrastive Representation Learning for Entity Alignment (2022.coling-1)
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| Challenge: | Existing studies focus on how to utilize information from different modalities, but it is not trivial to leverage multi-modal knowledge in entity alignment because of the modality heterogeneity. |
| Approach: | They propose a Multi-modal Contrastive Learning based Entity Alignment model which learns multiple individual representations from multiple modalities and performs contrastive learning to jointly model inter-modal and inter-modal interactions. |
| Outcome: | The proposed model outperforms state-of-the-art models on public datasets under both supervised and unsupervised conditions. |
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. |
Guiding Large Language Models for Biomedical Entity Linking via Restrictive and Contrastive Decoding (2025.findings-emnlp)
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| Challenge: | Existing attempts to apply large language models to BioEL have revealed difficulties . |
| Approach: | They propose a framework that enables large language models to adapt well to BioEL . they employ restrictive decoding to ensure the generation of valid entities . |
| Outcome: | Extensive experiments show that the framework outperforms existing LLMs. |
Decoder-Only LLMs can be Masked Auto-Encoders (2025.acl-short)
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Dan Qiao, Yuan Gao, Zheming Yang, Di Yang, Ziheng Wu, Pengcheng Lu, Minghui Qiu, Juntao Li, Min Zhang
| Challenge: | Modern NLP workflows require different models for generation and embedding tasks. |
| Approach: | They propose a method that transforms an LLM into a Uni-Directional Masked Auto-Encoder. |
| Outcome: | The proposed method achieves state-of-the-art under unsupervised conditions with merely 100 training steps. |
Biomedical Entity Linking as Multiple Choice Question Answering (2024.lrec-main)
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| Challenge: | Existing methods for biomedical entity linking are discriminative and disambiguative . Existing models for bioMEDical entity linking use a BERT-based encoder to encode mentions and entities into the same embedding space and dissociate mentions by nearest neighbors. |
| Approach: | They propose a model that treats biomedical entity linking as Multiple Choice Question Answering. |
| Outcome: | The proposed model outperforms state-of-the-art models on several datasets. |
A Survey on Foundation Language Models for Single-cell Biology (2025.acl-long)
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| Challenge: | Existing single-cell foundation language models are based on pre-trained and large language models. |
| Approach: | They review the development of single-cell foundation language models . they discuss data tokenization strategies and pre-training paradigms . |
| Outcome: | The proposed models have shown remarkable performance in a variety of single-cell data analysis tasks. |
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)
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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
| Challenge: | Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs). |
| Approach: | They propose a general framework to compensate for the deficiency of contextualized knowledge by querying large language models from various perspectives. |
| Outcome: | The proposed framework improves knowledge graph completion (KGC) by querying large language models from various perspectives. |
BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis (2023.emnlp-industry)
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| Challenge: | Recent text-to-image models require multiple passes of prompt engineering by humans to produce satisfactory results for real-world applications. |
| Approach: | They propose a deep generative model to generate high-quality prompts from raw descriptions using visual feedback. |
| Outcome: | The proposed model produces high-quality prompts from simple raw descriptions . it can be integrated to a cloud-native AI platform to provide better image generation service in the cloud. |