Papers by Tianyang Zhang
MLLM-Protector: Ensuring MLLM’s Safety without Hurting Performance (2024.emnlp-main)
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Renjie Pi, Tianyang Han, Jianshu Zhang, Yueqi Xie, Rui Pan, Qing Lian, Hanze Dong, Jipeng Zhang, Tong Zhang
| Challenge: | MLLMs are deployed on limited image-text pairs, which makes them more vulnerable to catastrophic forgetting of their original abilities during safety fine-tuning. |
| Approach: | They propose a plug-and-play strategy that detects harmful visual inputs and transforms harmful ones into harmless ones. |
| Outcome: | The proposed approach mitigates the risks posed by malicious visual inputs without compromising the original performance of MLLMs. |
Towards Enhanced Immersion and Agency for LLM-based Interactive Drama (2025.acl-long)
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| Challenge: | Existing studies have focused on the role of immersion and agency in interactive drama. |
| Approach: | They propose a playwriting-guided generation method that helps LLMs craft dramatic stories with substantially improved structures and narrative quality. |
| Outcome: | The proposed method improves storytelling quality and immersion and agency, while allowing agents to refine their reactions to align with the player’s intentions. |
Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models (2026.acl-long)
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Yanbin Yin, Kun Zhou, Zhen Wang, Xiangdong Zhang, Yifei Shao, Shibo Hao, Yi Gu, Jieyuan Liu, Somanshu Singla, Tianyang Liu, Eric P. Xing, Zhengzhong Liu, Haojian Jin, Zhiting Hu
| Challenge: | closed-ended question-based benchmarks struggle with saturation as newer models emerge . crowd-sourced leaderboards rely on costly and slow human judges . |
| Approach: | They propose a framework that leverages collective intelligence from all large language models to evaluate each other. |
| Outcome: | a new framework enables a democratic, pairwise evaluation of all large language models . it achieves 97% correlation with human judgements, while significantly reducing the cost. |
The Instinctive Bias: Spurious Images lead to Illusion in MLLMs (2024.emnlp-main)
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| Challenge: | Existing multi-modal large language models (MLLMs) are able to process visual inputs by converting them into visual tokens that share the same latent space as language tokens in LLMs. |
| Approach: | They propose a benchmark that assesses the visual illusion level given spurious images and a pipeline that converts visual inputs into visual tokens. |
| Outcome: | The proposed benchmark shows that MLLMs suffer from an instinctive bias to varying degrees when presented with spurious images. |
Sensitivity-LoRA : Low-Load Sensitivity-Based Fine-Tuning for Large Language Models (2025.findings-emnlp)
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Hao Zhang, Bo Huang, Zhenjia Li, Xi Xiao, Hui Yi Leong, Zumeng Zhang, Xinwei Long, Tianyang Wang, Hao Xu
| Challenge: | Low-Rank Adaptation (LoRA) is a promising approach to adapting LLMs to specialized tasks . existing rank allocation techniques remain computationally inefficient and unstable . |
| Approach: | They propose a low-rank adapted model that approximates model weight updates using low-ranked decomposition. |
| Outcome: | The proposed method is limited by its uniform rank allocation to each incremental matrix . it leverages the second-order derivatives of the loss function to capture weight sensitivity . |
Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism (2025.findings-emnlp)
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Zhiwei Wang, Yunji Wang, Zhongwang Zhang, Zhangchen Zhou, Hui Jin, Tianyang Hu, Jiacheng Sun, Zhenguo Li, Yaoyu Zhang, Zhi-Qin John Xu
| Challenge: | Large language models struggle with complex reasoning tasks, such as mathematical problem-solving. |
| Approach: | They constructed a symbolic multi-step reasoning task to investigate the information propagation mechanisms in Transformer models when solving the task through direct answering and Chain-of-Thought (CoT) reasoning. |
| Outcome: | The proposed algorithm improves on 7 multi-step reasoning datasets, while introducing only 132 trainable parameters. |
HyperAdaLoRA: Accelerating LoRA Rank Allocation During Training via Hypernetworks without Sacrificing Performance (2026.findings-acl)
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Hao Zhang, Zhenjia Li, Yifan Gao, Xi Xiao, Heng Zhang, Shuyang Zhang, null Xiaoxincc, Bo Huang, Yuhang Wu, Tianyang Wang, Hao Xu
| Challenge: | Low-Rank Adaptation (LoRA) assumes a uniform rank r for each incremental matrix, not accounting for the varying significance of weight matrices across modules and layers. |
| Approach: | They propose a framework that allows for faster convergence of low-rank adaptive models . they use a hypernetwork to prune the outputs of the hypernetworks to generate parameters . |
| Outcome: | The proposed framework accelerates convergence of AdaLoRA by leveraging a hypernetwork. |
Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation (2026.acl-long)
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Xi Xiao, Chenrui Ma, Yunbei Zhang, Chen Liu, Zhuxuanzi Wang, Yanshu Li, Lin Zhao, Guosheng Hu, Tianyang Wang, Hao Xu
| Challenge: | Low-Rank Adaptation (LoRA) is a key parameter-efficient fine-tuning method . however, its effectiveness is hampered by semantic drift and structural incoherence . |
| Approach: | They propose a low-rank Adaptation framework that tackles semantic drift and structural incoherence by pruning task-irrelevant directions. |
| Outcome: | Experiments on large language models, vision models, and vision models show that the proposed framework outperforms LoRA and advanced dynamic rank allocation and sparsity-based methods. |
RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance (2024.emnlp-industry)
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| Challenge: | Large Language Models (LLMs) have improved the open-domain QA’s performance, but how to efficiently handle enterprise-exclusive corpora and build domain-specific QA systems are still not studied for industrial applications. |
| Approach: | They propose a general and comprehensive framework based on Retrieval Augmented Generation (RAG) and facilitate the whole business process of establishing QA systems for IT operations and maintenance. |
| Outcome: | The proposed framework achieves superior results on two kinds of QA tasks. |
Towards Generating Controllable and Solvable Geometry Problem by Leveraging Symbolic Deduction Engine (2025.acl-industry)
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Zhuoxuan Jiang, Tianyang Zhang, Peiyan Peng, Jing Chen, Yinong Xun, Haotian Zhang, Lichi Li, Yong Li, Shaohua Zhang
| Challenge: | Compared to math word problems, geometry problems emphasize multi-modal formats and the translation between informal and formal languages. |
| Approach: | They propose a symbolic deduction engine-based geometry problem generation framework that leverages a symbolic deduction engine to generate geometry problems. |
| Outcome: | The proposed method avoids inherent biases in translating natural language into formal language and guarantees to control the generated problems in terms of knowledge points and difficulties by an elaborate checking function. |
Verification-Aware Planning for Multi-Agent Systems (2026.eacl-long)
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| Challenge: | Large language model (LLM) agents are increasingly deployed to tackle complex tasks . multi-agent collaboration introduces new challenges in planning, coordination, and verification . |
| Approach: | They propose a framework for multi-agent collaboration with verification-aware planning . the framework decomposes tasks, models subtask dependencies, and encodes planner-defined passing criteria as subtask verification functions (VFs) |
| Outcome: | The proposed framework outperforms baselines on diverse datasets while improving system robustness and interpretability. |
Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs (2025.emnlp-main)
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Dayu Yang, Tianyang Liu, Daoan Zhang, Antoine Simoulin, Xiaoyi Liu, Yuwei Cao, Zhaopu Teng, Xin Qian, Grey Yang, Jiebo Luo, Julian McAuley
| Challenge: | Recent breakthrough models like OpenAI-o1 and DeepSeek-R1 show powerful task-solving capabilities, particularly advances in reasoning. |
| Approach: | They propose future research directions that may deepen the synergy, ultimately advancing LLM performance in both complex reasoning and code intelligence. |
| Outcome: | The proposed research may deepen the synergy, ultimately advancing LLM performance in both complex reasoning and code intelligence. |
SACTOR: LLM-Driven Correct and Idiomatic C to Rust Translation with Static Analysis and FFI-Based Verification (2026.acl-long)
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Tianyang Zhou, Ziyi Zhang, Haowen Lin, Somesh Jha, Mihai Christodorescu, Kirill Levchenko, Varun Chandrasekaran
| Challenge: | Large language models (LLMs) have shown promise in producing idiomatic translations, but offer no correctness guarantees. |
| Approach: | They propose a C-to-Rust translation tool that uses an initial "unidiomatic" translation followed by an "idiomatic refinement" they evaluate SACTOR on 200 programs from two datasets and two more complex scenarios . |
| Outcome: | The proposed tool delivers high end-to-end correctness and produces safe, idiomatic Rust with up to 7 fewer Clippy warnings. |
Mixture-of-Domain-Adapters: Decoupling and Injecting Domain Knowledge to Pre-trained Language Models’ Memories (2023.acl-long)
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| Challenge: | Pre-trained language models demonstrate excellent abilities to understand texts in the generic domain while struggling in a specific domain. |
| Approach: | They propose to decouple the feed-forward networks of the Transformer architecture into two parts to maintain old-domain knowledge and a mixture-of-adapters gate to inject domain-specific knowledge in parallel. |
| Outcome: | The proposed method achieves superior performance on in-domain, out-of-domain and knowledge-intensive tasks. |
How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence (2020.acl-main)
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| Challenge: | Legal Artificial Intelligence (LegalAI) focuses on applying artificial intelligence to help legal tasks. |
| Approach: | They introduce the history, current state, and future directions of research in LegalAI . they illustrate the tasks from the perspectives of legal professionals and NLP researchers . |
| Outcome: | The proposed system can reduce heavy and redundant work for legal professionals . it can also provide a reliable reference to those who are not familiar with the legal domain . |