Papers by Ziyi Zhou
AdaFuse: Adaptive Ensemble Decoding for Large Language Models (2026.acl-long)
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Chengming Cui, Tianxin Wei, Ziyi Chen, Ruizhong Qiu, Zhichen Zeng, Zhining Liu, Xuying Ning, Duo Zhou, Jingrui He
| Challenge: | Existing ensemble approaches to large language models lack flexibility for mid-generation adaptation. |
| Approach: | They propose an adaptive ensemble decoding framework that dynamically selects semantically appropriate fusion units during generation. |
| Outcome: | The proposed framework outperforms existing ensemble frameworks on open-domain QA, arithmetic reasoning, and machine translation tasks. |
A Framework of Knowledge Graph-Enhanced Large Language Model Based on Question Decomposition and Atomic Retrieval (2024.findings-emnlp)
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| Challenge: | Existing methods to enhance LLMs with knowledge graphs have limited results . knowledge graph question answering (KGQA) provides interpretable reasoning for large language models . |
| Approach: | They propose a framework for KG-enhanced LLM based on question decomposition and atomic retrieval . they propose question decomposing tree as framework for LLM reasoning . |
| Outcome: | The proposed framework outperforms existing reasoning-based baselines on KGQA datasets. |
Separation and Fusion: A Novel Multiple Token Linking Model for Event Argument Extraction (2024.naacl-long)
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Jing Xu, Dandan Song, Siu Hui, Zhijing Wu, Meihuizi Jia, Hao Wang, Yanru Zhou, Changzhi Zhou, Ziyi Yang
| Challenge: | Existing methods for event argument extraction (EAE) lack cross-event information and require longer role sequences . et al. (2017): outperforms state-of-the-art methods for EE. |
| Approach: | They propose a separation-and-fusion paradigm to separate the acquisition of cross-event information and fuse it into the argument extraction of a target event. |
| Outcome: | The proposed model outperforms the state-of-the-art models on four widely used datasets. |
InterIntent: Investigating Social Intelligence of LLMs via Intention Understanding in an Interactive Game Context (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated the potential to mimic human social intelligence, but most studies focus on static self-report or performance-based tests. |
| Approach: | They propose a framework to assess LLMs' ability to understand and manage intentions by mapping their ability to infer the intentions of others in a game setting. |
| Outcome: | The proposed framework assesses LLMs' ability to understand and manage intentions in a game setting. |
SimVBG: Simulating Individual Values by Backstory Generation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have strong human-like capabilities, but rarely simulating individualized human values. |
| Approach: | They propose a framework that simulates individual values based on individual backstories . they use structured data on an individual to transform their backstoried information to a backstory . |
| Outcome: | The proposed framework improves top-1 accuracy by more than 10% over retrieval-augmented generation methods. |
Beyond Static Persona Consistency: Dynamic Persona Coherence in LLM Role-Playing (2026.acl-long)
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Yirui QI, Xiaoming Zhang, Ruilin Zeng, Mengyao Liu, Ziyi Zhou, Dezhuang Miao, Bingyu Yan, Zhenyu Guan
| Challenge: | Existing LLMs conflate identity consistency with emotional rigidity . Existing models exhibit either robotic repetition or persona drift . |
| Approach: | They propose a framework that decouples Identity-Layer Stability from Adaptive-Layer Appropriateness to achieve persona coherence repair. |
| Outcome: | Experiments on GPT-4o, Claude-3.5-Sonnet, and DeepSeek-V3.2 show consistent improvements (+16–84% gains) |
LiveChat: A Large-Scale Personalized Dialogue Dataset Automatically Constructed from Live Streaming (2023.acl-long)
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| Challenge: | a recent study shows that open-domain dialogue systems are not able to perform well in fast-growing scenarios such as live streaming due to the domain gap between online-post constructed data and those required in downstream conversational tasks. |
| Approach: | They propose to train a conversational agent based on large social media datasets with multiple domains to improve response in live streaming scenarios. |
| Outcome: | The proposed model improves response modeling and addressee recognition in live open-domain scenarios. |
ProMediate: A Simulation Testbed for Evaluating Proactive Mediation in Multi-Party Negotiation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are increasingly integrated into agentic frameworks to assist individual users in completing diverse tasks. |
| Approach: | They propose a simulation environment with a plug-and-play proactive AI mediator . they use a socio-cognitive evaluation framework to measure consensus changes, intervention latency, mediator effectiveness and intelligence. |
| Outcome: | The proposed model outperforms a generic baseline in multi-party negotiation scenarios while being 77% faster in response. |
TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation (2026.findings-acl)
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Bangde Du, Minghao Guo, Songming He, Ziyi Ye, Xi Zhu, Weihang Su, Shuqi Zhu, Yujia Zhou, Yongfeng Zhang, Qingyao Ai, Yiqun Liu
| Challenge: | Existing studies show that advanced LLMs produce text indistinguishable from human writing. |
| Approach: | They propose a benchmark to assess persona simulation across diverse contexts by decomposing the evaluation into six fundamental capabilities including opinion consistency, memory recall, logical reasoning, persona tone, and syntactic style. |
| Outcome: | The proposed model achieves moderate accuracy but falls short of the basic capabilities needed to simulate personas in real-world contexts. |
AiM: Taking Answers in Mind to Correct Chinese Cloze Tests in Educational Applications (2022.coling-1)
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| Challenge: | Existing methods to correct handwritten assignments are to use OCR to recognize characters and compare them to answers. |
| Approach: | They propose a multimodal approach to correct handwritten Chinese characters by combining the visual information of students' handwriting with the encoded representations of answers. |
| Outcome: | The proposed model outperforms OCR-based methods by a large margin. |
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. |
Evo-Attacker: Memory-Augmented Reinforcement Learning for Long-Horizon Tool Attacks on LLM-MAS (2026.acl-long)
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| Challenge: | Existing tool attacks are limited by domain specificity or fixed and static templates. |
| Approach: | They propose an attack-based memory-augmented reinforcement learning process that constructs a dynamic attack memory and employs deliberative reasoning to retrieve adversarial patterns. |
| Outcome: | Evo-Attacker outperforms baselines in the long-horizon credit assignment challenge. |