Zhuosheng Zhang

Selected Publications [Show All]

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[2025]

• Do NOT Think That Much for 2+ 3=? On the Overthinking of o1-Like LLMs
  Xingyu Chen, Jiahao Xu, Tian Liang, Zhiwei He, Jianhui Pang, Dian Yu, Linfeng Song, Qiuzhi Liu, Mengfei Zhou, Zhuosheng Zhang, Rui Wang, Zhaopeng Tu, Haitao Mi, Dong Yu.
  ICML, 2025
  [PDF] [Abstract] [Bib]
  

  The remarkable performance of models like the OpenAI o1 can be attributed to their ability to emulate human-like long-time thinking during inference. These models employ extended chain-of-thought (CoT) processes, exploring multiple strategies to enhance problem-solving capabilities. However, a critical question remains: How to intelligently and efficiently scale computational resources during testing. This paper presents the first comprehensive study on the prevalent issue of overthinking in these models, where excessive computational resources are allocated for simple problems with minimal benefit. We introduce novel efficiency metrics from both outcome and process perspectives to evaluate the rational use of computational resources by o1-like models. Using a self-training paradigm, we propose strategies to mitigate overthinking, streamlining reasoning processes without compromising accuracy. Experimental results show that our approach successfully reduces computational overhead while preserving model performance across a range of testsets with varying difficulty levels, such as GSM8K, MATH500, GPQA, and AIME.

  @article{chen2024not,
      title={Do NOT Think That Much for 2+ 3=? On the Overthinking of o1-Like LLMs},
      author={Chen, Xingyu and Xu, Jiahao and Liang, Tian and He, Zhiwei and Pang, Jianhui and Yu, Dian and Song, Linfeng and Liu, Qiuzhi and Zhou, Mengfei and Zhang, Zhuosheng and others},
      journal={arXiv preprint arXiv:2412.21187},
      year={2024}
      }
                

• Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Models
  Tianjie Ju, Yi Hua, Hao Fei, Zhenyu Shao, Yubin Zheng, Haodong Zhao, Mong-Li Lee, Wynne Hsu, Zhuosheng Zhang*, Gongshen Liu*.
  ICML, 2025
  [PDF] [Abstract] [Bib]
  
  ProbingPrivacy

  Multi-Modal Large Language Models (MLLMs) have exhibited remarkable performance on various vision-language tasks such as Visual Question Answering (VQA). Despite accumulating evidence of privacy concerns associated with task-relevant content, it remains unclear whether MLLMs inadvertently memorize private content that is entirely irrelevant to the training tasks. In this paper, we investigate how randomly generated task-irrelevant private content can become spuriously correlated with downstream objectives due to partial mini-batch training dynamics, thus causing inadvertent memorization. Concretely, we randomly generate task-irrelevant watermarks into VQA fine-tuning images at varying probabilities and propose a novel probing framework to determine whether MLLMs have inadvertently encoded such content. Our experiments reveal that MLLMs exhibit notably different training behaviors in partial mini-batch settings with task-irrelevant watermarks embedded. Furthermore, through layer-wise probing, we demonstrate that MLLMs trigger distinct representational patterns when encountering previously seen task-irrelevant knowledge, even if this knowledge does not influence their output during prompting. Our code is available at https://github.com/illusionhi/ProbingPrivacy.

  @article{ju2025watch,
    title={Watch Out Your Album! On the Inadvertent Privacy Memorization in Multi-Modal Large Language Models},
    author={Ju, Tianjie and Hua, Yi and Fei, Hao and Shao, Zhenyu and Zheng, Yubin and Zhao, Haodong and Lee, Mong-Li and Hsu, Wynne and Zhang, Zhuosheng and Liu, Gongshen},
    journal={arXiv preprint arXiv:2503.01208},
    year={2025}
  }
                

• Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science
  Xiangru Tang, Qiao Jin, Kunlun Zhu, Tongxin Yuan, Yichi Zhang, Wangchunshu Zhou, Meng Qu, Yilun Zhao, Jian Tang, Zhuosheng Zhang, Arman Cohan, Zhiyong Lu, Mark Gerstein.
  Nature Communications, 2025 (IF: 14.7)
  [PDF] [Abstract] [Bib]
  

  Intelligent agents powered by large language models (LLMs) have demonstrated substantial promise in autonomously conducting experiments and facilitating scientific discoveries across various disciplines. While their capabilities are promising, they also introduce novel vulnerabilities that demand careful consideration for safety. However, there exists a notable gap in the literature, as there has been no comprehensive exploration of these vulnerabilities. This position paper fills this gap by conducting a thorough examination of vulnerabilities in LLM-based agents within scientific domains, shedding light on potential risks associated with their misuse and emphasizing the need for safety measures. We begin by providing a comprehensive overview of the potential risks inherent to scientific LLM agents, taking into account user intent, the specific scientific domain, and their potential impact on the external environment. Then, we delve into the origins of these vulnerabilities and provide a scoping review of the limited existing works. Based on our analysis, we propose a triadic framework involving human regulation, agent alignment, and an understanding of environmental feedback (agent regulation) to mitigate these identified risks. Furthermore, we highlight the limitations and challenges associated with safeguarding scientific agents and advocate for the development of improved models, robust benchmarks, and comprehensive regulations to address these issues effectively.

  @article{tang2025prioritizing,
    title={Prioritizing Safeguarding Over Autonomy: Risks of LLM Agents for Science},
    author={Tang, Xiangru and Jin, Qiao Jin and Zhu, Kunlun and Yuan, Tongxin and Zhang, Yichi and Zhou, Wangchunshu and Qu, Meng and Zhao, Yilun and Tang, Jian and Zhang, Zhuosheng and Cohan, Arman and Lu, Zhiyong and Gerstein, Mark},
    journal={arXiv preprint arXiv:2402.04247},
    year={2025}
  }
                

• Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents
  Zhuosheng Zhang#, Yao Yao#, Aston Zhang, Xiangru Tang, Xinbei Ma, Zhiwei He, Yiming Wang, Mark Gerstein, Rui Wang, Gongshen Liu, Hai Zhao.
  CSUR, 2025 (IF: 23.8)
  "Join us on an exciting journey from chain-of-thought reasoning to language agent!"
  [PDF] [Abstract] [Bib]
  
  CoT-Igniting-Agent

  Large language models (LLMs) have dramatically enhanced the field of language intelligence, as demonstrably evidenced by their formidable empirical performance across a spectrum of complex reasoning tasks. Additionally, theoretical proofs have illuminated their emergent reasoning capabilities, providing a compelling showcase of their advanced cognitive abilities in linguistic contexts. Critical to their remarkable efficacy in handling complex reasoning tasks, LLMs leverage the intriguing chain-of-thought (CoT) reasoning techniques, obliging them to formulate intermediate steps en route to deriving an answer. The CoT reasoning approach has not only exhibited proficiency in amplifying reasoning performance but also in enhancing interpretability, controllability, and flexibility. In light of these merits, recent research endeavors have extended CoT reasoning methodologies to nurture the development of autonomous language agents, which adeptly adhere to language instructions and execute actions within varied environments. This survey paper orchestrates a thorough discourse, penetrating vital research dimensions, encompassing: (i) the foundational mechanics of CoT techniques, with a focus on elucidating the circumstances and justification behind its efficacy; (ii) the paradigm shift in CoT; and (iii) the burgeoning of language agents fortified by CoT approaches. Prospective research avenues envelop explorations into generalization, efficiency, customization, scaling, and safety. We hope to offer readers a comprehensive understanding of prevalent research areas such as CoT reasoning and language agents and illuminate the interconnections weaving through these areas. This paper caters to a wide audience, including beginners seeking comprehensive knowledge of CoT reasoning and language agents, as well as experienced researchers interested in foundational mechanics and engaging in cutting-edge discussions on these topics. A repository for the related papers is available at https://github.com/Zoeyyao27/CoT-Igniting-Agent.

  @article{zhang2025igniting,
    title={Igniting Language Intelligence: The Hitchhiker's Guide From Chain-of-Thought Reasoning to Language Agents},
    author={Zhang, Zhuosheng and Yao, Yao and Zhang, Aston and Tang, Xiangru and Ma, Xinbei and He, Zhiwei and Wang, Yiming and Gerstein, Mark and Wang, Rui and Liu, Gongshen and others},
    journal={ACM Computing Surveys},
    year={2025}
  }
                

• RaSA: Rank-Sharing Low-Rank Adaptation
  Zhiwei He, Zhaopeng Tu, Xing Wang, Xingyu Chen, Zhijie Wang, Jiahao Xu, Tian Liang, Wenxiang Jiao, Zhuosheng Zhang, Rui Wang.
  ICLR, 2025
  [PDF] [Abstract] [Bib]
  RaSA

  Low-rank adaptation (LoRA) has been prominently employed for parameter-efficient fine-tuning of large language models (LLMs). However, the limited expressive capacity of LoRA, stemming from the low-rank constraint, has been recognized as a bottleneck, particularly in rigorous tasks like code generation and mathematical reasoning. To address this limitation, we introduce Rank-Sharing Low-Rank Adaptation (RaSA), an innovative extension that enhances the expressive capacity of LoRA by leveraging partial rank sharing across layers. By forming a shared rank pool and applying layer-specific weighting, RaSA effectively increases the number of ranks without augmenting parameter overhead. Our theoretically grounded and empirically validated approach demonstrates that RaSA not only maintains the core advantages of LoRA but also significantly boosts performance in challenging code and math tasks.

• ChemAgent: Self-updating Memories in Large Language Models Improves Chemical Reasoning
  Xiangru Tang, Tianyu Hu, Muyang Ye, Yanjun Shao, Xunjian Yin, Siru Ouyang, Wangchunshu Zhou, Pan Lu, Zhuosheng Zhang, Yilun Zhao, Arman Cohan, Mark Gerstein.
  ICLR, 2025
  [PDF] [Abstract] [Bib]
  chemagent

  Chemical reasoning usually involves complex, multi-step processes that demand precise calculations, where even minor errors can lead to cascading failures. Furthermore, large language models (LLMs) encounter difficulties handling domain-specific formulas, executing reasoning steps accurately, and integrating code ef- effectively when tackling chemical reasoning tasks. To address these challenges, we present ChemAgent, a novel framework designed to improve the performance of LLMs through a dynamic, self-updating library. This library is developed by decomposing chemical tasks into sub-tasks and compiling these sub-tasks into a structured collection that can be referenced for future queries. Then, when presented with a new problem, ChemAgent retrieves and refines pertinent information from the library, which we call memory, facilitating effective task decomposition and the generation of solutions. Our method designs three types of memory and a library-enhanced reasoning component, enabling LLMs to improve over time through experience. Experimental results on four chemical reasoning datasets from SciBench demonstrate that ChemAgent achieves performance gains of up to 46% (GPT-4), significantly outperforming existing methods. Our findings suggest substantial potential for future applications, including tasks such as drug discovery and materials science.

• SynGhost: Invisible and Universal Task-agnostic Backdoor Attack via Syntactic Transfer
  Pengzhou Cheng, Wei Du, Zongru Wu, Fengwei Zhang, Libo Chen, Zhuosheng Zhang*, Gongshen Liu*.
  NAACL-Findings, 2025
  [PDF] [Abstract] [Bib]
  SynGhost

  Although pre-training achieves remarkable performance, it suffers from task-agnostic backdoor attacks due to vulnerabilities in data and training mechanisms. These attacks can transfer backdoors to various downstream tasks. In this paper, we introduce $\mathtt{maxEntropy}$, an entropy-based poisoning filter that mitigates such risks. To overcome the limitations of manual target setting and explicit triggers, we propose $\mathtt{SynGhost}$, an invisible and universal task-agnostic backdoor attack via syntactic transfer, further exposing vulnerabilities in pre-trained language models (PLMs). Specifically, $\mathtt{SynGhost}$ injects multiple syntactic backdoors into the pre-training space through corpus poisoning, while preserving the PLM's pre-training capabilities. Second, adaptively selects optimal targets based on contrastive learning, creating a uniform distribution in the pre-training space. To identify syntactic differences, we also introduce an awareness module to minimize interference between backdoors. Experiments show that $\mathtt{SynGhost}$ poses significant threats and can transfer to various downstream tasks. Furthermore, $\mathtt{SynGhost}$ resists defenses based on perplexity, fine-pruning, and $\mathtt{maxEntropy}$.

• Look before You Leap: Enhancing Attention and Vigilance regarding Harmful Content with GuidelineLLM
  Shaoqing Zhang, Zhuosheng Zhang, Kehai Chen, Rongxiang Weng, Muyun Yang, Tiejun Zhao, Min Zhang.
  AAAI, 2025
  [PDF] [Abstract] [Bib]
  
  GuidelineLLM

  Despite being empowered with alignment mechanisms, large language models (LLMs) are increasingly vulnerable to emerging jailbreak attacks that can compromise their alignment mechanisms. This vulnerability poses significant risks to the real-world applications. Existing work faces challenges in both training efficiency and generalization capabilities (i.e., Reinforcement Learning from Human Feedback and Red-Teaming). Developing effective strategies to enable LLMs to resist continuously evolving jailbreak attempts represents a significant challenge. To address this challenge, we propose a novel defensive paradigm called GuidelineLLM, which assists LLMs in recognizing queries that may have harmful content. Before LLMs respond to a query, GuidelineLLM first identifies potential risks associated with the query, summarizes these risks into guideline suggestions, and then feeds these guidelines to the responding LLMs. Importantly, our approach eliminates the necessity for additional safety fine-tuning of the LLMs themselves; only the GuidelineLLM requires fine-tuning. This characteristic enhances the general applicability of GuidelineLLM across various LLMs. Experimental results demonstrate that GuidelineLLM can significantly reduce the attack success rate (ASR) against the LLMs (an average reduction of 34.17\% ASR) while maintaining the helpfulness of the LLMs in handling benign queries. Code is available at https://github.com/sqzhang-lazy/GuidelineLLM.

  @article{zhang2024look,
    title={Look Before You Leap: Enhancing Attention and Vigilance Regarding Harmful Content with GuidelineLLM},
    author={Zhang, Shaoqing and Zhang, Zhuosheng and Chen, Kehai and Weng, Rongxiang and Yang, Muyun and Zhao, Tiejun and Zhang, Min},
    journal={arXiv preprint arXiv:2412.10423},
    year={2024}
  }
                

• Gracefully Filtering Backdoor Samples for Generative Large Language Models without Retraining
  Zongru Wu, Pengzhou Cheng, Lingyong Fang, Zhuosheng Zhang*, Gongshen Liu*.
  COLING, 2025
  [PDF] [Abstract] [Bib]
  
  GraceFul

  Backdoor attacks remain significant security threats to generative large language models (LLMs). Since generative LLMs output sequences of high-dimensional token logits instead of low-dimensional classification logits, most existing backdoor defense methods designed for discriminative models like BERT are ineffective for generative LLMs. Inspired by the observed differences in learning behavior between backdoor and clean mapping in the frequency space, we transform gradients of each training sample, directly influencing parameter updates, into the frequency space. Our findings reveal a distinct separation between the gradients of backdoor and clean samples in the frequency space. Based on this phenomenon, we propose Gradient Clustering in the Frequency Space for Backdoor Sample Filtering (GraCeFul), which leverages sample-wise gradients in the frequency space to effectively identify backdoor samples without requiring retraining LLMs. Experimental results show that GraCeFul outperforms baselines significantly. Notably, GraCeFul exhibits remarkable computational efficiency, achieving nearly 100% recall and F1 scores in identifying backdoor samples, reducing the average success rate of various backdoor attacks to 0% with negligible drops in clean accuracy across multiple free-style question answering datasets. Additionally, GraCeFul generalizes to Llama-2 and Vicuna. The codes are publicly available at https://github.com/ZrW00/GraceFul.

  @inproceedings{wu2024gracefully,
    title   = {Gracefully Filtering Backdoor Samples for Generative Large Language Models without Retraining},
    author  = {Wu, Zongru and Cheng, Pengzhou and Fang, Lingyong and Zhang, Zhuosheng and Liu, Gongshen},
    booktitle = {Proceedings of the 31st International Conference on Computational Linguistics (COLING 2025)},
    year    = {2025}
  }
                

[2024 & Before]

• Is it Possible to Edit Large Language Models Robustly?
  Xinbei Ma, Tianjie Ju, Jiyang Qiu, Zhuosheng Zhang*, Hai Zhao*, Lifeng Liu, Yulong Wang.
  EMNLP, 2024
  "The robustness of model editing remains an open question."
  [PDF] [Abstract] [Bib]
  
  edit_analysis

  Large language models (LLMs) have played a pivotal role in building communicative AI to imitate human behaviors but face the challenge of efficient customization. To tackle this challenge, recent studies have delved into the realm of model editing, which manipulates specific memories of language models and changes the related language generation. However, the robustness of model editing remains an open question. This work seeks to understand the strengths and limitations of editing methods, thus facilitating robust, realistic applications of communicative AI. Concretely, we conduct extensive analysis to address the three key research questions. Q1: Can edited LLMs behave consistently resembling communicative AI in realistic situations? Q2: To what extent does the rephrasing of prompts lead LLMs to deviate from the edited knowledge memory? Q3: Which knowledge features are correlated with the performance and robustness of editing? Our experimental results uncover a substantial disparity between existing editing methods and the practical application of LLMs. On rephrased prompts that are complex and flexible but common in realistic applications, the performance of editing experiences a significant decline. Further analysis shows that more popular knowledge is memorized better, easier to recall, and more challenging to edit effectively.

  @article{ma2024possible,
    title={Is it Possible to Edit Large Language Models Robustly?},
    author={Ma, Xinbei and Ju, Tianjie and Qiu, Jiyang and Zhang, Zhuosheng and Zhao, Hai and Liu, Lifeng and Wang, Yulong},
    journal={arXiv preprint arXiv:2402.05827},
    year={2024}
  }
                

• R-Judge: Benchmarking Safety Risk Awareness for LLM Agents
  Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang*, Rui Wang, Gongshen Liu.
  EMNLP-Findings, 2024
  "Are LLM agents aware of safety risks in real-world applications? Let's find out with R-Judge!"
  [PDF] [Abstract] [Bib]
  
  R-Judge

  Large language models (LLMs) have exhibited great potential in autonomously completing tasks across real-world applications. Despite this, these LLM agents introduce unexpected safety risks when operating in interactive environments. Instead of centering on LLM-generated content safety in most prior studies, this work addresses the imperative need for benchmarking the behavioral safety of LLM agents within diverse environments. We introduce R-Judge, a benchmark crafted to evaluate the proficiency of LLMs in judging safety risks given agent interaction records. R-Judge comprises 162 agent interaction records, encompassing 27 key risk scenarios among 7 application categories and 10 risk types. It incorporates human consensus on safety with annotated safety risk labels and high-quality risk descriptions. Utilizing R-Judge, we conduct a comprehensive evaluation of 8 prominent LLMs commonly employed as the backbone for agents. The best-performing model, GPT-4, achieves 72.29% in contrast to the human score of 89.38%, showing considerable room for enhancing the risk awareness of LLMs. Notably, leveraging risk descriptions as environment feedback significantly improves model performance, revealing the importance of salient safety risk feedback. Furthermore, we design an effective chain of safety analysis technique to help the judgment of safety risks and conduct an in-depth case study to facilitate future research. R-Judge is publicly available at https://github.com/Lordog/R-Judge.

  @article{yuan2024r,
    title={R-Judge: Benchmarking Safety Risk Awareness for LLM Agents},
    author={Yuan, Tongxin and He, Zhiwei and Dong, Lingzhong and Wang, Yiming and Zhao, Ruijie and Xia, Tian and Xu, Lizhen and Zhou, Binglin and Li, Fangqi and Zhang, Zhuosheng and Wang, Rui and Liu, Gongshen},
    journal={arXiv preprint arXiv:2401.10019},
    year={2024}
  }
                

• Multimodal Chain-of-Thought Reasoning in Language Models
  Zhuosheng Zhang, Aston Zhang, Mu Li, Hai Zhao, George Karypis, Alex Smola.
  TMLR, 2024
  "Imagine learning a textbook with no figures: Multimodal-CoT surpasses humans on ScienceQA."
  Featured in Dive into Deep Learning (Adopted at 500 universities from 70 countries)
  [Top Trending Research on paperswithcode] [Idea Inspiration] [PDF] [Abstract] [Bib]
  
  MM-CoT

  Large language models (LLMs) have shown impressive performance on complex reasoning by leveraging chain-of-thought (CoT) prompting to generate intermediate reasoning chains as the rationale to infer the answer. However, existing CoT studies are mostly isolated in the language modality with LLMs, where LLMs are hard to deploy. To elicit CoT reasoning in multimodality, a possible solution is to fine-tune small language models by fusing the vision and language features to perform CoT reasoning. The key challenge is that those language models tend to generate hallucinated reasoning chains that mislead the answer inference. To mitigate the effect of such mistakes, we propose Multimodal-CoT that incorporates vision features in a decoupled training framework. The framework separates the rationale generation and answer inference into two stages. By incorporating the vision features in both stages, the model is able to generate effective rationales that contribute to answer inference. With Multimodal-CoT, our model under 1 billion parameters outperforms the previous state-of-the-art LLM (GPT-3.5) by 16% (75.17%->91.68%) on the ScienceQA benchmark and even surpasses human performance. Code is publicly available at https://github.com/amazon-science/mm-cot.

  @article{zhang2023multicot,
    title={Multimodal Chain-of-Thought Reasoning in Language Models},
    author={Zhang, Zhuosheng and Zhang, Aston and Li, Mu and Zhao, Hai and Karypis, George and Smola, Alex},
    journal={arXiv preprint arXiv:2302.00923},
    year={2023}
  }
                

• You Only Look at Screens: Multimodal Chain-of-Action Agents
  Zhuosheng Zhang, Aston Zhang.
  ACL-Findings, 2024
  "Perform a task on smart phones? Train an agent using screenshots."
  [PDF] [Abstract] [Bib] [slides]
  
  Auto-GUI

  Autonomous graphical user interface (GUI) agents aim to facilitate task automation by interacting with the user interface without manual intervention. Recent studies have investigated eliciting the capabilities of large language models (LLMs) for effective engagement in diverse environments. To align with the input-output requirement of LLMs, most existing approaches are developed under a sandbox setting where they rely on external tools and application-specific APIs to parse the environment into textual elements and interpret the predicted actions. Consequently, those approaches often grapple with inference inefficiency and error propagation risks. To mitigate the challenges, we introduce Auto-GUI, a multimodal solution that directly interacts with the interface, bypassing the need for environment parsing or reliance on application-dependent APIs. Moreover, we propose a chain-of-action technique -- leveraging a series of intermediate previous action histories and future action plans -- to help the agent decide what action to execute. We evaluate our approach on a new device-control benchmark AITW with 30K unique instructions, spanning multi-step tasks such as application operation, web searching, and web shopping. Experimental results show that Auto-GUI achieves state-of-the-art performance with an action type prediction accuracy of 90\% and an overall action success rate of 74\%. Code is publicly available at https://github.com/cooelf/Auto-GUI.

  @article{zhang2023autoui,
    title={You Only Look at Screens: Multimodal Chain-of-Action Agents},
    author={Zhang, Zhuosheng and Zhang, Aston},
    journal={arXiv preprint arXiv:2309.11436},
    year={2023}
  }
                

• Automatic Chain of Thought Prompting in Large Language Models
  Zhuosheng Zhang, Aston Zhang, Mu Li, Alex Smola.
  ICLR, 2023
  "Let's think not just step by step, but also one by one."
  Featured in Dive into Deep Learning (Adopted at 400 universities from 60 countries)
  [PDF] [Abstract] [Bib] [bilibili] [slides]
  
  Auto-CoT

  Large language models (LLMs) can perform complex reasoning by generating intermediate reasoning steps. Providing these steps for prompting demonstrations is called chain-of-thought (CoT) prompting. CoT prompting has two major paradigms. One leverages a simple prompt like "Let's think step by step" to facilitate step-by-step thinking before answering a question. The other uses a few manual demonstrations one by one, each composed of a question and a reasoning chain that leads to an answer. The superior performance of the second paradigm hinges on the hand-crafting of task-specific demonstrations one by one. We show that such manual efforts may be eliminated by leveraging LLMs with the "Let's think step by step" prompt to generate reasoning chains for demonstrations one by one, i.e., let's think not just step by step, but also one by one. However, these generated chains often come with mistakes. To mitigate the effect of such mistakes, we find that diversity matters for automatically constructing demonstrations. We propose an automatic CoT prompting method: Auto-CoT. It samples questions with diversity and generates reasoning chains to construct demonstrations. On ten public benchmark reasoning tasks with GPT-3, Auto-CoT consistently matches or exceeds the performance of the CoT paradigm that requires manual designs of demonstrations. Code is available at https://github.com/amazon-research/auto-cot

  @inproceedings{zhang2023automatic,
    title={Automatic Chain of Thought Prompting in Large Language Models},
    author={Zhang, Zhuosheng and Zhang, Aston and Li, Mu and Smola, Alex},
    booktitle={The Eleventh International Conference on Learning Representations (ICLR 2023)},
    year={2023}
  }
                

• Universal Multimodal Representation for Language Understanding
  Zhuosheng Zhang#, Kehai Chen, Rui Wang#, Masao Utiyama, Eiichiro Sumita, Zuchao Li, Hai Zhao
  TPAMI, 2023 (IF: 20.8)
  "Let's retrieve images to overcome the lack of large-scale bilingual pairs."
  [PDF] [Abstract] [Bib]
  UVR-NMT

  Representation learning is the foundation of natural language processing (NLP). This work presents new methods to employ visual information as assistant signals to general NLP tasks. For each sentence, we first retrieve a flexible number of images either from a light topic-image lookup table extracted over the existing sentence-image pairs or a shared cross-modal embedding space that is pre-trained on out-of-shelf text-image pairs. Then, the text and images are encoded by a Transformer encoder and convolutional neural network, respectively. The two sequences of representations are further fused by an attention layer for the interaction of the two modalities. In this study, the retrieval process is controllable and flexible. The universal visual representation overcomes the lack of large-scale bilingual sentence-image pairs. Our method can be easily applied to text-only tasks without manually annotated multimodal parallel corpora. We apply the proposed method to a wide range of natural language generation and understanding tasks, including neural machine translation, natural language inference, and semantic similarity. Experimental results show that our method is generally effective for different tasks and languages. Analysis indicates that the visual signals enrich textual representations of content words, provide fine-grained grounding information about the relationship between concepts and events, and potentially conduce to disambiguation.

  @ARTICLE{zhang2023universal,
    author={Zhang, Zhuosheng and Chen, Kehai and Wang, Rui and Utiyama, Masao and Sumita, Eiichiro and Li, Zuchao and Zhao, Hai},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
    title={Universal Multimodal Representation for Language Understanding}, 
    year={2023},
    volume={},
    number={},
    pages={1-18},
    doi={10.1109/TPAMI.2023.3234170}}
                

• SG-Net: Syntax Guided Transformer for Language Representation
  Zhuosheng Zhang, Yuwei Wu, Junru Zhou, Sufeng Duan, Hai Zhao, Rui Wang.
  TPAMI, 2022 (IF: 20.8)
  [PDF] [Abstract] [Bib]
  
  SG-Net

  Understanding human language is one of the key themes of artificial intelligence. For language representation, the capacity of effectively modeling the linguistic knowledge from the detail-riddled and lengthy texts and getting rid of the noises is essential to improve its performance. Traditional attentive models attend to all words without explicit constraint, which results in inaccurate concentration on some dispensable words. In this work, we propose using syntax to guide the text modeling by incorporating explicit syntactic constraints into attention mechanisms for better linguistically motivated word representations. In detail, for self-attention network (SAN) sponsored Transformer-based encoder, we introduce syntactic dependency of interest (SDOI) design into the SAN to form an SDOI-SAN with syntax-guided self-attention. Syntax-guided network (SG-Net) is then composed of this extra SDOI-SAN and the SAN from the original Transformer encoder through a dual contextual architecture for better linguistics inspired representation. The proposed SG-Net is applied to typical Transformer encoders. Extensive experiments on popular benchmark tasks, including machine reading comprehension, natural language inference, and neural machine translation show the effectiveness of the proposed SG-Net design.

  @article{zhang2022sg,
    title={SG-Net: Syntax Guided Transformer for Language Representation},
    author={Zhang, Zhuosheng and Wu, Yuwei and Zhou, Junru and Duan, Sufeng and Zhao, Hai and Wang, Rui},
    journal={IEEE Transactions on Pattern Analysis \& Machine Intelligence},
    volume={44},
    number={06},
    pages={3285--3299},
    year={2022},
    publisher={IEEE Computer Society}
  }
                

• Text Compression-aided Transformer Encoding
  Zuchao Li, Zhuosheng Zhang, Hai Zhao, Rui Wang, Kehai Chen, Masao Utiyama, Eiichiro Sumita.
  TPAMI, 2022 (IF: 20.8)
  [PDF] [Abstract] [Bib]
  
  esc4nmt

  Text encoding is one of the most important steps in Natural Language Processing (NLP). It has been done well by the self-attention mechanism in the current state-of-the-art Transformer encoder, which has brought about significant improvements in the performance of many NLP tasks. Though the Transformer encoder may effectively capture general information in its resulting representations, the backbone information, meaning the gist of the input text, is not specifically focused on. In this paper, we propose explicit and implicit text compression approaches to enhance the Transformer encoding and evaluate models using this approach on several typical downstream tasks that rely on the encoding heavily. Our explicit text compression approaches use dedicated models to compress text, while our implicit text compression approach simply adds an additional module to the main model to handle text compression. We propose three ways of integration, namely backbone source-side fusion, target-side fusion, and both-side fusion, to integrate the backbone information into Transformer-based models for various downstream tasks. Our evaluation on benchmark datasets shows that the proposed explicit and implicit text compression approaches improve results in comparison to strong baselines. We therefore conclude, when comparing the encodings to the baseline models, text compression helps the encoders to learn better language representations.

  @article{li2022text,
    title={Text Compression-Aided Transformer Encoding},
    author={Li, Zuchao and Zhang, Zhuosheng and Zhao, Hai and Wang, Rui and Chen, Kehai and Utiyama, Masao and Sumita, Eiichiro},
    journal={IEEE Transactions on Pattern Analysis \& Machine Intelligence},
    volume={44},
    number={07},
    pages={3840--3857},
    year={2022},
    publisher={IEEE Computer Society}
  }
                

Shared Tasks

[May 2022] HellaSwag Leaderboard on Commonsense Reasoning

• The best among all submissions.
[Leaderboard] [Paper]

[January 2021] ShARC Leaderboard on Conversational Question Answering

• The best among all submissions.
[Leaderboard] [Paper]

[September 2020] MuTual Leaderboard on Dialogue Reasoning Challenge

• The best among all submissions.
[Leaderboard] [Paper]

[July 2019] SQuAD2.0 Leaderboard on Machine Reading Comprehension

• The best models for both single and ensemble settings among all submissions (2020.01).
• The first to surpass human benchmark on both EM and F1 scores with a single model (from 2019.07-09).
• The first time to exceed 90% F1 score with ensemble models.
  [Leaderboard] [Paper] [Report]

[March 2019] RACE Leaderboard on Machine Reading Comprehension

• The best among all submissions.
• The best among all academic submissions.
  [Leaderboard] [Paper] [Report]

[April 2019] SNLI Leaderboard on Language Inference

• The best among all submissions.
  [Leaderboard] [Paper]

[March 2019] GLUE Leaderboard on Language Understanding

• The 3rd best among all submissions.
• The best among all academic submissions.
  [Leaderboard] [Paper]

[August 2017] Chinese Machine Reading Comprehension (CCL-CMRC 2017)

• The best single system and the second ensemble system (Silver Medal).
  [Leaderboard] [Paper] [Source]

Awards & Honors

• 2024: WAIC Youth Outstanding Paper Award, World Artificial Intelligence Conference.

• 2024: WAIC YunFan Award: Bright Star, World Artificial Intelligence Conference.

• 2023: Excellent Doctoral Thesis of Chinese Information Processing Society (CIPS).

• 2023: Shanghai Outstanding Doctoral Graduate.

• 2022: Academic Stars of Graduate Students (10 recipients), Shanghai Jiao Tong University.

• 2021: Global Top 100 Chinese Rising Stars in Artificial Intelligence (Top 10 recommended), Baidu Research.

• 2021: Baidu Scholarship (10 recipients, worldwide), Baidu.

• 2020: National Scholarship of China, Ministry of Education of the P.R. China.

• 2019: Yang Yuanqing Education Fund, The foundation of Class 1988 in CS @ Shanghai Jiao Tong University.

• 2018: Academic Stars of Graduate Students (The only master student awardee), Shanghai Jiao Tong University.

• 2016: National Figures Nomination of College Students (20 total recipients), Ministry of Education of the P.R. China.

• 2015: CCF Elite Collegiate Award, China Computer Federation.

Teaching

• NIS3353: Artificial Intelligence Security
  Undergraduate, Shanghai Jiao Tong University, Spring 2025.
• NIS8021: Frontier Technology in Natural Language Processing
  Graduate, Shanghai Jiao Tong University, Fall 2024.
• NIS3353: Artificial Intelligence Security
  Undergraduate, Shanghai Jiao Tong University, Spring 2024.

Academic Service

• Organization:
  • Session Chair at RL China 2024.
  • Session Chair at CJNLP 2024.
  • Session Chair at IJCNLP-AACL 2023.
  • Co-chair of CCL Student Seminar, 2022
  • President of IBM Tech Club at Wuhan University, 2014-2015.
• (Senior) Area Chair / Action Editor/ SPC:
  • ACL Rolling Review
  • NeurIPS 2025
  • EMNLP 2025
  • ACL 2025
  • LREC-COLING 2024
  • IJCAI 2024
  • ICLR 2023 TinyPapers
• Program Committee Member:
  • ML/AI conferences: ICLR, ICML, NeurIPS, AAAI, IJCAI, etc.
  • CL/NLP conferences: ARR, ACL, EMNLP, COLING, NAACL, AACL, NLPCC, CCL, etc.

• Journal Reviewer: Artificial Intelligence, IEEE/ACM TASLP, IEEE TNNLS, IEEE TETCI, IEEE Communications Magazine, ACM TALLIP, ACM TOIS, TMLR, Neurocomputing, Multimedia Systems, Neural Computing and Applications, Expert Systems With Applications.

Experience

• Jul. 2022 - Aug. 2023, Amazon Web Services AI, CA, USA.
  Applied Scientist Intern (remote), advised by Dr. Aston Zhang, Mu Li, Alex Smola.
• Feb. 2022 - June. 2022, Microsoft Cognitive Services Research Group, WA, USA.
  Research Intern (remote), advised by Dr. Shuohang Wang.
• Mar. 2021 - Dec. 2021, Langboat Tech, Beijing, China.
  Research Intern (remote), advised by Prof. Ming Zhou.
• Jun. 2019 - Jul. 2020, NICT, Kyoto, Japan.
  Internship Research Fellow, advised by Prof. Rui Wang, Kehai Chen, Masao Utiyama, and Eiichiro Sumita.

Education

• Sept. 2020 - Sept. 2023
  Ph.D., Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, advised by Prof. Hai Zhao.
• Sept. 2016 - Mar. 2020
  M.S., Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, advised by Prof. Hai Zhao.
• Sept. 2012 - Jun. 2016
  B.S., Dept. of Computer Science and Engineering, Wuhan University, advised by Prof. Haojun Ai.