I am a research assistant at Generative AI Research Lab (GAIR) to explore Generative AI, fortunately working with Dr. Pengfei Liu. Before that, I received the M.S. degree in Computer Technology at Institute of Computing Technology (ICT) of Chinese Academy of Sciences (CAS) supervised by Prof. Jiafeng Guo in 2024 and the B.E. degree in computer science and technology from Shanghai Maritime University (SHMTU) in 2021.
Research Interests: My primary research interests include natural language processing, large language models, and machine learning. Specifically, My current research focuses:I am happy to collaborate and/or answer questions about my research. If you are interested in research collaboration or have any inquiries about my experience, please send me an email.
I am looking for a Ph.D. position in the 2025 fall (US), please drop me an email if you'd like to collaborate with me.
The 1st Workshop on Data Contamination (CONDA 2024) focuses on all relevant aspects of data contamination in natural language processing, where data contamination is understood as situations where evaluation data is included in pre-training corpora used to train large scale models, compromising evaluation results. The workshop fostered a shared task to collect evidence on data contamination in current available datasets and models. The goal of the shared task and associated database is to assist the community in understanding the extent of the problem and to assist researchers in avoiding reporting evaluation results on known contaminated resources. The shared task provides a structured, centralized public database for the collection of contamination evidence, open to contributions from the community via GitHub pool requests. This first compilation paper is based on 566 reported entries over 91 contaminated sources from a total of 23 contributors. The details of the individual contamination events are available in the platform. The platform continues to be online, open to contributions from the community. |
@inproceedings{sainz-etal-2024-data, title = "Data Contamination Report from the 2024 {CONDA} Shared Task", author = "Sainz, Oscar and Garc{\'\i}a-Ferrero, Iker and Jacovi, Alon and Ander Campos, Jon and Elazar, Yanai and Agirre, Eneko and Goldberg, Yoav and Chen, Wei-Lin and Chim, Jenny and Choshen, Leshem and D{'}Amico-Wong, Luca and Dell, Melissa and Fan, Run-Ze and Golchin, Shahriar and Li, Yucheng and Liu, Pengfei and Pahwa, Bhavish and Prabhu, Ameya and Sharma, Suryansh and Silcock, Emily and Solonko, Kateryna and Stap, David and Surdeanu, Mihai and Tseng, Yu-Min and Udandarao, Vishaal and Wang, Zengzhi and Xu, Ruijie and Yang, Jinglin", editor = "Sainz, Oscar and Garc{\'\i}a Ferrero, Iker and Agirre, Eneko and Ander Campos, Jon and Jacovi, Alon and Elazar, Yanai and Goldberg, Yoav", booktitle = "Proceedings of the 1st Workshop on Data Contamination (CONDA)", month = aug, year = "2024", address = "Bangkok, Thailand", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2024.conda-1.4", pages = "41--56", }
The evolution of Artificial Intelligence (AI) has been significantly accelerated by advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs), gradually showcasing potential cognitive reasoning abilities in problem-solving and scientific discovery (i.e., AI4Science) once exclusive to human intellect. To comprehensively evaluate current models' performance in cognitive reasoning abilities, we introduce OlympicArena, which includes 11,163 bilingual problems across both text-only and interleaved text-image modalities. These challenges encompass a wide range of disciplines spanning seven fields and 62 international Olympic competitions, rigorously examined for data leakage. We argue that the challenges in Olympic competition problems are ideal for evaluating AI's cognitive reasoning due to their complexity and interdisciplinary nature, which are essential for tackling complex scientific challenges and facilitating discoveries. Beyond evaluating performance across various disciplines using answer-only criteria, we conduct detailed experiments and analyses from multiple perspectives. We delve into the models' cognitive reasoning abilities, their performance across different modalities, and their outcomes in process-level evaluations, which are vital for tasks requiring complex reasoning with lengthy solutions. Our extensive evaluations reveal that even advanced models like GPT-4o only achieve a 39.97% overall accuracy, illustrating current AI limitations in complex reasoning and multimodal integration. Through the OlympicArena, we aim to advance AI towards superintelligence, equipping it to address more complex challenges in science and beyond. We also provide a comprehensive set of resources to support AI research, including a benchmark dataset, an open-source annotation platform, a detailed evaluation tool, and a leaderboard with automatic submission features. |
@article{huang2024olympicarena, title={OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI}, author={Zhen Huang and Zengzhi Wang and Shijie Xia and Xuefeng Li and Haoyang Zou and Ruijie Xu and Run-Ze Fan and Lyumanshan Ye and Ethan Chern and Yixin Ye and Yikai Zhang and Yuqing Yang and Ting Wu and Binjie Wang and Shichao Sun and Yang Xiao and Yiyuan Li and Fan Zhou and Steffi Chern and Yiwei Qin and Yan Ma and Jiadi Su and Yixiu Liu and Yuxiang Zheng and Shaoting Zhang and Dahua Lin and Yu Qiao and Pengfei Liu}, year={2024}, journal={arXiv preprint arXiv:2406.12753}, url={https://arxiv.org/abs/2406.12753} }
Amid the expanding use of pre-training data, the phenomenon of benchmark dataset leakage has become increasingly prominent, exacerbated by opaque training processes and the often undisclosed inclusion of supervised data in contemporary Large Language Models (LLMs). This issue skews benchmark effectiveness and fosters potentially unfair comparisons, impeding the field's healthy development. To address this, we introduce a detection pipeline utilizing Perplexity and N-gram accuracy, two simple and scalable metrics that gauge a model's prediction precision on benchmark, to identify potential data leakages. By analyzing 31 LLMs under the context of mathematical reasoning, we reveal substantial instances of training even test set misuse, resulting in potentially unfair comparisons. These findings prompt us to offer several recommendations regarding model documentation, benchmark setup, and future evaluations. Notably, we propose the "Benchmark Transparency Card" to encourage clear documentation of benchmark utilization, promoting transparency and healthy developments of LLMs. we have made our leaderboard, pipeline implementation, and model predictions publicly available, fostering future research. |
@article{xu2024benchmarking, title={Benchmarking Benchmark Leakage in Large Language Models}, author={Xu, Ruijie and Wang, Zengzhi and Fan, Run-Ze and Liu, Pengfei}, year={2024}, journal={arXiv preprint arXiv:2404.18824}, url={https://arxiv.org/abs/2404.18824} }
The quality of finetuning data is crucial for aligning large language models (LLMs) with human values. Current methods to improve data quality are either labor-intensive or prone to factual errors caused by LLM hallucinations. This paper explores elevating the quality of existing instruction data to better align with human values, introducing a simple and effective approach named ReAlign, which reformats the responses of instruction data into a format that better aligns with pre-established criteria and the collated evidence. This approach minimizes human annotation, hallucination, and the difficulty in scaling, remaining orthogonal to existing alignment techniques. Experimentally, ReAlign significantly boosts the general alignment ability, math reasoning, factuality, and readability of the LLMs. Encouragingly, without introducing any additional data or advanced training techniques, and merely by reformatting the response, LLaMA-2-13B's mathematical reasoning ability on GSM8K can be improved from 46.77% to 56.63% in accuracy. Additionally, a mere 5% of ReAlign data yields a 67% boost in general alignment ability measured by the Alpaca dataset. This work highlights the need for further research into the science and mechanistic interpretability of LLMs. We have made the associated code and data publicly accessible to support future studies at https://github.com/GAIR-NLP/ReAlign. |
@article{fan2024reformatted, title={Reformatted Alignment}, author={Fan, Run-Ze and Li, Xuefeng and Zou, Haoyang and Li, Junlong and He, Shwai and Chern, Ethan and Hu, Jiewen and Liu, Pengfei}, year={2024}, journal={arXiv preprint arXiv:2402.12219}, url={https://arxiv.org/abs/2402.12219} }
Automatic mainstream hashtag recommendation aims to accurately provide users with concise and popular topical hashtags before publication. Generally, mainstream hashtag recommendation faces challenges in the comprehensive difficulty of newly posted tweets in response to new topics, and the accurate identification of mainstream hashtags beyond semantic correctness. However, previous retrieval-based methods based on a fixed predefined mainstream hashtag list excel in producing mainstream hashtags, but fail to understand the constant flow of up-to-date information. Conversely, generation-based methods demonstrate a superior ability to comprehend newly posted tweets, but their capacity is constrained to identifying mainstream hashtags without additional features. Inspired by the recent success of the retrieval-augmented technique, in this work, we attempt to adopt this framework to combine the advantages of both approaches. Meantime, with the help of the generator component, we could rethink how to further improve the quality of the retriever component at a low cost. Therefore, we propose RetrIeval-augmented Generative Mainstream HashTag Recommender (RIGHT), which consists of three components: 1) a retriever seeks relevant hashtags from the entire tweet-hashtags set; 2) a selector enhances mainstream identification by introducing global signals; and 3) a generator incorporates input tweets and selected hashtags to directly generate the desired hashtags. The experimental results show that our method achieves significant improvements over state-of-the-art baselines. Moreover, RIGHT can be easily integrated into large language models, improving the performance of ChatGPT by more than 10%. |
@inproceedings{fan2024right, title={RIGHT: Retrieval-Augmented Generation for Mainstream Hashtag Recommendation}, author={Fan, Run-Ze and Fan, Yixing and Chen, Jiangui and Guo, Jiafeng and Zhang, Ruqing and Cheng, Xueqi}, booktitle={European Conference on Information Retrieval}, pages={39--55}, year={2024}, organization={Springer}, url={https://link.springer.com/chapter/10.1007/978-3-031-56027-9_3} }
The rapid development of Large Language Models (LLMs) has substantially expanded the range of tasks they can address. In the field of Natural Language Processing (NLP), researchers have shifted their focus from conventional NLP tasks (e.g., sequence tagging and parsing) towards tasks that revolve around aligning with human needs (e.g., brainstorming and email writing). This shift in task distribution imposes new requirements on evaluating these aligned models regarding generality (i.e., assessing performance across diverse scenarios), flexibility (i.e., examining under different protocols), and interpretability (i.e., scrutinizing models with explanations). In this paper, we propose a generative judge with 13B parameters, Auto-J, designed to address these challenges. Our model is trained on user queries and LLM-generated responses under massive real-world scenarios and accommodates diverse evaluation protocols (e.g., pairwise response comparison and single-response evaluation) with well-structured natural language critiques. To demonstrate the efficacy of our approach, we construct a new testbed covering 58 different scenarios. Experimentally, Auto-J outperforms a series of strong competitors, including both open-source and closed-source models, by a large margin. We also provide detailed analysis and case studies to further reveal the potential of our method and make a variety of resources public at this https://github.com/GAIR-NLP/auto-j. |
@article{li2023generative, title={Generative Judge for Evaluating Alignment}, author={Li, Junlong and Sun, Shichao and Yuan, Weizhe and Fan, Run-Ze and Zhao, Hai and Liu, Pengfei}, journal={arXiv preprint arXiv:2310.05470}, year={2023}, url={https://arxiv.org/abs/2310.05470} }
Scaling the size of language models usually leads to remarkable advancements in NLP tasks. But it often comes with a price of growing computational cost. Although a sparse Mixture of Experts (MoE) can reduce the cost by activating a small subset of parameters (e.g., one expert) for each input, its computation escalates significantly if increasing the number of activated experts, limiting its practical utility. Can we retain the advantages of adding more experts without substantially increasing the computational costs? In this paper, we first demonstrate the superiority of selecting multiple experts and then propose a computation-efficient approach called Merging Experts into One (MEO), which reduces the computation cost to that of a single expert. Extensive experiments show that MEO significantly improves computational efficiency, e.g., FLOPS drops from 72.0G of vanilla MoE to 28.6G (MEO). Moreover, we propose a token-level attention block that further enhances the efficiency and performance of token-level MEO, e.g., 83.3% (MEO) vs. 82.6% (vanilla MoE) average score on the GLUE benchmark. Our code will be released upon acceptance. Code will be released at: https://github.com/Shwai-He/MEO. |
@inproceedings{he-etal-2023-merging, title = "Merging Experts into One: Improving Computational Efficiency of Mixture of Experts", author = "He, Shwai and Fan, Run-Ze and Ding, Liang and Shen, Li and Zhou, Tianyi and Tao, Dacheng", editor = "Bouamor, Houda and Pino, Juan and Bali, Kalika", booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing", month = dec, year = "2023", address = "Singapore", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.emnlp-main.907", doi = "10.18653/v1/2023.emnlp-main.907", pages = "14685--14691", abstract = "Scaling the size of language models usually leads to remarkable advancements in NLP tasks. But it often comes with a price of growing computational cost. Although a sparse Mixture of Experts (MoE) can reduce the cost by activating a small subset of parameters (e.g., one expert) for each input, its computation escalates significantly if increasing the number of activated experts, limiting its practical utility. Can we retain the advantages of adding more experts without substantially increasing the computational costs? In this paper, we first demonstrate the superiority of selecting multiple experts and then propose a computation-efficient approach called \textbf{Merging Experts into One} (MEO), which reduces the computation cost to that of a single expert. Extensive experiments show that MEO significantly improves computational efficiency, e.g., FLOPS drops from 72.0G of vanilla MoE to 28.6G (MEO). Moreover, we propose a token-level attention block that further enhances the efficiency and performance of token-level MEO, e.g., 83.3{\%} (MEO) vs. 82.6{\%} (vanilla MoE) average score on the GLUE benchmark. Our code will be released upon acceptance. Code will be released at: \url{https://github.com/Shwai-He/MEO}.", }
Adapter tuning, which updates only a few parameters, has become a mainstream method for fine-tuning pretrained language models to downstream tasks. However, it often yields subpar results in few-shot learning. AdapterFusion, which assembles pretrained adapters using composition layers tailored to specific tasks, is a possible solution but significantly increases trainable parameters and deployment costs. Despite this, our preliminary study reveals that even single adapters can outperform Adapterfusion in few-shot learning, urging us to propose Merging Pretrained Adapters (MerA) that efficiently incorporates pretrained adapters to a single model through model fusion. Extensive experiments on two PLMs demonstrate that MerA achieves substantial improvements compared to both single adapters and AdapterFusion. To further enhance the capacity of MerA, we also introduce a simple yet effective technique, referred to as the "same-track" setting, that merges adapters from the same track of pretraining tasks. With the implementation of the "same-track" setting, we observe even more impressive gains, surpassing the performance of both full fine-tuning and adapter tuning by a substantial margin, e.g., 3.5% in MRPC and 5.0% in MNLI. |
@article{he2023mera, title={Mera: Merging pretrained adapters for few-shot learning}, author={He, Shwai and Fan, Run-Ze and Ding, Liang and Shen, Li and Zhou, Tianyi and Tao, Dacheng}, journal={arXiv preprint arXiv:2308.15982}, year={2023}, url={https://arxiv.org/abs/2308.15982} }
Shanghai Jiao Tong University, 2023.05 - Present
Generative AI Research Lab (GAIR)
Research assistant, supervised by Dr. Pengfei Liu.
University of Chinese Academy of Sciences, 2021.09 - 2024.06
Institute of Computing Technology
M.S. in Computer Science and Technology, supervised by Prof. Jiafeng Guo.
Shanghai Maritime University, 2017.09 - 2021.06
B.E. in Computer Science and Technology
2024: Excellent Master’s Graduation Thesis, Institute of Computing Technology
2021: Excellent Bachelor's Graduation Thesis, Shanghai Maritime University
2021: Excellent Graduate, Shanghai Maritime University
2019, 2020, 2021: First Class Scholarship, Shanghai Maritime University