Run-Ze Fan

Publications

• Benchmarking Benchmark Leakage in Large Language Models
  Ruijie Xu*, Zengzhi Wang*, Run-Ze Fan*, Pengfei Liu.
  arXiv, 2024
  [PDF] [Abstract] [Bib] [Code] [Page] [HuggingFace Demo]
  

  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}
  }

• Reformatted Alignment
  Run-Ze Fan, Xuefeng Li, Haoyang Zou, Junlong Li, Shwai He, Ethan Chern, Jiewen Hu, Pengfei Liu.
  arXiv, 2024
  [PDF] [Abstract] [Bib] [Code] [Page] [Featured by AK] [量子位]
  

  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}
  }

• RIGHT: Retrieval-augmented Generation for Mainstream Hashtag Recommendation
  Run-Ze Fan, Yixing Fan, Jiangui Chen, Jiafeng Guo, Ruqing Zhang, Xueqi Cheng.
  ECIR, 2024
  [PDF] [Abstract] [Bib] [Code]
  

  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}
  }

• Generative Judge for Evaluating Alignment
  Junlong Li, Shichao Sun, Weizhe Yuan, Run-Ze Fan, Hai Zhao, Pengfei Liu.
  ICLR, 2024
  [PDF] [Abstract] [Bib] [Code] [Page] [机器之心]
  

  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}
  }

• Merging Experts into One: Improving Computational Efficiency of Mixture of Experts
  Shwai He, Run-Ze Fan, Liang Ding, Li Shen, Tianyi Zhou, Dacheng Tao.
  EMNLP, 2023 (Oral)
  [PDF] [Abstract] [Bib] [Code]
  

  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}.",
  }

• MerA: Merging Pretrained Adapters For Few-Shot Learning
  Shwai He, Run-Ze Fan, Liang Ding, Li Shen, Tianyi Zhou, Dacheng Tao.
  arXiv, 2023
  [PDF] [Abstract] [Bib]
  

  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}
  }

Education & Research Experience

Blogs

Selected Honors & Awards

• 2021: Excellent Bachelor's Graduation Thesis, Shanghai Maritime University

• 2021: Excellent Graduate, Shanghai Maritime University

• 2019, 2020, 2021: First Class Scholarship, Shanghai Maritime University

Academic Service

• Reviewer:
  • EMNLP (2023), ARR (Feb 2024, Apr 2024).