Papers by Weizhe Yuan
ExplainaBoard: An Explainable Leaderboard for NLP (2021.acl-demo)
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Pengfei Liu, Jinlan Fu, Yang Xiao, Weizhe Yuan, Shuaichen Chang, Junqi Dai, Yixin Liu, Zihuiwen Ye, Graham Neubig
| Challenge: | Using leaderboards, researchers can track the performance of various systems on various NLP tasks. |
| Approach: | They propose a new conceptualization and implementation of NLP evaluation using a leaderboard. |
| Outcome: | The ExplainaBoard is an evaluation tool for natural language processing (NLP) it covers more than 400 systems, 50 datasets, 40 languages, and 12 tasks. |
Meta-Rewarding Language Models: Self-Improving Alignment with LLM-as-a-Meta-Judge (2025.emnlp-main)
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Tianhao Wu, Weizhe Yuan, Olga Golovneva, Jing Xu, Yuandong Tian, Jiantao Jiao, Jason E Weston, Sainbayar Sukhbaatar
| Challenge: | Existing methods for improving large language models have focused on improving model responses rather than judgment capabilities, resulting in rapid saturation during iterative training. |
| Approach: | They propose an iterative Meta-Rewarding step where the model judges its own judgements and uses that feedback to refine its judgment skills. |
| Outcome: | The proposed model improves Llama-3-8B-Instruct from 22.9% to 39.4% on AlpacaEval 2 and 20.6% to 29.1% on Arena-Hard. |
Following Length Constraints in Instructions (2025.emnlp-main)
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| Challenge: | Existing instruction following models fail to follow length constraints in their evaluations. |
| Approach: | They propose to train models that can be controlled at inference time with instructions containing desired length constraints. |
| Outcome: | The proposed models outperform standard instruction following models in length instructed evaluations. |
System-Level Natural Language Feedback (2024.eacl-long)
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| Challenge: | Existing studies on NL feedback focus on instance-level approaches to refine specific examples, but we present a framework for system-level use of NL. |
| Approach: | They propose a framework for system-level use of natural language feedback . they use feedback to formalize system-design decisions in a human-in-the-loop-process . |
| Outcome: | The proposed framework improves search query and dialog response generation and human written instance-level feedback brings further gains over GPT-3.5 written feedback. |
The Critique of Critique (2024.findings-acl)
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| Challenge: | MetaCritique builds specific quantification criteria to evaluate the quality of critique . a systematic method to evaluate critique is lacking. |
| Approach: | They propose a critique of critique, termed MetaCritique, which builds specific quantification criteria and aggregates each AIU's judgment for the overall score. |
| Outcome: | The proposed method can achieve near-human performance across 16 datasets. |
LLMCrit: Teaching Large Language Models to Use Criteria (2024.findings-acl)
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| Challenge: | Current research on using criteria to provide feedback on tasks is limited . a general framework that can be used to teach large language models to use criteria is lacking . |
| Approach: | They propose a framework that enables large language models to use criteria for feedback . criteria are extracted from guidelines and construct in-context demonstrations for each criterion . |
| Outcome: | The proposed framework can be used to provide natural language feedback on tasks. |
DataLab: A Platform for Data Analysis and Intervention (2022.acl-demo)
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Yang Xiao, Jinlan Fu, Weizhe Yuan, Vijay Viswanathan, Zhoumianze Liu, Yixin Liu, Graham Neubig, Pengfei Liu
| Challenge: | Existing tools and research focus on how to interpret and manipulate data, despite its crucial role in machine learning, . existing tools and researchers focus on systems on top of existing data, rather than how to use it. |
| Approach: | They propose a unified data-oriented platform that allows users to interactively analyze the characteristics of data and provides a standard interface for many data processing operations. |
| Outcome: | The proposed platform allows users to analyze the characteristics of data and provides a standardized interface so that many data processing operations can be provided within a single interface. |
Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats (2026.acl-industry)
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Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin, Zhiyuan Yang, Yu Zi Wei, Xin Wang, Mingxuan Yuan, Xianzhi Yu, Zhenhua Dong
| Challenge: | Low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. |
| Approach: | They evaluate HiFloat (HiF8 and HiF4), a family of floating-point formats tailored for Ascend NPUs. |
| Outcome: | The proposed formats excel with high-variance data and are compatible with state-of-the-art quantization frameworks. |
T5Score: Discriminative Fine-tuning of Generative Evaluation Metrics (2023.findings-emnlp)
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| Challenge: | Existing methods for evaluating text quality are discriminative and generative . current methods use manual annotation of human judgements to train them . |
| Approach: | They propose a framework that combines the best of both worlds by using supervised and unsupervised signals from whatever data we have available. |
| Outcome: | The proposed method outperforms existing metrics on 5 datasets, 19 languages and 280 systems. |