Papers by Guoyang Liu
UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models (2026.acl-demo)
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Qundong Shi, Jie Zhou, Biyuan Lin, Junbo Cui, Guoyang Zeng, Yixuan Zhou, Ziyang Wang, Xin Liu, Zhen Luo, Yudong Wang, Zhiyuan Liu
| Challenge: | Existing evaluation frameworks for audio foundation models are heavily reliant on English, making it difficult to objectively assess models’ performance on Chinese. |
| Approach: | They propose a unified framework that supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards. |
| Outcome: | The proposed framework supports 10 languages, 14 task categories, 24 models, and 36 benchmarks with one-command evaluation and real-time leaderboards. |
KnowDR-REC: Auditing Knowledge-Conditioned Visual Grounding in Referring Expression Comprehension (2026.findings-acl)
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| Challenge: | Existing evaluation metrics suggest that Multimodal large language models have acquired fine-grained visual grounding capabilities. |
| Approach: | They propose a benchmark to assess Referring Expression Comprehension (REC) that uses intra-image visual cues to localize target objects and a controllable evaluation mechanism to test sensitivity to fine-grained factual changes. |
| Outcome: | The proposed benchmarks show that multimodal large language models have a high level of performance on the RefCOCO family of benchmarks. |
BMInf: An Efficient Toolkit for Big Model Inference and Tuning (2022.acl-demo)
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Xu Han, Guoyang Zeng, Weilin Zhao, Zhiyuan Liu, Zhengyan Zhang, Jie Zhou, Jun Zhang, Jia Chao, Maosong Sun
| Challenge: | Recent years, pre-trained language models (PLMs) have achieved promising results on various NLP tasks. |
| Approach: | They propose an open-source toolkit for big model inference and tuning which can support big model tuning at extremely low computation cost. |
| Outcome: | The proposed toolkit can support big model inference and tuning at extremely low computation cost. |
OpenAttack: An Open-source Textual Adversarial Attack Toolkit (2021.acl-demo)
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Guoyang Zeng, Fanchao Qi, Qianrui Zhou, Tingji Zhang, Zixian Ma, Bairu Hou, Yuan Zang, Zhiyuan Liu, Maosong Sun
| Challenge: | Various attack models are distinct and implemented with different programming frameworks and settings, which hinders quick utilization and fair comparison of attack models. |
| Approach: | They propose an open-source textual adversarial attack toolkit to solve these issues by combining 15 typical attack models into one toolkit. |
| Outcome: | The proposed toolkit supports all attack types, multilinguality, and parallel processing. |
BMCook: A Task-agnostic Compression Toolkit for Big Models (2022.emnlp-demos)
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Zhengyan Zhang, Baitao Gong, Yingfa Chen, Xu Han, Guoyang Zeng, Weilin Zhao, Yanxu Chen, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing efforts to compress medium-sized models for specific tasks have limited results. |
| Approach: | They propose a task-agnostic compression toolkit for big models that implements quantization, pruning, distillation and MoEfication methods. |
| Outcome: | The proposed tool improves performance on a model with 3 billion parameters by 12x . it also outperforms the original model on three typical NLP benchmarks. |