Papers by Jiawen Liu
Towards Efficient NLP: A Standard Evaluation and A Strong Baseline (2022.naacl-main)
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Xiangyang Liu, Tianxiang Sun, Junliang He, Jiawen Wu, Lingling Wu, Xinyu Zhang, Hao Jiang, Zhao Cao, Xuanjing Huang, Xipeng Qiu
| Challenge: | Rather than pursuing the reachless SOTA accuracy, researchers are focusing on model efficiency and usability. |
| Approach: | They propose an evaluation and a public leaderboard for efficient NLP models that depicts the Pareto Frontier for various language understanding tasks. |
| Outcome: | The proposed model outperforms or performs on par with SOTA compressed and early exiting models. |
PersLEARN: Research Training through the Lens of Perspective Cultivation (2023.acl-demo)
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Yu-Zhe Shi, Shiqian Li, Xinyi Niu, Qiao Xu, Jiawen Liu, Yifan Xu, Shiyu Gu, Bingru He, Xinyang Li, Xinyu Zhao, Zijian Zhao, Yidong Lyu, Zhen Li, Sijia Liu, Lin Qiu, Jinhao Ji, Lecheng Ruan, Yuxi Ma, Wenjuan Han, Yixin Zhu
| Challenge: | PersLEARN is a tool designed to facilitate the cultivation of scientific perspectives . junior researchers struggle to identify the perspectives reflected in the literature and struggle to develop their own viewpoints. |
| Approach: | They propose a tool to facilitate the cultivation of scientific perspectives by interacting with a prompt-based model and allowing students to develop their own perspectives explicitly. |
| Outcome: | The proposed tool outperforms baseline approaches across multiple domains of literature from different perspectives. |
Rethinking Efficient Multilingual Text Summarization Meta-Evaluation (2024.findings-acl)
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| Challenge: | a limited number of human annotations are required to evaluate multilingual summarization evaluation metrics. |
| Approach: | They propose a multilingual meta-evaluation framework that uses machine translation systems to transform a monolingual metaevaluations dataset into multilingual versions. |
| Outcome: | The proposed framework outperforms classical text-matching-based metrics in non-English languages. |
Multimodal Pragmatic Jailbreak on Text-to-image Models (2025.acl-long)
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Tong Liu, Zhixin Lai, Jiawen Wang, Gengyuan Zhang, Shuo Chen, Philip Torr, Vera Demberg, Volker Tresp, Jindong Gu
| Challenge: | Existing jailbreaks for diffusion-based text-to-image models generate unsafe content . experimental results show that all tested models suffer from unsafe generation . |
| Approach: | They propose a jailbreak that triggers diffusion-based text-to-image models to generate the image with visual text, resulting in unsafe content. |
| Outcome: | The proposed model generates image with visual text, but the model is unsafe under such jailbreak. |
TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment (2026.acl-long)
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Zhewen Tan, Wenhan Yu, Jianfeng Si, Tongxin Liu, Kaiqi Guan, Huiyan Jin, Jiawen Tao, Xiaokun Yuan, Xiangzheng Zhang, Duohe Ma, Tong Yang, Lin Sun
| Challenge: | Existing approaches to safety alignment of large language models rely on costly manual annotations or human review. |
| Approach: | They propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative collaboration among three roles with near-zero manual annotation. |
| Outcome: | The proposed framework achieves 20%–50% improvement in adversarial effectiveness while preserving high output diversity while achieving 10%–30% gains in safety performance without degrading general reasoning capability. |
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding (2022.coling-1)
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Zhaoye Fei, Yu Tian, Yongkang Wu, Xinyu Zhang, Yutao Zhu, Zheng Liu, Jiawen Wu, Dejiang Kong, Ruofei Lai, Zhao Cao, Zhicheng Dou, Xipeng Qiu
| Challenge: | Existing methods to learn downstream tasks by stitches skill block lack rationality and interpretation. |
| Approach: | They propose a hierarchical framework with a coarse-to-fine paradigm for generalized text representations from the large-scale corpus. |
| Outcome: | The proposed model learns basic language properties from all tasks and boosts performance on relevant tasks. |
Benchmarking Generation and Evaluation Capabilities of Large Language Models for Instruction Controllable Summarization (2024.findings-naacl)
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Yixin Liu, Alexander Fabbri, Jiawen Chen, Yilun Zhao, Simeng Han, Shafiq Joty, Pengfei Liu, Dragomir Radev, Chien-Sheng Wu, Arman Cohan
| Challenge: | Recent studies have found that large language models (LLMs) can achieve state-of-the-art performance on generic summarization benchmarks, but their performance on more complex summarizing task settings is less studied. |
| Approach: | They benchmark large language models on instruction controllable text summarization . they use 4 evaluation protocols and 11 LLMs to evaluate their performance . |
| Outcome: | The proposed model performs well on instruction controllable text summarization tasks with 4 evaluation protocols and 11 LLMs. |
Towards Identifying Social Bias in Dialog Systems: Framework, Dataset, and Benchmark (2022.findings-emnlp)
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Jingyan Zhou, Jiawen Deng, Fei Mi, Yitong Li, Yasheng Wang, Minlie Huang, Xin Jiang, Qun Liu, Helen Meng
| Challenge: | a number of safety concerns hinder the deployment of open-domain dialog systems, such as offensive languages and toxic behaviors, such social bias is difficult to detect. |
| Approach: | They propose a Dial-Bias Framework for analyzing social bias in conversations . they introduce a Chinese social bias dialog dataset and conduct in-depth ablation studies . |
| Outcome: | The proposed framework is the first annotated Chinese social bias dialog dataset . the proposed framework also provides a fine-grained dialog bias measurement benchmark . |
ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models (2024.naacl-long)
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| Challenge: | Low-rank adaptation (LoRA) has demonstrated commendable performance as a popular method . however, it is implemented with a fixed intrinsic rank that might not be ideal for downstream tasks. |
| Approach: | They propose a method that estimates the importance score of each LoRA rank and prunes abundant LoRA ranks to improve performance. |
| Outcome: | The proposed method outperforms baselines on a variety of tasks with comparable parameters. |