Papers by Yuting Liu
Rewrite to Jailbreak: Discover Learnable and Transferable Implicit Harmfulness Instruction (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing jailbreak methods create a forced instruction-following scenario, or search adversarial prompts with prefix or suffix tokens to achieve a specific representation manually or automatically. |
| Approach: | They propose a method that rewrites the original instruction to achieve a jailbreak . they propose rewriting the original instructions to improve the attack strategy . |
| Outcome: | The proposed method is more efficient and easier to identify since no additional features are introduced. |
Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges (2025.acl-long)
Copied to clipboard
Bolei Ma, Yuting Li, Wei Zhou, Ziwei Gong, Yang Janet Liu, Katja Jasinskaja, Annemarie Friedrich, Julia Hirschberg, Frauke Kreuter, Barbara Plank
| Challenge: | linguistics studies how context influences meaning of language and how people use it to convey implied meanings, emotions, and intentions. |
| Approach: | They analyze task designs, data collection methods, evaluation approaches and their relevance to real-world applications. |
| Outcome: | The findings highlight emerging trends, challenges, and gaps in existing benchmarks . the findings will contribute to more nuanced and context-aware NLP models . |
Personalized Text Generation with Contrastive Activation Steering (2025.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to personalized text generation rely on retrieval-augmented generation and parameter-efficient fine-tuning. |
| Approach: | They propose a training-free framework that disentangles and represents personalized writing style as a vector in LLM’s activation-space. |
| Outcome: | The proposed framework achieves 8% relative improvement in personalized generation while reducing storage requirements by 1700 over PEFT method. |
AC-EVAL: Evaluating Ancient Chinese Language Understanding in Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | AC-EVAL is a benchmark designed to assess the advanced knowledge and reasoning capabilities of LLMs within the context of ancient Chinese. |
| Approach: | They propose a benchmark to assess the advanced knowledge and reasoning capabilities of LLMs in ancient Chinese. |
| Outcome: | AC-EVAL aims to assess the comprehension of ancient Chinese texts . the benchmark covers 13 tasks covering historical facts, geography, social customs, art, philosophy, classical poetry and prose. |
Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow (2026.findings-acl)
Copied to clipboard
Yangyang Zhong, Yanmei Gu, Zhengqing Zang, Xiaomeng Li, Yuqi Ding, Xibei Jia, Yuting Shen, Zhenzhong Lan, Liwang Zhu, Weiping Liu, Junlin Zhou, Haisheng Liu, Zhong Xin Yu, Pengxin Luo, Donglian Qi, Yunfeng Yan, Junbo Zhao
| Challenge: | Autoregressive (AR) language models dominate modern natural language processing due to strong likelihood-based training objectives and reliable left-to-right decoding. |
| Approach: | They characterize MDLM behavior along two dimensions: parallelism strength and generation order . authors propose a Generate-then-Edit paradigm that mitigates dependency loss . |
| Outcome: | The proposed model improves on tasks that require "backward information" the Generate-then-Edit paradigm improves parallel decoding efficiency while reducing dependency loss. |
DCP: Dual-Cue Pruning for Efficient Large Vision-Language Models (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing pruning methods for large vision language models use visual tokens to prune . existing methods fail to balance efficiency and semantic alignment due to large number of visual token. |
| Approach: | They propose a cross-modal pruning framework that considers textual semantics and visual self-attention to combine them to achieve efficient inference acceleration. |
| Outcome: | The proposed pruning framework can retain only 25% of the visual tokens, with a minimal performance degradation of only 0.063% on LLaVA-1.5-13B. |
Everything Has a Cause: Leveraging Causal Inference in Legal Text Analysis (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing studies focus on analyzing structured data, while mining causal relationship among factors from unstructured data is of great importance. |
| Approach: | They propose a graph-based causal inference framework which builds causal graphs from fact descriptions without much human involvement. |
| Outcome: | The proposed framework can capture nuance from fact descriptions among confusing charges and provide explainable discrimination in few-shot settings. |
Neighborhood Matching Network for Entity Alignment (2020.acl-main)
Copied to clipboard
| Challenge: | Structural heterogeneity between knowledge graphs is an outstanding challenge for entity alignment. |
| Approach: | They propose a framework for entity alignment that uses a neighborhood matching module to combine neighborhood differences. |
| Outcome: | The proposed framework outperforms existing methods on three datasets. |
S2-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency (2025.naacl-long)
Copied to clipboard
Yuting Zeng, Weizhe Huang, Lei Jiang, Tongxuan Liu, XiTai Jin, Chen Tianying Tiana, Jing Li, Xiaohua Xu
| Challenge: | Large language models exhibit limitations when handling complex mathematical reasoning and logical inference tasks. |
| Approach: | They propose a sparsification strategy to reduce token costs within Multi-agent Debate (MAD) this strategy minimizes ineffective exchanges of information and unproductive discussions among agents . |
| Outcome: | The proposed approach reduces token costs by up to 94.5% while maintaining performance degradation below 2.0%. |
H-MAS: Hierarchical Multi-Agent Scheduling for Multi-Tenant LLM Serving (2026.findings-acl)
Copied to clipboard
Yuhan Liu, Cong Xu, Qi Jia, Yihua Wang, Feiyu Chen, Liang Jin, Lu Liu, Yaqian Zhao, Yuting Ding, Xiang Li
| Challenge: | Multi-tenant Model-as-a-Service (MaaS) workloads exhibit non-stationarity across multiple time scales . existing request schedulers often rely on a fixed policy that remains unchanged at runtime . |
| Approach: | They propose a hierarchical multi-agent scheduler that operates in a layered closed loop . they propose to maintain 1.2–3.0 higher Goodput than SGLang and vLLM . |
| Outcome: | Experiments show that H-MAS achieves 1.2–3.0 higher Goodput than SGLang and vLLM . it maintains more stable QoS under diverse request lengths and heterogeneous SLO targets . |
Stealthy Attack on Large Language Model based Recommendation (2024.acl-long)
Copied to clipboard
| Challenge: | Recent advances in recommender systems have been overlooked due to their emphasis on textual content. |
| Approach: | They propose to introduce large language models into recommendation models to exploit the semantic understanding and strong transferability of LLMs. |
| Outcome: | The proposed approach significantly boosts an item’s exposure by altering its textual content during the testing phase, without requiring direct interference with the model’s training process. |
Jointly Learning Entity and Relation Representations for Entity Alignment (D19-1)
Copied to clipboard
| Challenge: | Entity alignment is a viable method for integrating heterogeneous knowledge among different knowledge graphs (KGs). |
| Approach: | They propose a Graph Convolutional Network-based framework for learning relation representations by embedding relation seeds into entities and incorporating relation approximation into entities to iteratively improve alignment. |
| Outcome: | The proposed approach outperforms state-of-the-art methods on three real-world cross-lingual datasets. |
"I Don’t Know What to Say": A Fact-Filling Questionnaire Method to Help Non-Experts Talk to LegalAI Assistant (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent advances in large language models (LLMs) have greatly expanded the scope of legal AI. |
| Approach: | They propose a method that generates questionnaires to help users refine queries . they leverage an iterative training process that collects valuable questionnaires . |
| Outcome: | The proposed method improves the completeness of queries and ensures the performance of domain-specific models in downstream legal tasks. |
Logic-of-Thought: Injecting Logic into Contexts for Full Reasoning in Large Language Models (2025.naacl-long)
Copied to clipboard
Tongxuan Liu, Wenjiang Xu, Weizhe Huang, Yuting Zeng, Jiaxing Wang, Xingyu Wang, Hailong Yang, Jing Li
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks but their performance in complex logical reasoning tasks remains unsatisfactory. |
| Approach: | They propose a propositional logic prompting method which generates expanded logical information descriptions and utilizes them as an additional augmentation to original contexts. |
| Outcome: | Extensive experiments show that Logic-of-Thought boosts the performance of various prompting methods with a striking margin across five logical reasoning tasks. |