Papers by Mingyang Liu
Repairing Catastrophic-Neglect in Text-to-Image Diffusion Models via Attention-Guided Feature Enhancement (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Text-to-Image Diffusion models generate high-quality images from textual descriptions, but they often produce images that do not fully align with the input prompts, resulting in semantic inconsistencies. |
| Approach: | They propose an automated repair approach to address catastrophic-neglect in T2I DMs. |
| Outcome: | The proposed model achieves 10.1%-16.3% higher Correct Rate in image generation compared to baselines. |
Lost in Multilinguality: Dissecting Cross-lingual Factual Inconsistency in Transformer Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Multilingual language models store factual knowledge across languages but struggle to provide consistent responses to semantically equivalent prompts in different languages. |
| Approach: | They propose a linear shortcut method that bypasses computations in the final layers . this method improves accuracy and cross-lingual consistency . |
| Outcome: | The proposed method improves prediction accuracy and cross-lingual consistency. |
Noisy Multi-Label Text Classification via Instance-Label Pair Correction (2024.findings-naacl)
Copied to clipboard
| Challenge: | Noise is a significant challenge for machine learning models, especially deep learning models. |
| Approach: | They propose a holistic selection metric that identifies noisy pairs while considering global loss information and instance-specific ranking information. |
| Outcome: | The proposed approach significantly improves performance in noisy multi-label text classification tasks. |
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)
Copied to clipboard
Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen
| Challenge: | Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval. |
| Approach: | They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs). |
| Outcome: | The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models. |
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026.findings-acl)
Copied to clipboard
Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, Ngai Wong
| Challenge: | Existing literature on mechanistic interpretation (MI) treats it as an observational science, leaving practical applications underexplored. |
| Approach: | They propose a survey structured around the pipeline to identify and improve MI models. |
| Outcome: | The proposed framework enables tangible improvements in Alignment, Capability, and Efficiency. |
Mitigating Over-Generation for Unsupervised Keyphrase Extraction with Heterogeneous Centrality Detection (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing keyphrase extraction models incorrectly determine a keyphrase as a phrase but output other candidates as keyphrases because they contain the same word. |
| Approach: | They propose a new approach that detects both implicit and explicit centrality within a heterogeneous graph as the importance score of each candidate keyphrase. |
| Outcome: | The proposed approach outperforms state-of-the-art keyphrase extraction models on three benchmark datasets. |
How Transliterations Improve Crosslingual Alignment (2025.coling-main)
Copied to clipboard
Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Ayyoob ImaniGooghari, Orgest Xhelili, Haotian Ye, Chunlan Ma, François Yvon, Hinrich Schütze
| Challenge: | Recent studies show that post-aligning multilingual pretrained language models improve crosslingual alignment, but it is unclear how and why this is achieved. |
| Approach: | They propose to explicitly evaluate crosslingual alignment by adding transliterations to models using original and transliterated data. |
| Outcome: | The proposed approach improves crosslingual alignment even for random sentences. |
Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge (2022.emnlp-main)
Copied to clipboard
Longxu Dou, Yan Gao, Xuqi Liu, Mingyang Pan, Dingzirui Wang, Wanxiang Che, Dechen Zhan, Min-Yen Kan, Jian-Guang Lou
| Challenge: | Existing approaches to text-to-SQL require domain knowledge to parse expert questions into SQL queries. |
| Approach: | They propose a framework to leverage domain knowledge during parsing by building a new benchmark KnowSQL with domain-specific questions. |
| Outcome: | The proposed framework improves the performance of the proposed benchmark by 28.2%. |
Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing jailbreak attacks primarily utilize scenario camouflage techniques, however their explicit mention of malicious intent will be easily recognized and defended by LLMs. |
| Approach: | They propose an indirect jailbreak attack approach, Puzzler, which can bypass LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query. |
| Outcome: | The proposed approach can bypass the LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query. |
OFA: A Framework of Initializing Unseen Subword Embeddings for Efficient Large-scale Multilingual Continued Pretraining (2024.findings-naacl)
Copied to clipboard
| Challenge: | Existing methods to pretrain multilingual models are limited by the number of embedding parameters and the complexity of the model. |
| Approach: | They propose a framework that initializes the embeddings of unseen subwords and can adapt a model to multiple languages. |
| Outcome: | The proposed framework can adapt a pre-trained model to multiple languages efficiently and effectively. |
SAD: A Large-Scale Strategic Argumentative Dialogue Dataset (2026.acl-long)
Copied to clipboard
YongKang Liu, Jiayang Yu, Mingyang Wang, Yiqun Zhang, Ercong Nie, Shi Feng, Daling Wang, Kaisong Song, Hinrich Schuetze
| Challenge: | Argumentation is a key part of human reasoning and decision-making . existing argumentative corpora focus on single-turn settings, but multi-turn dialogues are often realized as multi-turned dialogues . |
| Approach: | They present a dataset for strategic multi-turn argumentation dialogues . they annotate each utterance with five strategy types, allowing multiple strategies per utterrance . |
| Outcome: | The proposed dataset shows that explicit prompting improves fluency, stylistic coherence and persuasiveness. |
PLAN-TUNING: Post-Training Language Models to Learn Step-by-Step Planning for Complex Problem Solving (2025.emnlp-main)
Copied to clipboard
Mihir Parmar, Palash Goyal, Xin Liu, Yiwen Song, Mingyang Ling, Chitta Baral, Hamid Palangi, Tomas Pfister
| Challenge: | Recent studies have shown that decomposing complex problems into simple subtasks has significantly boosted the performance of large language models (LLMs). |
| Approach: | They propose a unified post-training framework that distills synthetic task decompositions and fine-tunes smaller LLMs via supervised and reinforcement-learning objectives to improve complex reasoning. |
| Outcome: | The proposed framework outperforms strong baselines on GSM8k and MATH benchmarks and shows that it can improve generalization capabilities on out-of-domain datasets. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
Copied to clipboard
Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
Unsupervised Keyphrase Extraction by Learning Neural Keyphrase Set Function (2023.findings-acl)
Copied to clipboard
| Challenge: | Unsupervised keyphrase extraction is a task of extracting a keyphrase set that provides readers with highlevel information about the key ideas or important topics described in the document. |
| Approach: | They propose an unsupervised keyphrase extraction task that is a document-set matching problem instead of modeling the relevance between an individual phrase and the document. |
| Outcome: | The proposed model outperforms the state-of-the-art unsupervised keyphrase extraction baselines by a large margin. |
Beyond Completion: A Foundation Model for General Knowledge Graph Reasoning (2025.findings-acl)
Copied to clipboard
Yin Hua, Zhiqiang Liu, Mingyang Chen, Zheng Fang, Chi Man Wong, Lingxiao Li, Chi Man Vong, Huajun Chen, Wen Zhang
| Challenge: | Existing foundation models for general knowledge graph reasoning have focused on their structural aspects, with most efforts restricted to in-KG tasks. |
| Approach: | They propose a conditional encoding architecture that bridges the gap between textual and structural modalities, enabling seamless integration. |
| Outcome: | The proposed model outperforms baseline models on 28 datasets and is generalized to out-of-KG tasks. |
On Relation-Specific Neurons in Large Language Models (2025.emnlp-main)
Copied to clipboard
Yihong Liu, Runsheng Chen, Lea Hirlimann, Ahmad Dawar Hakimi, Mingyang Wang, Amir Hossein Kargaran, Sascha Rothe, François Yvon, Hinrich Schuetze
| Challenge: | In large language models, certain neurons can store distinct pieces of knowledge learned during pretraining. |
| Approach: | They hypothesize that relation-specific neurons detect relation in input text and guide generation involving such a relation. |
| Outcome: | The proposed model can handle facts involving relation r and facts containing a different relation . |
HyperRank: Hyperbolic Ranking Model for Unsupervised Keyphrase Extraction (2023.emnlp-main)
Copied to clipboard
| Challenge: | Existing unsupervised keyphrase extraction models overlook latent hierarchical structures when extracting keyphrases. |
| Approach: | They propose a new ranking model that models global and local contexts to estimate the importance of each candidate keyphrase within the hyperbolic space. |
| Outcome: | The proposed model outperforms state-of-the-art models in keyphrase extraction tasks. |
LangSAMP: Language-Script Aware Multilingual Pretraining (2025.acl-long)
Copied to clipboard
| Challenge: | Recent multilingual pretrained language models often avoid using language embeddings, which places a significant burden on token representations to encode all language-specific information. |
| Approach: | They propose a method that incorporates both language and script embeddings into the output of Transformer blocks before passing the final representations to the language modeling head for prediction. |
| Outcome: | The proposed method outperforms the baseline model in zero-shot crosslingual transfer across diverse downstream tasks. |
One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systems (2025.findings-emnlp)
Copied to clipboard
Zhiyuan Chang, Mingyang Li, Xiaojun Jia, Junjie Wang, Yuekai Huang, Ziyou Jiang, Yang Liu, Qing Wang
| Challenge: | Existing knowledge poisoning attacks against RAG systems require multiple poisoned documents or can only function effectively on simplistic queries. |
| Approach: | They propose a more realistic knowledge poisoning attack that poisons only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements. |
| Outcome: | The proposed attack achieves success by poisoning only a single document while remaining effective for complex multi-hop questions involving complex relationships between multiple elements. |
Tracing Multilingual Factual Knowledge Acquisition in Pretraining (2025.findings-emnlp)
Copied to clipboard
Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Felicia Körner, Ercong Nie, Barbara Plank, François Yvon, Hinrich Schuetze
| Challenge: | Large Language Models are capable of recalling multilingual factual knowledge, but most studies evaluate only the final model, leaving the development of factual recall and crosslingual consistency unexplored. |
| Approach: | They trace how factual recall and crosslingual consistency evolve during pretraining, focusing on OLMo-7B as a case study. |
| Outcome: | The results show that fact frequency is the key to a better recall of multilingual facts, regardless of language, and some low-frequency facts in non-English languages can still be correctly recalled. |
Knowledge Graph Pooling and Unpooling for Concept Abstraction (2025.coling-main)
Copied to clipboard
| Challenge: | Knowledge graph embedding (KGE) aims to embed entities and relations as vectors in a continuous space. |
| Approach: | They propose a framework with KG Pooling and unpooling and Contrastive Learning to abstract and encode latent concepts for better KG prediction. |
| Outcome: | The proposed framework outperforms baselines on link prediction task. |
Improving Embedding-based Unsupervised Keyphrase Extraction by Incorporating Structural Information (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing unsupervised keyphrase extraction models ignore the indicative role of the highlights in certain locations, leading to wrong keyphrases extraction. |
| Approach: | They propose a Highlight-Guided Unsupervised Keyphrase Extraction model that models phrase-document relevance via the highlights of documents and calculates cross-phrase relevance between all candidate phrases. |
| Outcome: | The proposed model outperforms the state-of-the-art unsupervised keyphrase extraction models on three benchmarks. |
PlaM: Training-Free Plateau-Guided Model Merging for Better Visual Grounding in MLLMs (2026.findings-acl)
Copied to clipboard
Zijing Wang, YongKang Liu, Mingyang Wang, Ercong Nie, Deyuan Chen, Zhengjie Zhao, Shi Feng, Daling Wang, Xiaocui Yang, Yifei Zhang, Hinrich Schuetze
| Challenge: | Multimodal instruction fine-tuning degrades textual reasoning capability, undermining multimodal performance. |
| Approach: | They propose a plateau-guided model merging method that selectively injects base language model parameters into MLLMs to mitigate this degradation. |
| Outcome: | The proposed framework reduces multimodal instruction fine-tuning degradation by incorporating a plateau-guided model merging method into MLLMs. |
Croppable Knowledge Graph Embedding (2025.acl-long)
Copied to clipboard
| Challenge: | Knowledge Graph Embedding (KGE) is a common approach for Knowledge Grasse (KGs) in AI tasks. |
| Approach: | They propose a new KGE training framework MED that allows one training to obtain a croppable KGE model for multiple scenarios with different dimensional needs. |
| Outcome: | The proposed framework improves low-dimensional sub-models and makes high-dimensional models retain the low-dimension sub-modells’ capacity. |
On Temperature-Constrained Non-Deterministic Machine Translation: Potential and Evaluation (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent studies have focused on the non-deterministic properties of language models, but these properties remain under-explored in machine translation. |
| Approach: | They propose a method that evaluates MT systems and identifies temperature-constrained non-deterministic MT as a distinct phenomenon. |
| Outcome: | The proposed framework provides higher-quality candidates than Deterministic MT under temperature constraints. |