Papers by Yangyang Liu
Enhance Robustness of Language Models against Variation Attack through Graph Integration (2024.lrec-main)
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| Challenge: | Pre-trained language models (PLMs) are used in many NLP applications but their vulnerability to adversarial attacks can lead to false or misleading information being distributed. |
| Approach: | They propose a method to incorporate a Chinese character variation graph into pre-trained language models to increase their robustness against character variation attacks in Chinese content. |
| Outcome: | The proposed method outperforms existing language models in combating adversarial attacks in Chinese content. |
From Insight to Action: A Novel Framework for Interpretability-Guided Data Selection in Large Language Models (2026.acl-long)
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Ling Shi, Xinwei Wu, Xiaohu Zhao, Hao Wang, Heng Liu, Yangyang Liu, Linlong Xu, Longyue Wang, Deyi Xiong, Weihua Luo
| Challenge: | Recent research in mechanistic interpretability has revealed that Large Language models contain disentangled, human-understandable components. |
| Approach: | They propose a framework that first identifies causal task features through frequency recall and interventional filtering, then selects “Feature-Resonant Data” that maximally activates task features for fine-tuning. |
| Outcome: | The proposed framework outperforms existing models on mathematical reasoning, summarization, and translation tasks while using only 50% of the data. |
Evolving Knowledge Distillation with Large Language Models and Active Learning (2024.lrec-main)
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| Challenge: | Existing studies have focused on the direct use of large language models for text generation and labeling, without fully exploring their potential to comprehend the target task and acquire valuable knowledge. |
| Approach: | They propose to distill the knowledge of large language models into smaller models by generating annotated data. |
| Outcome: | The proposed method improves the performance of small domain models while enhancing the ability of large language models. |
Detect Camouflaged Spam Content via StoneSkipping: Graph and Text Joint Embedding for Chinese Character Variation Representation (D19-1)
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| Challenge: | Currently, Chinese characters share glyph and phonetic variations to escape detection algorithms due to their complexity and complexity. |
| Approach: | They propose a Chinese variation-enhanced Graph Embedding algorithm that can learn Chinese character embeddings and latent variation families. |
| Outcome: | The proposed model outperforms state-of-the-art models on Chinese spam detection datasets and review datasets. |
Binary and Ternary Natural Language Generation (2023.acl-long)
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| Challenge: | ternary and binary neural networks have proven difficult to optimize since both parameter and output space are discretized . authors demonstrate ternaries and binary models on downstream tasks of summarization and machine translation . |
| Approach: | They propose to use ternary and binary neural networks to optimize for multiplication-free computation . they propose to apply statistics-based quantization for the weights and elastic quantization of the activations to the transformer text generation model. |
| Outcome: | The proposed model outperforms the best existing models on machine translation tasks. |
From Model-centered to Human-Centered: Revision Distance as a Metric for Text Evaluation in LLMs-based Applications (2024.findings-acl)
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| Challenge: | Existing evaluation metrics for large language models yield numerical scores that ignore user experience. |
| Approach: | They propose a metric that suggests revision edits that mimic the human writing process . their results show that the metric offers more insightful feedback and distinguishes between texts . |
| Outcome: | The proposed metric can provide a self-explained text evaluation result in a human-understandable manner beyond the context-independent score. |
Gold Panning in Vocabulary: An Adaptive Method for Vocabulary Expansion of Domain-Specific LLMs (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) struggle when it comes to specialized domains due to limited domain-specific knowledge. |
| Approach: | They propose an adaptive method that automatically identifies valuable words from a given domain vocabulary. |
| Outcome: | The proposed method has been validated on three Chinese datasets and performed on general tasks. |
HSCR: Hierarchical Self-Contrastive Rewarding for Aligning Medical Vision Language Models (2025.acl-long)
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| Challenge: | Existing methods for medical vision-language models overlook modality misalignment . HSCR generates high-quality preference data with higher sampling probability . |
| Approach: | They propose a hierarchical self-contrastive reward approach that addresses two challenges in alignment . they leverage the inherent capability of Med-VLMs to generate dispreferred responses . |
| Outcome: | The proposed approach improves accuracy and trustworthiness of medical vision-label models with 2,000 training entries. |
Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow (2026.findings-acl)
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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. |
M2PO: Multi-Perspective Multi-Pair Preference Optimization for Machine Translation (2026.acl-long)
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Hao Wang, Linlong Xu, Heng Liu, Yangyang Liu, Xiaohu Zhao, Bo Zeng, Liangying Shao, Yichen Dong, Xinwei Wu, Jiang Zhou, Tianyu Dong, Xiangxiang Zeng, Longyue Wang, Weihua Luo
| Challenge: | prevailing methods for machine translation are often hindered by misleading reward signals. |
| Approach: | They propose a framework that aligns large language models to human preferences . they propose 'M2PO' to correct the bias towards partial errors . |
| Outcome: | The proposed framework outperforms open-source models and achieves parity with proprietary models. |
FLAG-TRADER: Fusion LLM-Agent with Gradient-based Reinforcement Learning for Financial Trading (2025.findings-acl)
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Guojun Xiong, Zhiyang Deng, Keyi Wang, Yupeng Cao, Haohang Li, Yangyang Yu, Xueqing Peng, Mingquan Lin, Kaleb E Smith, Xiao-Yang Liu, Jimin Huang, Sophia Ananiadou, Qianqian Xie
| Challenge: | Large language models (LLMs) have impressive reasoning capabilities in financial tasks, but struggle with multi-step, goal-oriented scenarios in interactive financial markets. |
| Approach: | They propose a framework that integrates large language models with gradient-driven reinforcement learning (RL) policy optimization. |
| Outcome: | The proposed framework improves performance in trading and other financial domain tasks. |
Scaling Parameter-Constrained Language Models with Quality Data (2024.emnlp-industry)
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Ernie Chang, Matteo Paltenghi, Yang Li, Pin-Jie Lin, Changsheng Zhao, Patrick Huber, Zechun Liu, Rastislav Rabatin, Yangyang Shi, Vikas Chandra
| Challenge: | Scaling laws in language modeling quantify training loss as a function of dataset size and model parameters, but neglect the critical role of data quality in model generalization. |
| Approach: | They propose to use effective training tokens as a combination of text diversity and syntheticity as measured by a teacher model to calculate scaling laws. |
| Outcome: | The proposed term effective training tokens is a combination of two readily-computed indicators of text diversity and syntheticity as measured by a teacher model. |
UniLR: Unleashing the Power of LLMs on Multiple Legal Tasks with a Unified Legal Retriever (2025.acl-long)
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Ang Li, Yiquan Wu, Yifei Liu, Ming Cai, Lizhi Qing, Shihang Wang, Yangyang Kang, Chengyuan Liu, Fei Wu, Kun Kuang
| Challenge: | Existing retrieval methods are designed for general domains, struggling with legal knowledge, or tailored for specific legal tasks, unable to handle diverse legal knowledge types. |
| Approach: | They propose a novel retrieval method that integrates specialized knowledge into LLMs. |
| Outcome: | The proposed method can perform multiple legal retrieval tasks for LLMs. |
RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction (2023.findings-emnlp)
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Chengyuan Liu, Fubang Zhao, Yangyang Kang, Jingyuan Zhang, Xiang Zhou, Changlong Sun, Kun Kuang, Fei Wu
| Challenge: | Named Entity Recognition (NER) and Relation Extraction (RE) models have limited success when extracting general schemas such as quadruples and quintuples. |
| Approach: | They propose a formal formulation that covers almost all extraction schemas and a Recursive Method with Explicit Schema Instructor for UIE. |
| Outcome: | The proposed method shows strong performance under full-shot and few-shot settings and achieves state-of-the-art results on the tasks of extracting complex schemas. |
Camouflaged Chinese Spam Content Detection with Semi-supervised Generative Active Learning (2020.acl-main)
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| Challenge: | Existing active learning models for text spam detection tasks are based on pool-based active learning, but the annotating process is laborious and time consuming for humans. |
| Approach: | They propose a semi-supervised active learning model to address spam imbalances . they propose masked attention learning approach and character variation graph-enhanced augmentation procedure . |
| Outcome: | The proposed model can improve the performance of existing models for Chinese spam detection task. |
More Than Catastrophic Forgetting: Integrating General Capabilities For Domain-Specific LLMs (2024.emnlp-main)
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Chengyuan Liu, Yangyang Kang, Shihang Wang, Lizhi Qing, Fubang Zhao, Chao Wu, Changlong Sun, Kun Kuang, Fei Wu
| Challenge: | a recent study shows that performance on general tasks decreases after Large Language Models are fine-tuned on domain-specific tasks. |
| Approach: | They propose a general capability integration approach to integrate general capabilities and domain knowledge within a single instance. |
| Outcome: | The proposed method improves performance on domain-specific tasks by integrating general capabilities and domain knowledge. |
LLM-based Medical Assistant Personalization with Short- and Long-Term Memory Coordination (2024.naacl-long)
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| Challenge: | Existing studies have focused on learning and enhancing large language models to understand and generate natural language. |
| Approach: | They propose a computational bionic memory mechanism equipped with a parameter-efficient fine-tuning schema to personalize medical assistants. |
| Outcome: | The proposed method can enhance the response with aware of previous mistakes for new queries during a dialogue session, but the training costs are prohibitive. |
Efficient and Accurate Prompt Optimization: the Benefit of Memory in Exemplar-Guided Reflection (2025.acl-long)
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Cilin Yan, Jingyun Wang, Lin Zhang, Ruihui Zhao, Xiaopu Wu, Kai Xiong, Qingsong Liu, Guoliang Kang, Yangyang Kang
| Challenge: | Recent work utilizes feedbacks generated from erroneous cases to guide prompt optimization . previous methods rely on computational resources and powerful GPUs . |
| Approach: | They propose an automatic prompt engineering method that leverages feedbacks from erroneous cases to guide prompt optimization. |
| Outcome: | The proposed method surpasses state-of-the-art methods with less steps and lower computational resources. |
Can Large Language Models Grasp Legal Theories? Enhance Legal Reasoning with Insights from Multi-Agent Collaboration (2024.findings-emnlp)
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Weikang Yuan, Junjie Cao, Zhuoren Jiang, Yangyang Kang, Jun Lin, Kaisong Song, Tianqianjin Lin, Pengwei Yan, Changlong Sun, Xiaozhong Liu
| Challenge: | Existing studies have found that when LLMs are given criminal facts and legal rules, then asked whether cases constitute a certain charge, they struggle to understand legal theories and perform basic legal reasoning tasks. |
| Approach: | They propose a task to assess LLMs' understanding of legal theories and reasoning capabilities by using a novel framework: Multi-Agent framework for improving complex legal reasoning capability. |
| Outcome: | The proposed framework improves LLMs' understanding of legal theories and reasoning abilities in real-world scenarios. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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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 . |
Target-Aware Language Modeling via Granular Data Sampling (2024.emnlp-main)
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Ernie Chang, Pin-Jie Lin, Yang Li, Changsheng Zhao, Daeil Kim, Rastislav Rabatin, Zechun Liu, Yangyang Shi, Vikas Chandra
| Challenge: | Language model pretraining is the cornerstone of universal language models (LMs), creating generalpurpose representations to excel across a variety of downstream tasks. |
| Approach: | They propose to use multi-granular tokens to sample large-scale language models for domain-specific use cases. |
| Outcome: | The proposed model outperforms random sampled samples on eight benchmarks with 1% of the data and performs on par with the full RefinedWeb data. |
Multi-Turn Dialogue Generation in E-Commerce Platform with the Context of Historical Dialogue (2020.findings-emnlp)
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WeiSheng Zhang, Kaisong Song, Yangyang Kang, Zhongqing Wang, Changlong Sun, Xiaozhong Liu, Shoushan Li, Min Zhang, Luo Si
| Challenge: | Existing research on customer service dialogue generation generates generic responses from sellers . however, such cost prohibits small businesses, and multiturn dialogue generation is becoming more popular. |
| Approach: | They propose a novel and extensible dialogue generation method by leveraging sellers’ historical dialogue information to generate generic seller responses. |
| Outcome: | The proposed model can generate high-quality responses that cater to specific sellers’ characteristics and exhibit consistent superiority over baselines on a real-world multi-turn customer service dialogue dataset. |
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models (2024.findings-acl)
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Zechun Liu, Barlas Oguz, Changsheng Zhao, Ernie Chang, Pierre Stock, Yashar Mehdad, Yangyang Shi, Raghuraman Krishnamoorthi, Vikas Chandra
| Challenge: | Several post-training quantization methods have been shown to perform well down to 8-bits. |
| Approach: | They propose a data-free distillation method that leverages generations produced by the pre-trained model to quantize any generative model independent of its training data. |
| Outcome: | The proposed method outperforms SoTA PTQ and LLaMA models at low bit precision. |
STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion (2023.emnlp-main)
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Xurui Li, Yue Qin, Rui Zhu, Tianqianjin Lin, Yongming Fan, Yangyang Kang, Kaisong Song, Fubang Zhao, Changlong Sun, Haixu Tang, Xiaozhong Liu
| Challenge: | Commercial news provides rich semantics and timely information for automated financial risk detection. |
| Approach: | They propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph to endorse the risk detection enhancement. |
| Outcome: | The proposed model outperforms existing models in terms of generalization and semantics and annotation. |
PDAMeta: Meta-Learning Framework with Progressive Data Augmentation for Few-Shot Text Classification (2024.lrec-main)
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| Challenge: | Existing text data augmentation methods can not ensure the diversity and quality of the generated data, which leads to sub-optimal performance. |
| Approach: | They propose a meta-learning framework with progressive data augmentation for few-shot text classification using prompt-based data augmented by attention-based methods. |
| Outcome: | The proposed framework outperforms state-of-the-art models and shows better robustness on four public few-shot text classification datasets. |
COF: Adaptive Chain of Feedback for Comparative Opinion Quintuple Extraction (2025.coling-main)
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| Challenge: | Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text. |
| Approach: | They propose a model-unaware adaptive chain-of-feedback method to extract quintuples from product review text. |
| Outcome: | The proposed method improves performance on three benchmarks. |
A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis (2021.emnlp-main)
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Jiawei Liu, Kaisong Song, Yangyang Kang, Guoxiu He, Zhuoren Jiang, Changlong Sun, Wei Lu, Xiaozhong Liu
| Challenge: | Recent efforts to predict chatbot failure hatches vital apprehensions due to complexity of human conversation. |
| Approach: | They propose a model that integrates dialogue satisfaction estimation and handoff prediction in one multi-task learning framework. |
| Outcome: | The proposed model integrates dialogue satisfaction estimation and handoff prediction in one multi-task learning framework. |
Sentiment Classification towards Question-Answering with Hierarchical Matching Network (D18-1)
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Chenlin Shen, Changlong Sun, Jingjing Wang, Yangyang Kang, Shoushan Li, Xiaozhong Liu, Luo Si, Min Zhang, Guodong Zhou
| Challenge: | Existing methods to classify QA text contain rich sentiment information. |
| Approach: | They propose a task/method to address QA sentiment analysis by annotating QA text pair with annotation guidelines. |
| Outcome: | The proposed method can learn the matching vectors of each Q-sentence, A-sentent unit. |
Adjacency List Oriented Relational Fact Extraction via Adaptive Multi-task Learning (2021.findings-acl)
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| Challenge: | Existing models for relational fact extraction do not analyze the output data structure from the perspective of graph representation flexibility and heterogeneity. |
| Approach: | They propose a relational fact extraction model based on graph-oriented analytical perspective that outperforms other models. |
| Outcome: | The proposed model outperforms state-of-the-art models on two benchmark datasets and shows that it is flexible and space-efficient. |
Incentivizing Parametric Knowledge via Reinforcement Learning with Verifiable Rewards for Cross-Cultural Entity Translation (2026.acl-long)
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Jiang Zhou, Xiaohu Zhao, Xinwei Wu, Tianyu Dong, Hao Wang, Yangyang Liu, Heng Liu, Linlong Xu, Longyue Wang, Weihua Luo, Deyi Xiong
| Challenge: | Current systems often fall short of this goal in settings where translation hinges on culturally grounded entities such as books, films, places, songs and idioms. |
| Approach: | They propose a framework that anchors supervision on a verifiable, entity-level reward signal and incorporates lightweight structural gates to stabilize optimization. |
| Outcome: | The proposed framework improves on XC-Translate and shows that it can learn a robust reasoning process rather than imitating reference translations. |
SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment (2026.findings-acl)
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Tianyu Dong, Yangyang Liu, Jiang Zhou, Xinwei Wu, Xiaohu Zhao, Hao Wang, Heng Liu, Linlong Xu, Longyue Wang, Weihua Luo, Shaolin Zhu, Deyi Xiong
| Challenge: | Low-resource language tokens are often routed to different experts than those activated by high-resourced inputs, which hinders their efficacy in multilingual contexts. |
| Approach: | They propose a framework to transfer specialized capabilities from high-resource languages as anchors to low-resourced languages by using a symmetric Jensen-Shannon constraint. |
| Outcome: | The proposed framework outperforms standard instruction tuning on 5 low-resource languages and 3 benchmarks. |
Content- and Topology-Aware Representation Learning for Scientific Multi-Literature (2023.emnlp-main)
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| Challenge: | Existing approaches focus on learning textual information at sentence- or document-level, but ignore inter-document connections. |
| Approach: | They propose a model that extends representation learning to the multi-document level . it integrates latent semantic and rich relatedness information from topological networks . |
| Outcome: | The proposed model learns latent semantic information from content and rich relatedness information from topological networks. |
Knowledge Triplets Derivation from Scientific Publications via Dual-Graph Resonance (2024.lrec-main)
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| Challenge: | Existing relation extraction methods aim to extract explicit triplet knowledge from documents, but they can hardly perceive unobserved factual relations. |
| Approach: | They propose a novel Extraction-Contextualization-Derivation strategy to generate a document-specific dynamic graph from a shared static knowledge graph. |
| Outcome: | The proposed method can generate richer explicit and implicit relations under the guidance of static and dynamic knowledge topologies. |