Papers by Wen Ma
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences (2021.findings-acl)
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| Challenge: | Recent advances in pretrained language models have shown promising results on commonsense reasoning benchmark datasets. |
| Approach: | They propose a commonsense reasoning benchmark dataset with 4k sentence pairs . they propose 'gamified' model-in-the-loop setup to incentivize challenging samples . |
| Outcome: | The proposed benchmarks show that the proposed model achieves 71% standard accuracy and 51% pairwise accuracy, well below human performance. |
Synergizing Stylometrics with Semantics: Dual-Path Framework for LLM Detection and Attribution (2026.findings-acl)
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| Challenge: | Existing methods for identifying MGTs rely on statistical likelihood or deep embeddings. |
| Approach: | They propose a framework that extracts model-specific stylistic fingerprints across lexical, syntactic, and structural dimensions. |
| Outcome: | The proposed framework achieves a Macro-F1 score of 95.6% on the Wikipedia dataset. |
Improving Preference Alignment of LLM with Inference-Free Self-Refinement (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) develop in-context learning capability through pretraining and instruction tuning. |
| Approach: | Large language models (LLMs) develop in-context learning capability through pretraining and instruction tuning. |
| Outcome: | Experiments show that incorporating IFSR into preference alignment yields performance improvement over 10%. |
Mitigating Posterior Salience Attenuation in Long-Context LLMs with Positional Contrastive Decoding (2025.acl-short)
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| Challenge: | Current solutions incur prohibitive training costs, leaving statistical behaviors and cost-effective approaches underexplored. |
| Approach: | They propose a positional contrast decoding technique that contrasts long-aware attention with designed local-awn attention. |
| Outcome: | The proposed model achieves state-of-the-art performance on long-context benchmarks. |
FAITH: Factuality Alignment through Integrating Trustworthiness and Honestness (2026.findings-acl)
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| Challenge: | Existing approaches to correct factually inaccurate outputs are lacking the semantic richness needed to properly understand its internal states of trustworthiness and honesty. |
| Approach: | They propose a framework for factuality alignment that integrates natural-language uncertainty signals with external knowledge and computes confidence scores and semantic entropy from LLM outputs. |
| Outcome: | Extensive experiments on four knowledge-intensive benchmarks show that FAITH improves the factual accuracy and truthfulness of Large Language Models (LLMs). |
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution (2022.emnlp-main)
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| Challenge: | Existing methods to attack transformer models are not effective at character level, but they are a natural attack scenario. |
| Approach: | They propose a character-level adversarial attack method against transformer models . they use a gradient-based method to find the most vulnerable words in a sentence . |
| Outcome: | The proposed method outperforms previous methods on sentence-level and token-level tasks. |
TrendSim: Simulating Trending Topics in Social Media Under Poisoning Attacks with LLM-based Multi-agent System (2025.findings-naacl)
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Zeyu Zhang, Jianxun Lian, Chen Ma, Yaning Qu, Ye Luo, Lei Wang, Rui Li, Xu Chen, Yankai Lin, Le Wu, Xing Xie, Ji-Rong Wen
| Challenge: | Trending topics bring in a new channel for poisoning attacks, resulting in negative impacts on society. |
| Approach: | They propose an LLM-based multi-agent system to simulate trending topics in social media . they propose a time-aware interaction mechanism, centralized message dissemination, and an interactive system . |
| Outcome: | The proposed system simulates trending topics under poisoning attacks on social media platforms. |
MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learning (2024.emnlp-main)
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Yufei Ma, Zihan Liang, Huangyu Dai, Ben Chen, Dehong Gao, Zhuoran Ran, Wang Zihan, Linbo Jin, Wen Jiang, Guannan Zhang, Xiaoyan Cai, Libin Yang
| Challenge: | Recent advances in open-source Large Language Models (LLMs) have achieved notable successes in natural language processing. |
| Approach: | They propose a Parameter Efficient Fine-Tuning paradigm for improved fine-tuning and parameter efficiency in multi-task learning. |
| Outcome: | The proposed model outperforms existing methods on multi-task learning while reducing training costs by over 80% without losing general capability. |
EventPlus: A Temporal Event Understanding Pipeline (2021.naacl-demos)
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Mingyu Derek Ma, Jiao Sun, Mu Yang, Kung-Hsiang Huang, Nuan Wen, Shikhar Singh, Rujun Han, Nanyun Peng
| Challenge: | Event information is a type of common sense knowledge that helps people understand how stories evolve and provides predictive hints for future events. |
| Approach: | They propose a temporal event understanding pipeline that integrates state-of-the-art components. |
| Outcome: | The proposed pipeline can be easily adapted to other domains, including biomedical domains. |
Unlocking Data-free Low-bit Quantization with Matrix Decomposition for KV Cache Compression (2024.acl-long)
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| Challenge: | Existing methods to compress KV cache compromise precision or require extra data for calibration, limiting their practicality in LLM deployment. |
| Approach: | They propose a low-bit quantization technique based on tensor decomposition to effectively compress KV cache. |
| Outcome: | The proposed method reduces memory footprint and performance by 75% . it is compared with existing methods that compromise precision or require extra data for calibration . |
DR-HM: Distill-then-Reinforce Training with Cognition-Aware Data Synthesis for Harmful Meme Detection (2026.findings-acl)
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Zihan Cheng, Jianxiang Ma, Xiaocui Yang, Peidong Wang, Wen Zhang, Shi Feng, Daling Wang, Yifei Zhang, Mingfu Zhang
| Challenge: | Current methods for harmful meme detection lack the knowledge required to identify such hate . current methods lack the ability to identify cultural stereotypes and visual metaphors . |
| Approach: | They propose a framework that decomposes meme analysis into a human-inspired reasoning process . they propose DR-HM to transfer knowledge from closed-source models while mitigating biases . |
| Outcome: | The proposed framework outperforms existing methods on three benchmark datasets. |
On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations (2024.findings-acl)
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| Challenge: | Existing DocRE models which perform well may make more mistakes when merely changing the entity names in the document, hindering the generalization to novel entity names. |
| Approach: | They propose a pipeline to generate entity-renamed documents by replacing the original entity names with names from Wikidata. |
| Outcome: | The proposed pipeline generates entity-renamed documents by replacing the original entity names with names from Wikidata. |
LongWeave: A Long-Form Generation Benchmark Bridging Real-World Relevance and Verifiability (2025.findings-emnlp)
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Zikai Xiao, Fei Huang, Jianhong Tu, Jianhui Wei, Wen Ma, Yuxuan Zhou, Jian Wu, Bowen Yu, Zuozhu Liu, Junyang Lin
| Challenge: | Existing benchmarks for long-form generation assess real-world queries with hard-to-verify metrics or use synthetic setups that overlook real-life intricacies. |
| Approach: | They propose a new approach that balances verifiable and real-world assessment with Target-Anchored Evaluation. |
| Outcome: | The proposed model balances real-world and verifiable assessment with Target-Anchored Evaluation (TAE) it generates queries, textual materials, and anchors based on verifier targets within real-life scenarios . |
RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction (2023.emnlp-main)
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| Challenge: | Existing methods to identify semantic relations between entities are time-consuming and labor-intensive. |
| Approach: | They propose a relation-aware prototype learning method for document-level relation extraction (FSDLRE) they propose RAPL, which judiciously leverages relation descriptions and real NOTA instances as guidance . |
| Outcome: | The proposed method outperforms state-of-the-art approaches by 2.61% F1 . it generates task-specific NOTA prototypes and refines relation prototypes . |
Ditto: A Simple and Efficient Approach to Improve Sentence Embeddings (2023.emnlp-main)
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Qian Chen, Wen Wang, Qinglin Zhang, Siqi Zheng, Chong Deng, Hai Yu, Jiaqing Liu, Yukun Ma, Chong Zhang
| Challenge: | Prior studies diagnose the anisotropy problem in sentence embeddings from pre-trained language models without fine-tuning. |
| Approach: | They propose an unsupervised method that weights words with model-based importance estimations and computes the weighted average of word representations from pre-trained models as sentence embeddings. |
| Outcome: | Empirical evaluations show that the proposed method can alleviate the anisotropy problem and improve various pre-trained models on the STS benchmarks. |
AMR-based Network for Aspect-based Sentiment Analysis (2023.acl-long)
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| Challenge: | Recent studies have used dependency trees to extract relation between aspects and contexts, but there is a potential mismatch between the dependency tree and sentiment classification as a semantic task. |
| Approach: | They propose to replace the syntactic dependency tree with a semantic structure to capture the relation between an aspect and a context. |
| Outcome: | The proposed model improves ABSA on four public datasets with 1.13% improvement over baselines. |
Counterfactual Inference for Text Classification Debiasing (2021.acl-long)
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| Challenge: | Existing methods to capture unintended dataset biases are expensive and require elaborate balancing strategies. |
| Approach: | They propose a model-agnostic text classification debiasing framework which can effectively avoid employing data manipulations or designing balancing mechanisms. |
| Outcome: | The proposed framework can effectively avoid data manipulations or designing balancing mechanisms to capture unintended dataset biases. |
Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer (2023.findings-acl)
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| Challenge: | Existing approaches to cross-lingual natural language inference lack annotated parallel corpora. |
| Approach: | They propose a new prompt learning framework with the Multilingual Verbalizer for XNLI that uses a multilingual verbalizer to align the representations of original and augmented multilingual questions into a unified semantic space with consistency regularization. |
| Outcome: | The proposed framework outperforms existing methods under few-shot and full-shot cross-lingual transfer settings. |
Implicit Discourse Relation Identification for Open-domain Dialogues (P19-1)
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| Challenge: | Discourse relation identification is a challenging problem in open-domain dialogue systems . previous work relies on formal text but this data is not suitable for informal dialogue . |
| Approach: | They propose a method to automatically extract the implicit discourse relation argument pairs from dialogic turns and a pipeline to identify them. |
| Outcome: | The proposed pipeline extracts argument pairs from dialogic turns and improves it by performing feature ablation and incorporating dialogue features. |
From Informal to Formal – Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofs (2025.acl-long)
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Jialun Cao, Yaojie Lu, Meiziniu Li, Haoyang Ma, Haokun Li, Mengda He, Cheng Wen, Le Sun, Hongyu Zhang, Shengchao Qin, Shing-Chi Cheung, Cong Tian
| Challenge: | Recent studies in formal mathematical reasoning have shown an unstoppable growth trend. |
| Approach: | They constructed 18k high-quality instruction-response pairs across five mainstream formal specification languages and evaluated them against ten open-sourced LLMs. |
| Outcome: | The proposed model compared instruction-response pairs across five formal specification languages and found that the LLMs were good at writing proof segments when given either the code, or the detailed description of proof steps. |
Semi-supervised Relation Extraction via Incremental Meta Self-Training (2021.findings-emnlp)
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| Challenge: | Existing methods suffer from the gradual drift problem, where noisy pseudo labels are incorporated during training. |
| Approach: | They propose a method that uses pseudo labels to assess quality on unlabeled samples . they use a relation label generation network to learn from successful and failed attempts . |
| Outcome: | Experimental results show the proposed method can improve on two public datasets. |
Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition (2021.acl-long)
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| Challenge: | Named entity recognition (NER) is a well-studied task in natural language processing. |
| Approach: | They propose a method that generates span proposals and labels them with categories . they use boundary information of entities and partially matched spans to locate them . |
| Outcome: | The proposed method outperforms state-of-the-art models on nested NER datasets. |
MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers (2026.acl-long)
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Linrui Ma, Chun Hei Lo, Xinyu Wang, Peng Lu, Xihao Yuan, Hanting Chen, Kai Han, Xinghao Chen, Chengjun Zhan, Hanlin xu, Yichun Yin, Lifeng Shang, Feng Wen, Boxing Chen, Yufei Cui
| Challenge: | Existing approaches to improve efficiency often enforce rigid structural constraints such as local attention windows. |
| Approach: | They propose a framework that augments sparse-attention mechanisms with dynamically integrated in-context information through an efficient retrieval system. |
| Outcome: | Empirical results show that MATCH significantly improves the performance of sparse-attention models on synthetic and real-world natural-language tasks. |
Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing (2023.findings-acl)
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| Challenge: | Existing models struggle on the text-to-SQL benchmarks, but we propose a method to improve their generalization ability. |
| Approach: | They propose a method to improve the combinatorial generalization of Text-to-SQL models by aligning previous SQL statements with the input utterance. |
| Outcome: | The proposed method improves the generalization ability of Text-to-SQL models. |