Papers by Ren Ma
KESA: A Knowledge Enhanced Approach To Sentiment Analysis (2022.aacl-main)
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| Challenge: | Recent work on injecting sentiment knowledge into pre-trained language models, but it is difficult to integrate external knowledge into PLMs. |
| Approach: | They propose two sentiment-aware auxiliary tasks to integrate sentiment knowledge into the objective of the downstream task. |
| Outcome: | The proposed tasks outperform baselines and complement existing sentiment-enhanced models. |
Meta-rater: A Multi-dimensional Data Selection Method for Pre-training Language Models (2025.acl-long)
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Xinlin Zhuang, Jiahui Peng, Ren Ma, Yinfan Wang, Tianyi Bai, Xingjian Wei, Qiu Jiantao, Chi Zhang, Ying Qian, Conghui He
| Challenge: | composition of pre-training datasets for large language models remains undisclosed . current methods for evaluating data quality are limited by single-dimensional evaluation or redundancy-focused strategies. |
| Approach: | They propose a multi-dimensional data selection method that integrates dimensions with existing quality metrics through learned optimal weightings. |
| Outcome: | The proposed method doubles convergence speed for 1.3B model models and improves downstream task performance by 3.23%. |
Reinforced Efficient Reasoning via Semantically Diverse Exploration (2026.acl-long)
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Ziqi Zhao, Zhaochun Ren, Jiahong Zou, Liu Yang, Zhiwei Xu, Xuri Ge, Zhumin Chen, Xinyu Ma, Daiting Shi, Shuaiqiang Wang, Dawei Yin, Xin Xin
| Challenge: | Existing methods for reinforcement learning with verifiable rewards suffer from limited exploration diversity and inefficient reasoning. |
| Approach: | They propose a method that rewards concise and correct reasoning while penalizing unnecessarily long reasoning chains. |
| Outcome: | Extensive experiments on Qwen and Llama models validate the effectiveness and efficiency of ROSE. |
Beyond "I Don’t Know": Evaluating LLM Self-Awareness in Discriminating Data and Model Uncertainty (2026.acl-long)
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| Challenge: | Prior studies treat refusal as a generic "I don't know" lack of distinction limits downstream action decisions like requesting clarification or invoking external tools. |
| Approach: | They propose a benchmark to evaluate explicit uncertainty attribution in large language models. |
| Outcome: | The proposed method improves uncertainty attribution while preserving answer accuracy. |
A Retrieve-and-Rewrite Initialization Method for Unsupervised Machine Translation (2020.acl-main)
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| Challenge: | Recent work shows successful methods for unsupervised machine translation (UMT) initialization stage is important since bad initialization may wrongly squeeze the search space and too much noise may hurt the final performance. |
| Approach: | They propose a retrieval and rewriting based method to better initialize unsupervised translation models. |
| Outcome: | The proposed method improves translation performance by over 4 BLEU scores. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
MINED: Probing and Updating with Multimodal Time-Sensitive Knowledge for Large Multimodal Models (2026.findings-acl)
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Kailin Jiang, Ning Jiang, Yuntao Du, Yuchen Ren, Yuchen Li, Yifan Gao, Jinhe Bi, Yunpu Ma, Bin Li, Lei Liu, Qing Li
| Challenge: | Existing benchmarks for Large Multimodal Models (LMMs) are constrained by static representations, inadequately evaluating their ability to understand time-sensitive knowledge. |
| Approach: | They propose a benchmark containing 2,104 time-sensitive knowledge samples spanning six knowledge types to evaluate temporal awareness along 6 key dimensions and 11 challenging tasks. |
| Outcome: | The proposed benchmark measures temporal awareness along 6 key dimensions and 11 tasks, while most open-source LMMs still lack time understanding ability. |
SheetDesigner: MLLM-Powered Spreadsheet Layout Generation with Rule-Based and Vision-Based Reflection (2025.emnlp-main)
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| Challenge: | Existing automated layout models are ill-suited for spreadsheets, authors say . existing layout models treat components as rectangles with continuous coordinates . authors: spreadsheets are powerful tools for organizing and analyzing data . |
| Approach: | They formalize a spreadsheet layout generation task and introduce a framework for spreadsheet layouts . they use multimodal large language models to combine rule and vision reflection . |
| Outcome: | The proposed framework outperforms baselines in a spreadsheet layout generation task by 22.6%. |
Deconvolution-Based Global Decoding for Neural Machine Translation (C18-1)
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| Challenge: | Existing models for Neural Machine Translation (NMT) use Recurrent Neural Network (RNN) to generate translation word by word following a sequential order. |
| Approach: | They propose a Neural Machine Translation (NMT) model that decodes the sequence with the guidance of its structural prediction of the target-side context. |
| Outcome: | The proposed model is more competitive compared with the state-of-the-art methods and reduces repetition with the instruction from the target-side context for decoding. |
Unlocking Multilingual Reasoning Capability of LLMs and LVLMs through Representation Engineering (2026.acl-long)
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Qiming Li, Xiaocheng Feng, Yixuan Ma, Ruihan Chen, Zihe Tong, Zekai Ye, Xiachong Feng, Libo Qin, Haoyu Ren, Kun Chen, Yunfei Lu, Dandan Tu, Bing Qin
| Challenge: | Existing approaches to enhance multilingual reasoning capabilities rely on costly multilingual training or employ prompting with external translation tools. |
| Approach: | They propose a training-free inference-time method to enhance multilingual reasoning capabilities via Representation Engineering without additional training data or tools. |
| Outcome: | The proposed method outperforms existing methods on four reasoning benchmarks in English and Thai and Swahili. |
Structural Bias for Aspect Sentiment Triplet Extraction (2022.coling-1)
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| Challenge: | Existing structural bias adapters for aspect sentiment triplet extraction are under-confident . a large-scale dataset for ASTE shows the adapter is effective and efficient to a larger scale. |
| Approach: | They propose to use a structural adapter to integrate structural bias into pretrained language models . they propose to add a relative position structure in place of the syntactic dependency structure . |
| Outcome: | The proposed adapter achieves state-of-the-art performance over strong baselines, but with a light parameter demand and low latency. |
STORM-BORN: A Challenging Mathematical Derivations Dataset Curated via a Human-in-the-Loop Multi-Agent Framework (2025.findings-acl)
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Wenhao Liu, Zhenyi Lu, Xinyu Hu, Jerry Zhang, Dailin Li, Jiacheng Cen, Huilin Cao, Haiteng Wang, Yuhan Li, Xie Kun, Dandan Li, Pei Zhang, Chengbo Zhang, Yuxiang Ren, Xiaohong Huang, Yan Ma
| Challenge: | Existing datasets suffer from outdated and insufficient challenging content, neglecting human-like reasoning, and limited reliability due to single-LLM generation. |
| Approach: | They propose a human-in-the-loop, multi-agent data generation framework that integrates reasoning-dense filters, multiagent collaboration, and human mathematicians’ evaluations to ensure the reliability and quality of the dataset. |
| Outcome: | The proposed framework improves accuracy and quality of the 2,000-synthesized datasets by integrating reasoning-dense filters, multi-agent collaboration, and human mathematicians’ evaluations. |
Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents (2023.emnlp-main)
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Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, Zhaochun Ren
| Challenge: | Existing work utilizes generative LLMs for Information Retrieval (IR) rather than direct passage ranking. |
| Approach: | They investigate generative LLMs such as ChatGPT and GPT-4 for relevance ranking in IR and use a test set to verify the model’s ability to rank unknown knowledge. |
| Outcome: | The proposed model outperforms a 3B supervised model on the BEIR benchmark. |
Social Welfare Function Leaderboard: On the Emergence of LLM Agents as the Welfare Dictator (2026.findings-acl)
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Zhengliang Shi, Ruotian Ma, Jen-tse Huang, Xinbei Ma, Xingyu Chen, Mengru Wang, Qu Yang, Yue Wang, Fanghua Ye, Ziyang Chen, Shanyi Wang, Cixing LI, Wenxuan Wang, Zhaopeng Tu, Xiaolong Li, Zhaochun Ren, Liefeng Bo
| Challenge: | Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare. |
| Approach: | They evaluate 20 state-of-the-art Large language models (LLMs) and 20 LLM dictators to create a social welfare function benchmark. |
| Outcome: | The proposed model creates dilemma between maximizing collective efficiency and ensuring distributive fairness. |
Cross-Domain Audio Deepfake Detection: Dataset and Analysis (2024.emnlp-main)
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| Challenge: | Existing audio deepfake detection datasets are outdated and lack generalization capabilities. |
| Approach: | They construct a new cross-domain audio deepfake detection dataset comprising over 300 hours of speech data that is generated by five advanced zero-shot TTS models. |
| Outcome: | The proposed models achieve 4.1% and 6.5% error rates in the cross-domain ADD dataset generated by five advanced zero-shot TTS models. |
Not All Tokens Matter: Towards Efficient LLM Reasoning via Token Significance in Reinforcement Learning (2026.acl-long)
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| Challenge: | Large language models (LLMs) often produce unnecessarily long explanations that reduce efficiency. |
| Approach: | They propose a length-aware reward that selectively penalizes insignificance tokens . they also propose 'dynamic length control' that encourages more detailed reasoning . |
| Outcome: | The proposed method reduces response length while maintaining correctness, the authors show . it selectively penalizes insignificance tokens while maintaining accuracy . |
Explicit Cross-lingual Pre-training for Unsupervised Machine Translation (D19-1)
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| Challenge: | Existing approaches to build initial unsupervised machine translation models with cross-lingual n-gram embeddings are inexplicit and limited. |
| Approach: | They propose a cross-lingual pre-training method that incorporates cross-linguistic training signals into pre-trained models by randomly choosing source n-grams in the input text stream. |
| Outcome: | The proposed method significantly improves the performance of unsupervised machine translation. |
Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation (2023.acl-long)
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| Challenge: | Recent studies show the effectiveness of retrieval augmentation in many generative NLP tasks. |
| Approach: | They investigate retrieval settings from the input and label distribution views . they further augment document-level EAE with pseudo demonstrations sampled from event semantic regions . |
| Outcome: | The proposed methods can augment document-level EAE with pseudo demonstrations . the methods can be used in generative NLP tasks such as dialogue response generation . |
Large Language Models for Predictive Analysis: How Far Are They? (2025.findings-acl)
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| Challenge: | Existing studies on LLMs do not evaluate their capability in predictive analysis. |
| Approach: | They propose a benchmark to evaluate Large Language Models (LLMs) they integrate 1130 queries from 44 real-world datasets of 8 different fields to evaluate their capability . |
| Outcome: | The proposed benchmark evaluates 12 renowned LLMs from 44 real-world datasets . results offer insights into their practical use in predictive analysis . |
XPrompt: Exploring the Extreme of Prompt Tuning (2022.emnlp-main)
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| Challenge: | Prompt tuning learns soft prompts to condition pre-trained Language Models for performing downstream tasks in a parameter-efficient manner. |
| Approach: | They propose a Prompt tuning model with an eXtremely small scale that learns soft prompts to condition the frozen Pre-trained Language Models for performing downstream tasks in a parameter-efficient manner. |
| Outcome: | The proposed model outperforms the vanilla Prompt-Tuning and can significantly improve across tasks and model scales. |
A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction (2020.acl-main)
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| Challenge: | Recent methods for bilingual lexicon induction are based on unsupervised cross-lingual word embeddings . previous methods only use word-level information, which is limited and inaccurate . |
| Approach: | They propose a graph-based approach to induce bilingual lexicons in a coarse-to-fine way . they use word cliques from graphs and aligned clique-level words to find initial translation solution . |
| Outcome: | The proposed method improves the performance of bilingual lexicon induction compared with previous methods. |
UR2 : Unify RAG and Reasoning through Reinforcement Learning (2026.acl-long)
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| Challenge: | Existing attempts to unify large language models are limited to open-domain QA with fixed retrieval settings. |
| Approach: | They propose a general reinforcement learning framework that dynamically coordinates retrieval and reasoning. |
| Outcome: | The proposed framework outperforms existing paradigms on open-domain QA, MMLU-Pro, medical, and mathematical reasoning tasks. |
XRec: Large Language Models for Explainable Recommendation (2024.findings-emnlp)
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| Challenge: | Collaborative filtering (CF) is a widely adopted approach, but lacks the ability to provide explanations for the recommended items. |
| Approach: | They propose a model-agnostic framework that enables large language models to provide comprehensive explanations for user behaviors in recommender systems. |
| Outcome: | The proposed framework outperforms baseline approaches in explainable recommender systems. |
LLMs know their vulnerabilities: Uncover Safety Gaps through Natural Distribution Shifts (2025.acl-long)
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Qibing Ren, Hao Li, Dongrui Liu, Zhanxu Xie, Xiaoya Lu, Yu Qiao, Lei Sha, Junchi Yan, Lizhuang Ma, Jing Shao
| Challenge: | Current safety training focuses on teaching models to reject harmful queries, but recent research shows that adversarial attacks or jailbreak methods bypass these safety mechanisms. |
| Approach: | They propose to use a new attack method to craft multi-turn toxic prompts that gradually lead LLMs to reveal unsafe content. |
| Outcome: | The proposed method outperforms existing methods in diversity, effectiveness, and efficiency across aligned LLMs. |
Bridging the Editing Gap in LLMs: FineEdit for Precise and Targeted Text Modifications (2025.findings-emnlp)
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| Challenge: | a recent study shows that large language models can perform precise text editing tasks. |
| Approach: | InstrEditBench is a benchmark dataset that compares 30,000 structured editing tasks . experimental evaluations show FineEdit outperforms state-of-the-art models . |
| Outcome: | The proposed model outperforms state-of-the-art models on single-turn edits and mistral-7B-OpenOrca on direct edits. |
DEIE: Benchmarking Document-level Event Information Extraction with a Large-scale Chinese News Dataset (2024.lrec-main)
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| Challenge: | Existing event-based datasets mainly target sentence-level tasks . current models struggle with "document" annotation, a key feature of the current model . |
| Approach: | They propose a large-scale document-level event information extraction dataset with over 56,000+ events and 242,000+ arguments. |
| Outcome: | The proposed dataset has over 56,000+ events and 242,000+ arguments. |
CoTrust: Privacy-Preserving Collaboration Between Large and Small Language Models in Trusted Execution Environments (2026.findings-acl)
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| Challenge: | Large language models (LLMs) provide powerful text generation capabilities, but accessing sensitive user inputs raises privacy concerns. |
| Approach: | They propose a privacy-preserving collaborative inference framework that combines large language models with small language models inside TEE to preserve privacy. |
| Outcome: | Experiments show that CoTrust outperforms unconstrained LLMs on multiple question answering and summarization benchmarks while maintaining strong privacy protection. |
Query and Output: Generating Words by Querying Distributed Word Representations for Paraphrase Generation (N18-1)
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| Challenge: | Existing models tend to memorize words instead of learning meaning of words . existing models tend not to model semantic information, resulting in incorrect sentences . |
| Approach: | They propose a novel model that generates words by querying distributed word representations . they evaluate model on two paraphrase-oriented tasks, namely text simplification and short abstractive summarization . |
| Outcome: | The proposed model outperforms the baseline model on two paraphrase-oriented tasks . it achieves state-of-the-art performance on these benchmark datasets . |
CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction (2022.coling-1)
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| Challenge: | Existing methods for document-level event extraction struggle due to two intrinsic challenges: nested arguments and multiple events. |
| Approach: | They propose a role-interactive multi-event head attention network to solve two challenges . they map different events to multiple subspaces and then determine whether the current event exists . |
| Outcome: | The proposed model improves on two widely used DEE datasets on the Internet. |
On the Influence of Masking Policies in Intermediate Pre-training (2021.emnlp-main)
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Qinyuan Ye, Belinda Z. Li, Sinong Wang, Benjamin Bolte, Hao Ma, Wen-tau Yih, Xiang Ren, Madian Khabsa
| Challenge: | Existing studies show that inserting an intermediate pre-training stage improves performance of masked language models. |
| Approach: | They propose methods to automate the discovery of optimal masking policies via direct supervision or meta-learning. |
| Outcome: | The proposed method outperforms the heuristic of masking named entities on TriviaQA and can be generalizable beyond that task. |
SemFace: Pre-training Encoder and Decoder with a Semantic Interface for Neural Machine Translation (2021.acl-long)
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| Challenge: | Using pre-training methods for NMT models is difficult because of the cross-attention module . cross-linguistic embeddings are not used to pretrain a decoder . |
| Approach: | They propose a semantic interface between pre-trained encoder and pre-train decoder to improve NMT performance. |
| Outcome: | The proposed method improves on translation and unsupervised translation tasks. |
CodeAttack: Revealing Safety Generalization Challenges of Large Language Models via Code Completion (2024.findings-acl)
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| Challenge: | Large Language Models exhibit remarkable generative capabilities but can be misused for harmful purposes. |
| Approach: | They propose a framework that transforms natural language inputs into code inputs. |
| Outcome: | The proposed framework bypasses the safety guardrails of all models more than 80% of the time. |
GBV-SQL: Guided Generation and SQL2Text Back-Translation Validation for Multi-Agent Text2SQL (2026.acl-long)
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| Challenge: | Existing frameworks for Text2SQL generation still have a critical semantic gap . a dedicated validator translates generated SQL back into natural language and checks whether its logic is aligned with the original question. |
| Approach: | They propose a framework that introduces Guided Generation with SQL2Text Back-translation Validation . dedicated validator translates generated SQL back into natural language and checks whether logic is aligned with original question . |
| Outcome: | The proposed framework achieves 63.23% execution accuracy on the BIRD benchmark and 90.42% on repaired BIDR dev. |
Triangular Architecture for Rare Language Translation (P18-1)
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| Challenge: | Empirical results show that Neural Machine Translation (NMT) performs poor on low-resource pairs especially when Z is a rare language. |
| Approach: | They propose a triangular triangulation technique to leverage bilingual data to optimize the translation performance of low-resource pairs. |
| Outcome: | Empirical results show that the proposed architecture significantly improves translation quality of rare languages on MultiUN and IWSLT2012 datasets and even better when combining back-translation methods. |
Extract, Denoise and Enforce: Evaluating and Improving Concept Preservation for Text-to-Text Generation (2021.emnlp-main)
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| Challenge: | Existing models for text-to-text generation do not explicitly focus on important concepts in the input and output. |
| Approach: | They propose a framework to automatically extract, denoise, and enforce important input concepts as lexical constraints. |
| Outcome: | The proposed framework performs comparably or better than its unconstrained counterpart on automatic metrics and receives better ratings in the human evaluation. |
Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data? (C18-1)
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| Challenge: | Existing neural models take long distance dependencies into account when predicting the tag of the current token. |
| Approach: | They propose a method to capture long distance tag dependencies and use them for dependency analysis. |
| Outcome: | The proposed model can predict multiple tags for the current token without taking dependencies between tags into account. |
CAML: A Conflict-Aware Molecular Language Model Merging Framework for Multi-Constraint Molecular Generation (2026.acl-long)
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Xuanbai Ren, Luoda Tan, Pei Liu, Tengfei Ma, Xiangzheng Fu, Longyue Wang, Yiping Liu, Xiangxiang Zeng
| Challenge: | Existing paradigms struggle with this challenge due to catastrophic forgetting or gradient conflicts. |
| Approach: | They propose a conflict-aware molecular language model merging framework that generates multiple constraints moleculaire as a cooperative game among property-specific fine-tune models. |
| Outcome: | The proposed framework generates multiple constraints molecular as a cooperative game among property-specific fine-tune models (expert models) it minimizes conflicts among properties by exploring the optimal combination of the importance of the task parameter and relative fusion weights of each expert (fusion coefficient). |
Subtle Signatures, Strong Shields: Advancing Robust and Imperceptible Watermarking in Large Language Models (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have led to an increase in AI-generated text on the Internet, presenting a crucial challenge to differentiate AI-created content from human-written text. |
| Approach: | They propose a novel approach to embed watermarks into LLMs that leverages token prior probabilities to improve detectability and maintain watermark imperceptibility. |
| Outcome: | The proposed method improves detectability and imperceptibility of watermarks by partitioning tokens into two distinct groups based on prior probabilities and employing tailored strategies for each group. |
GMN: Generative Multi-modal Network for Practical Document Information Extraction (2022.naacl-main)
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| Challenge: | Document Information Extraction (DIE) has attracted increasing attention due to its various advanced applications in the real world. |
| Approach: | They propose a multi-modal generation method without predefined label categories for real-world scenarios using a spatial encoder and modal-aware mask module. |
| Outcome: | The proposed method can deal with complex documents that are hard to serialize into sequential order. |
MAIR: A Massive Benchmark for Evaluating Instructed Retrieval (2024.emnlp-main)
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Weiwei Sun, Zhengliang Shi, Wu Long, Lingyong Yan, Xinyu Ma, Yiding Liu, Min Cao, Dawei Yin, Zhaochun Ren
| Challenge: | Existing IR benchmarks focus on a limited scope of tasks, making them insufficient for evaluating the latest IR models. |
| Approach: | They propose a multi-task instruction-tuned IR benchmark that includes 126 distinct IR tasks across 6 domains. |
| Outcome: | The proposed model performs better on instruction-tuned models than non-instruction-tunned models on MAIR. |