Papers by Ge Xu

MMRC: A Large-Scale Benchmark for Understanding Multimodal Large Language Model in Real-World Conversation (2025.acl-long)

Challenge:	Recent studies on compression of pretrained language models usually use preserved accuracy as the metric for evaluation.
Approach:	They propose two new metrics that measure how closely a compressed model mimics the original model.
Outcome:	The proposed metrics measure how closely a compressed model (i.e., student) mimics the original model (e.g., teacher).

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Haochen Xue, Feilong Tang, Ming Hu, Yexin Liu, Qidong Huang, Yulong Li, Chengzhi Liu, Zhongxing Xu, Chong Zhang, Chun-Mei Feng, Yutong Xie, Imran Razzak, Zongyuan Ge, Jionglong Su, Junjun He, Yu Qiao

Natural Language Interfaces

Challenge:	Existing multimodal large language models lack the ability to memorize, recall, and reason in sustained interactions.
Approach:	They propose a multimodal real-world conversation benchmark for evaluating open-ended abilities of multimodal large language models.
Outcome:	The proposed benchmarks show that the models perform better in open-ended conversations.

Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge (2021.naacl-main)

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Canwen Xu, Wangchunshu Zhou, Tao Ge, Ke Xu, Julian McAuley, Furu Wei

Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge (2023.findings-acl)

Challenge:	Cant is important for understanding advertising, comedies and dogwhistle politics . currently, there are very few resources available for the research of cant .
Approach:	They propose a large and diverse dataset for creating and understanding cant from a computational linguistics perspective.
Outcome:	The proposed dataset can be used to test word embedding similarity and pretrained language models.

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Hua Cai, Xuli Shen, Qing Xu, Weilin Shen, Xiaomei Wang, Weifeng Ge, Xiaoqing Zheng, Xiangyang Xue

Dialogue Systems & Conversational Agents

Challenge:	Existing work on generating empathetic responses by utilizing the speaker's emotion has not been successful.
Approach:	They propose an approach which incorporates an adaptive module for commonsense knowledge selection to ensure consistency between the generated empathetic responses and the speaker’s situation.
Outcome:	The proposed approach outperforms baseline models in both automatic and human evaluations, exhibiting the generation of more coherent and empathetic responses.

UP5: Unbiased Foundation Model for Fairness-aware Recommendation (2024.eacl-long)

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Wenyue Hua, Yingqiang Ge, Shuyuan Xu, Jianchao Ji, Zelong Li, Yongfeng Zhang

Responsible & Trustworthy NLP

Challenge:	Large Language Models (LLMs) are gaining a foothold in Recommender Systems (RS) but there is growing concern that LLMs perpetuate stereotypes and may result in unfair recommendations.
Approach:	They propose a counterfactually-fair-prompt method for LLM-based recommendation that is based on unbiased foundation mOdels.
Outcome:	The proposed method achieves better recommendation performance with a high level of fairness on two real-world datasets.

RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts (2024.lrec-main)

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Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, Jinkun Lin, Yangguang Mei, Linnan Xu

Bypassing Neural Evaluations for Fast Audio Editing via Adaptive Trajectory Extrapolation (2026.findings-acl)

Challenge:	RAAMove is a comprehensive multi-domain corpus dedicated to the annotation of move structures in Research Article (RA) abstracts.
Approach:	They propose a multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
Outcome:	The proposed corpus is based on a human-annotated dataset and a BERT-based model to verify its effectiveness.

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Xiaoqian Liu, Zhengkun Ge, Jianjin Wang, Haoran Zhang, Yuan Ge, Kaiyan Chang, Chen Xu, Tong Xiao, Zhengtao Yu, Linfeng Zhang, JingBo Zhu

Low-Bit Quantization Favors Undertrained LLMs (2025.acl-long)

Challenge:	Recent advances in audio diffusion models have significantly improved text-to-audio editing via inversion techniques, but these models typically rely on dense, fixed-step sampling trajectories to maintain structural integrity.
Approach:	They propose a model-agnostic Adaptive Trajectory Extrapolation framework that accelerates inversion-based editing process by dynamically evaluating only the most critical generative phases.
Outcome:	The proposed framework achieves a 3.9 speedup with negligible loss in fidelity.

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Xu Ouyang, Tao Ge, Thomas Hartvigsen, Zhisong Zhang, Haitao Mi, Dong Yu

Challenge:	Larger models or those trained on fewer tokens exhibit less quantization-induced degradation (QiD), while smaller, well-trained models face significant performance losses.
Approach:	They propose to use QiD to measure an LLM’s training levels and determine the number of training tokens required for fully training LLMs of various sizes.
Outcome:	The proposed scaling laws can predict the quantization performance of different-sized LLMs trained with tokens.

CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025.findings-naacl)

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Shangda Wu, Yashan Wang, Ruibin Yuan, Guo Zhancheng, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, Maosong Sun

PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics (2026.findings-acl)

Challenge:	Current music information retrieval systems struggle to meet linguistic diversity challenges . current systems struggle with text queries in non-English languages .
Approach:	They propose a music information retrieval system that supports both ABC notation and MIDI . CLaMP 2 includes a multilingual text encoder and a multiple-modal music encoder .
Outcome:	The proposed system achieves state-of-the-art results in multilingual semantic search and music classification across modalities.

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Yaling Shen, Stephanie Fong, Yiwen Jiang, Zimu Wang, Feilong Tang, Qingyang Xu, Xiangyu Zhao, Zhongxing Xu, Jiahe Liu, Jinpeng Hu, Dominic Dwyer, Zongyuan Ge

Responsible & Trustworthy NLP

Challenge:	Mental disorders affect nearly one in seven people worldwide, yet the vast majority do not receive adequate care.
Approach:	They propose a framework to evaluate LLMs' ethical knowledge and behavioral responses through multiple-choice and open-ended tasks with fine-grained ethicality annotations.
Outcome:	Empirical results across 14 models reveal that refusal rates are poor indicators of ethical behavior, revealing a significant divergence between safety triggers and clinical appropriateness.

PRIME: A Process-Outcome Alignment Benchmark for Verifiable Reasoning in Mathematics and Engineering (2026.acl-long)

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Xiangfeng Wang, Hangyu Guo, Yanlin Lai, Mitt Huang, Liang Zhao, Chengyuan Yao, Yinmin Zhang, Qi Han, null Xiaoxiaoren, Chun Yuan, Tong Xu, Zheng Ge, Xiangyu Zhang, Daxin Jiang

Reasoning

Challenge:	Current outcome-centric verification paradigms neglect potential errors in the derivation process.
Approach:	They propose a process-aware RLVR training paradigm utilizing verifiers selected via PRIME.
Outcome:	The proposed approach outperforms the baseline verification paradigm on AIME24, AIME25, and Beyond-AIME models.

Rethinking Multiple-Choice Questions for RLVR: Unlocking Potential via Distractor Design (2026.findings-acl)

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Xu Guo, Qiming Ge, Jian Tong, Kedi Chen, Jin Zhang, Xiaogui Yang, Xuan Gao, Haijun Lv, Zhihui Lu, Yicheng Zou, Qipeng Guo

Reasoning

Challenge:	Existing approaches to RLVR use multiple-choice questions as verifiable rewards . however, not all tasks provide reliable verification .
Approach:	They propose a framework that actively constructs high-quality distractors to block elimination shortcuts and promote deep reasoning.
Outcome:	The proposed method significantly improves reasoning capabilities of Large Language Models.

Domain Adaptation for Conversational Query Production with the RAG Model Feedback (2023.findings-emnlp)

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Ante Wang, Linfeng Song, Ge Xu, Jinsong Su

Dialogue Systems & Conversational Agents

Challenge:	Existing studies have focused on human-annotated search queries but they can not cover conversations of various domains.
Approach:	They propose a domain adaptation framework that uses retrieval-augmented generation to improve the model's robustness.
Outcome:	The proposed model is more robust and performs significantly better in a more challenging setting over strong baselines.

Mitigating Judgment Preference Bias in Large Language Models through Group-Based Polling (2026.findings-acl)

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Shuliang Liu, Zhipeng Xu, Zhenghao Liu, Yukun Yan, Minghe Yu, Yu Gu, Chong Chen, Huiyuan Xie, Ge Yu

Structural Information Preserving for Graph-to-Text Generation (2020.acl-main)

Challenge:	Large Language Models (LLMs) are used as automatic evaluators to provide accurate and reliable assessments.
Approach:	They propose a framework that integrates LLM-based judgment models into a multi-agent system and simulates the interactive client-server polling mechanism.
Outcome:	The proposed framework outperforms supervised models trained on annotated judgment data while requiring no human-labeled annotations.

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Linfeng Song, Ante Wang, Jinsong Su, Yue Zhang, Kun Xu, Yubin Ge, Dong Yu

Structured Data in NLP

Challenge:	Existing models that mess up or drop the core structural information of input graphs are lacking in graph-to-text generation.
Approach:	They propose to leverage richer training signals to guide a graph-to-text generation model by focusing on autoencoding losses and back-propagating the losses to better calibrate the model.
Outcome:	Experiments on two benchmarks show the proposed model over a state-of-the-art model . two types of autoencoding losses are used to back-propagate the model based on multitask training .

ThinkNote: Enhancing Knowledge Integration and Utilization of Large Language Models via Constructivist Cognition Modeling (2026.findings-eacl)

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Zhipeng Xu, Zhenghao Liu, Yukun Yan, Shuo Wang, Shi Yu, Zheni Zeng, Chaojun Xiao, Zhiyuan Liu, Ge Yu, Chenyan Xiong

Improving Grammatical Error Correction with Machine Translation Pairs (2020.findings-emnlp)

Challenge:	Large Language Models (LLMs) exhibit suboptimal behaviors and inconsistencies when exposed to unfamiliar external information, underscoring their limitations in effectively leveraging such knowledge.
Approach:	They propose a framework that enhances the external knowledge utilization of Large Language Models through a two-stage constructivist cognitive modeling process.
Outcome:	The proposed framework achieves a 10% improvement over baseline methods on various question-answering benchmarks.

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Wangchunshu Zhou, Tao Ge, Chang Mu, Ke Xu, Furu Wei, Ming Zhou

Machine Translation

Challenge:	Existing methods to generate error-corrected sentence pairs for improving grammatical error correction are not available.
Approach:	They propose a method to generate error-corrected sentence pairs for improving grammatical error correction based on machine translation models of different qualities .
Outcome:	The proposed method can generate multiple error-corrected sentence pairs from Chinese to English text.

SummaCoz: A Dataset for Improving the Interpretability of Factual Consistency Detection for Summarization (2024.findings-emnlp)

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Ge Luo, Weisi Fan, Miaoran Li, Guoruizhe Sun, Runlong Zhang, Chenyu Xu, Forrest Bao

Explainability & Interpretability in NLP

Challenge:	Summarization is an important application of Large Language Models.
Approach:	They integrate human-annotated and model-generated natural language explanations to elucidate how a summary deviates and becomes inconsistent with its source article.
Outcome:	The proposed model provides rationales for its judgments and improves its accuracy significantly.

UNIKIE-BENCH: Benchmarking Large Multimodal Models for Key Information Extraction in Visual Documents (2026.acl-long)

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Yifan Ji, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Qian Zhang, Zhibo Yang, Junyang Lin, Yu Gu, Ge Yu, Maosong Sun

Information Extraction & Text Mining

Challenge:	Recent Large Multimodal Models (LMMs) have shown promising potential for performing end-to-end KIE directly from document images.
Approach:	They propose a benchmark to evaluate the performance of Large Multimodal Models (LMMs) using a constrained-category KIE track and an open-categorical KIE Track.
Outcome:	Experiments on 15 state-of-the-art LMMs show performance degradation under diverse schema definitions, long-tail key fields, and complex layouts, along with pronounced performance disparities across different document types and scenarios.

Risk-Controlled Event-Driven Cascading Updates for Knowledge Graph Consistency Restoration (2026.findings-acl)

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Bo Ni, Qinwen Ge, Haowei Fu, Ryan A. Rossi, Xiaorui Liu, Jiejun Xu, Tyler Derr

Structured Data in NLP

Challenge:	Knowledge Graphs (KGs) typically treat updates as independent facts . factual, localized updates can contradict and invalidate previously correct knowledge .
Approach:	They propose a model-agnostic framework for cascading KG update identification that leverages conformal prediction to provide reliable uncertainty guarantees over the cascade as a whole.
Outcome:	The proposed framework provides reliable uncertainty guarantees over the cascade as a whole . it integrates large language models to enrich event representations with world knowledge.

MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)

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Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang

BERT-of-Theseus: Compressing BERT by Progressive Module Replacing (2020.emnlp-main)

Challenge:	Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences.
Approach:	They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner.
Outcome:	The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner.

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Canwen Xu, Wangchunshu Zhou, Tao Ge, Furu Wei, Ming Zhou

Unraveling the Mystery: Defending Against Jailbreak Attacks Via Unearthing Real Intention (2025.coling-main)

Challenge:	a novel approach to compress neural networks by progressive module replacement is proposed . a number of techniques have been proposed to compress pretraining and fine-tuning models .
Approach:	They propose a model compression approach that divides BERT into modules and builds their compact substitutes.
Outcome:	The proposed approach outperforms existing knowledge distillation approaches on GLUE benchmark . it is based on a model that divides the original BERT into several modules and builds their substitutes .

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Yanhao Li, Hongshen Chen, Heng Zhang, Zhiwei Ge, Tianhao Li, Sulong Xu, Guibo Luo

Improving Sequence-to-Sequence Pre-training via Sequence Span Rewriting (2021.emnlp-main)

Challenge:	Large Language Models (LLMs) are increasingly vulnerable to elusive and implicit intentions, causing security risks and compromising user experience.
Approach:	They propose a method to detect and mitigate implicit jailbreak attacks using LLMs by unearthing real intentions and a greedy gradient-based algorithm to remove the least important parts of a sentence.
Outcome:	The proposed method reduces attacks success rate and Harmful Score while maintaining overall model performance.

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Wangchunshu Zhou, Tao Ge, Canwen Xu, Ke Xu, Furu Wei

Text Generation

Challenge:	Existing text infilling objectives for pretrained language models require self-supervision by masking out tokens or spans in text.
Approach:	They propose to extend text infilling to a self-supervised sequence-to-sequence (Seq2Sequen) task.
Outcome:	The proposed task improves the model's performance on various natural language generation tasks.

Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains (2026.findings-acl)

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Yuqi Xiong, Chunyi Peng, Zhipeng Xu, Zhenghao Liu, Zulong Chen, Yukun Yan, Shuo Wang, Yu Gu, Ge Yu

Grounded-VideoLLM: Sharpening Fine-grained Temporal Grounding in Video Large Language Models (2025.findings-emnlp)

Challenge:	Existing frameworks depend on rigid, pre-defined external tools to extend perceptual capabilities of VLMs.
Approach:	They propose a framework that leverages self-emergent linguistic toolchains to enhance visual perception and reasoning.
Outcome:	The proposed framework improves the visual perception capabilities of large language models by incorporating external visual documents to address a given query.

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Haibo Wang, Zhiyang Xu, Yu Cheng, Shizhe Diao, Yufan Zhou, Yixin Cao, Qifan Wang, Weifeng Ge, Lifu Huang

Scheduled DropHead: A Regularization Method for Transformer Models (2020.findings-emnlp)

Challenge:	Video Large Language Models (VLMs) have been praised for their performance in coarse-grained video understanding but still face ineffective temporal grounding and inadequate timestamp representations.
Approach:	They propose a novel Video-LLM that senses and reasoned over specific video moments with fine-grained temporal precision.
Outcome:	The proposed model surpasses existing models in fine-grained video understanding tasks and exhibits strong potential as a general video understanding assistant.

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Wangchunshu Zhou, Tao Ge, Furu Wei, Ming Zhou, Ke Xu

OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)

Challenge:	DropHead is a structured dropout method for regularizing multi-head attention . DropHed drops entire attention heads during training to prevent overfitting .
Approach:	They propose a structured dropout method specifically designed for regularizing multi-head attention mechanism . DropHead drops entire attention heads during training to prevent overfitting .
Outcome:	The proposed method can improve transformer models by 0.9 BLEU score on translation task and around 1.0 accuracy for various text classification tasks.

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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang

FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMs (2025.naacl-short)

Challenge:	Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence.
Approach:	They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included .
Outcome:	The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model.

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Forrest Sheng Bao, Miaoran Li, Renyi Qu, Ge Luo, Erana Wan, Yujia Tang, Weisi Fan, Manveer Singh Tamber, Suleman Kazi, Vivek Sourabh, Mike Qi, Ruixuan Tu, Chenyu Xu, Matthew Gonzales, Ofer Mendelevitch, Amin Ahmad

Text Generation

Challenge:	Existing evaluations of hallucinations in large language models suffer from a lack of diversity and recency in the LLM and LLM families considered.
Approach:	They propose a summarization hallucination benchmark that challenges models to disagree on hallucines . they use models to generate answers or summaries from textual input .
Outcome:	The proposed model combines the best of 10 modern LLMs with ground truth annotations.

High-Quality Medical Dialogue Synthesis for Improving EMR Generation (2025.emnlp-industry)

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Chengze Ge, Yu Xu, Qi Shao, Shengping Liu

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for generating EMRs from doctor-patient dialogues produce rigid and repetitive dialogues.
Approach:	They propose a framework that integrates Intent Graph Planning, Dual-Agent Simulation and Rule-Reward Quality Control to generate realistic doctor-patient dialogues.
Outcome:	The proposed framework significantly enhances realism, diversity and downstream EMR quality, reducing physician editing efforts.

O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning (2021.findings-acl)

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Fenglin Liu, Xuancheng Ren, Xian Wu, Bang Yang, Shen Ge, Xu Sun

UnihanLM: Coarse-to-Fine Chinese-Japanese Language Model Pretraining with the Unihan Database (2020.aacl-main)

Challenge:	Existing methods for video captioning consider a sequence of frames and biases towards focused objects.
Approach:	They propose an Object-Oriented Non-Autoregressive approach to video captioning . it performs three steps: 1) identify the focused objects and predict their locations . 2) generate related attribute words and relation words of these focused objects to form a draft caption .
Outcome:	The proposed method achieves competitive results with the state-of-the-art methods but with higher diversity and faster inference speed.

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Canwen Xu, Tao Ge, Chenliang Li, Furu Wei

Unsupervised Multi-Granularity Summarization (2022.findings-emnlp)

Challenge:	Chinese and Japanese share many characters with similar surface morphology.
Approach:	They propose a Chinese-Japanese pretrained masked language model with a coarse-to-fine training approach to exploit the shared knowledge across the languages.
Outcome:	The proposed model is effective on mono- and cross-lingual Chinese and Japanese tasks.

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Ming Zhong, Yang Liu, Suyu Ge, Yuning Mao, Yizhu Jiao, Xingxing Zhang, Yichong Xu, Chenguang Zhu, Michael Zeng, Jiawei Han

Information Extraction & Text Mining

Challenge:	Experimental results confirm the substantial superiority of GranuSum on multi-granularity summarization over strong baselines.
Approach:	They propose to rank events by their salience and annotate a benchmark for GranuSum that contains multiple summaries at different granularities for each document cluster.
Outcome:	The proposed framework is capable of producing multi-granular summaries in unsupervised manner over strong baselines.

CodeDPO: Aligning Code Models with Self Generated and Verified Source Code (2025.acl-long)

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Kechi Zhang, Ge Li, Yihong Dong, Jingjing Xu, Jun Zhang, Jing Su, Yongfei Liu, Zhi Jin

Programming Languages in NLP

Challenge:	Existing training methods for code generation do not improve code correctness and efficiency.
Approach:	They propose a framework that integrates preference learning into code generation to improve code correctness and efficiency.
Outcome:	The proposed framework improves code correctness and efficiency by integrating preference learning into code generation.

BERT-based Lexical Substitution (P19-1)

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Wangchunshu Zhou, Tao Ge, Ke Xu, Furu Wei, Ming Zhou

Pseudo-Bidirectional Decoding for Local Sequence Transduction (2020.findings-emnlp)

Challenge:	Existing approaches to lexical substitution tend to overlook good substitute candidates that are not the synonyms of the target words in the lexicals and fail to take into account the substitution’s influence on the global context of the sentence.
Approach:	They propose an end-to-end BERT-based lexical substitution approach which proposes and validates substitute candidates without using annotated data or manually curated resources.
Outcome:	The proposed approach performs well in proposing and ranking substitute candidates, achieving the state-of-the-art results in both LS07 and LS14 benchmarks.

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Wangchunshu Zhou, Tao Ge, Ke Xu