Papers by Xu Tan

TeleMelody: Lyric-to-Melody Generation with a Template-Based Two-Stage Method (2022.emnlp-main)

Challenge:	Existing benchmarks focus more on end-to-end performance, but neglect the underlying principles of knowledge acquisition and generalization.
Approach:	They propose a benchmark specifically designed to explore the problem-solving principles by decomposing 6.5K visual math problems into 10.9K step-level questions for evaluation.
Outcome:	The proposed benchmark covers 6.5K visual math problems and 10.9K step-level questions spanning 5 layers of knowledge granularity and 67 hierarchical knowledge concepts.

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Zeqian Ju, Peiling Lu, Xu Tan, Rui Wang, Chen Zhang, Songruoyao Wu, Kejun Zhang, Xiang-Yang Li, Tao Qin, Tie-Yan Liu

Towards Modern Topic Models: A Survey of Taxonomies and Paradigm Shifts from Algorithm-Centric to LLM-Centered Topic Analysis (2026.findings-acl)

Challenge:	a new lyric-to-melody generation system bridges the gap between lyrics and melodies . previous generation systems lack paired data and lack of control on generated melodie.
Approach:	They develop a lyric-to-melody generation system with music template to bridge the gap between lyrics and melodies.
Outcome:	The proposed system bridges the gap between lyrics and melodies by using music template.

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Xuan Xu, Zhongliang Yang, Haolun Li, Rui Tian, Beilin Chu, J Song, Yu Li, Shaolin Tan, Linna Zhou

Self-Criticism: Aligning Large Language Models with their Understanding of Helpfulness, Honesty, and Harmlessness (2023.emnlp-industry)

Challenge:	Topic modeling (TM) is a classic unsupervised learning task in the field of natural language processing.
Approach:	They propose a new taxonomy that emphasizes the role of LLMs and the design of end-to-end workflows.
Outcome:	The proposed taxonomy emphasizes the role of LLMs and the design of end-to-end workflows.

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Xiaoyu Tan, Shaojie Shi, Xihe Qiu, Chao Qu, Zhenting Qi, Yinghui Xu, Yuan Qi

Understanding Social Media Cross-Modality Discourse in Linguistic Space (2022.findings-emnlp)

Challenge:	Recent studies have shown that large language models are useful, honest, harmless (HHH) however, RLHF requires high hardware resources and human efforts.
Approach:	They propose a framework that allows LLMs to align themselves with HHH . they use IF and reinforcement learning from human feedback to fine-tune their models .
Outcome:	The proposed framework achieves similar performance to RLHF and human-generated models with a minimal alignment tax.

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Chunpu Xu, Hanzhuo Tan, Jing Li, Piji Li

MusicAgent: An AI Agent for Music Understanding and Generation with Large Language Models (2023.emnlp-demo)

Challenge:	Existing studies on how images are structured with texts to form coherent meanings in human cognition have not addressed the problem.
Approach:	They propose a concept of cross-modality discourse which defines how human readers couple image and text understandings.
Outcome:	The proposed model shows that trendy encoders based on multi-head attention are unable to understand cross-modality discourse and modeling texts at the output layer helps yield the-state-of-the-art results.

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Dingyao Yu, Kaitao Song, Peiling Lu, Tianyu He, Xu Tan, Wei Ye, Shikun Zhang, Jiang Bian

BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks (2026.acl-long)

Challenge:	MusicAgent integrates numerous music-related tools and an autonomous workflow to address user requirements.
Approach:	a new system is built to integrate music-related tools and an autonomous workflow . the system is based on large language models (LLMs) that can be used to organize and decompose requests .
Outcome:	the proposed system integrates numerous music-related tools and an autonomous workflow to address user requirements.

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Rui Miao, Yixin Liu, Yili Wang, Xu Shen, Yue Tan, Yiwei Dai, Shirui Pan, Xin Wang

Robustness in NLP

Challenge:	Existing supervised defense methods rely on labeled malicious agents to train a supervised model of malicious behavior.
Approach:	They propose an unsupervised defense method that learns without requiring any attack-specific labels or prior knowledge of malicious behaviors.
Outcome:	The proposed method detects diverse attack types across MAS with various communication patterns while maintaining superior generalizability compared to baselines.

On Efficient Retrieval of Top Similarity Vectors (D19-1)

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Shulong Tan, Zhixin Zhou, Zhaozhuo Xu, Ping Li

Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers (P19-1)

Challenge:	Existing representation learning methods such as Word2vec represent word embeddings in the semantic space.
Approach:	They propose an efficient method for searching vectors via a non-metric matching function: inner product.
Outcome:	Experiments on data representations learned for different machine learning tasks show the proposed method outperforms existing methods.

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Haoyu Wang, Ming Tan, Mo Yu, Shiyu Chang, Dakuo Wang, Kun Xu, Xiaoxiao Guo, Saloni Potdar

Condensing Multilingual Knowledge with Lightweight Language-Specific Modules (2023.emnlp-main)

Challenge:	Existing approaches to extract multiple relations from a paragraph require multiple passes over the paragraph.
Approach:	They propose a method to extract multiple relations from a paragraph by encoding the paragraph only once.
Outcome:	The proposed approach can perform state-of-the-art on the benchmark ACE 2005.

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Haoran Xu, Weiting Tan, Shuyue Li, Yunmo Chen, Benjamin Van Durme, Philipp Koehn, Kenton Murray

Non-Autoregressive Sequence Generation (2022.acl-tutorials)

Challenge:	Existing methods to boost performance in multilingual models but scalability is difficult to manage.
Approach:	They propose a method that incorporates language-specific (LS) modules to boost model performance.
Outcome:	The proposed method outperforms state-of-the-art methods while outperforming existing methods.

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Jiatao Gu, Xu Tan

SimulSpeech: End-to-End Simultaneous Speech to Text Translation (2020.acl-main)

Challenge:	Non-autoregressive sequence generation (NAR) models generate output sequences in parallel to speed up generation process.
Approach:	This tutorial provides a thorough introduction and review of non-autoregressive sequence generation . it aims to generate the entire or partial output sequences in parallel to speed up the generation process .
Outcome:	This tutorial provides a thorough introduction and review of non-autoregressive sequence generation . it aims to reduce the performance gap between state-of-the-art models due to lack of modeling power .

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Yi Ren, Jinglin Liu, Xu Tan, Chen Zhang, Tao Qin, Zhou Zhao, Tie-Yan Liu

AlphaFin: Benchmarking Financial Analysis with Retrieval-Augmented Stock-Chain Framework (2024.lrec-main)

Challenge:	SimulSpeech is an end-to-end simultaneous speech to text translation system . conventional approaches to simultaneous speech translation divide the translation process into two stages .
Approach:	They develop an end-to-end simultaneous speech to text translation system which translates speech in source language to text in target language concurrently.
Outcome:	The proposed system achieves reasonable BLEU scores and lower delay compared to full-sentence translation model.

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Xiang Li, Zhenyu Li, Chen Shi, Yong Xu, Qing Du, Mingkui Tan, Jun Huang

Information Retrieval

Challenge:	Currently, ML&DL methods fail to provide reasons for stock trend predictions, lacking interpretability and reasoning processes. large language models (LLMs) suffer from hallucinations and are unable to keep up with the latest information.
Approach:	They develop a method to train large language models to handle financial analysis tasks . they use AlphaFin datasets to compare performance with traditional methods .
Outcome:	The proposed method improves stock trend prediction and financial question answering tasks.

MalURLBench: A Benchmark Evaluating Agents’ Vulnerabilities When Processing Web URLs (2026.findings-acl)

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Dezhang Kong, Zhuxi Wu, Shiqi Liu, ZhiCheng Tan, Kuichen Lu, Minghao Li, Qichen Liu, Shengyu Chu, Zhenhua Xu, Xuan Liu, Meng Han

Robustness in NLP

Challenge:	Existing models struggle to detect elaborately disguised malicious URLs, despite their ability to process malicious URL's.
Approach:	They propose a benchmark to evaluate LLMs’ vulnerabilities to malicious URLs and a lightweight defense module to mitigate the vulnerability.
Outcome:	The proposed framework analyzes 61,845 attack instances spanning 10 real-world scenarios and 7 categories of real malicious websites.

ULMR: Unlearning Large Language Models via Negative Response and Model Parameter Average (2024.emnlp-industry)

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Shaojie Shi, Xiaoyu Tan, Xihe Qiu, Chao Qu, Kexin Nie, Yuan Cheng, Wei Chu, Xu Yinghui, Yuan Qi

LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable Prompting (2022.coling-1)

Challenge:	Large language models (LLMs) have attracted significant interest from the research community due to their broad applicability in many language-oriented tasks.
Approach:	They propose a framework which uses pre-training datasets to rewrite instructions and generate negative responses to preserve the performance of the original LLM.
Outcome:	The proposed framework can erase the pre-training data while maintaining the performance of the original model.

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Xiang Chen, Lei Li, Shumin Deng, Chuanqi Tan, Changliang Xu, Fei Huang, Luo Si, Huajun Chen, Ningyu Zhang

Low-Resource NLP

Challenge:	Existing approaches for Named Entity Recognition (NER) use extensive labeled data for model training, which struggles in low-resource scenarios.
Approach:	They propose a lightweight tuning paradigm for low-resource NER via pluggable prompting . they construct a learnable verbalizer of entity categories without any label-specific classifiers .
Outcome:	The proposed model outperforms baselines and class transfer models in low-resource scenarios.

Revisiting Over-Smoothness in Text to Speech (2022.acl-long)

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Yi Ren, Xu Tan, Tao Qin, Zhou Zhao, Tie-Yan Liu

Towards Fine-grained Text Sentiment Transfer (P19-1)

Challenge:	Non-autoregressive text to speech models ignore correlation in time and frequency domains, causing blurry results.
Approach:	They revisit the problem of over-smoothness in non-autoregressive text to speech models . they use methods that reduce complexity of data distributions and improve modeling methods .
Outcome:	The proposed models achieve better voice quality and faster inference speed than autoregressive models.

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Fuli Luo, Peng Li, Pengcheng Yang, Jie Zhou, Yutong Tan, Baobao Chang, Zhifang Sui, Xu Sun

Sentiment Analysis

Challenge:	Existing methods for fine-grained text sentiment transfer only reverse the sentiment polarity of text, but they lack a robust and parallel learning algorithm.
Approach:	They propose a novel fine-grained text sentiment transfer task that revises a sequence to satisfy a given sentiment intensity while preserving the original semantic content.
Outcome:	The proposed model outperforms existing methods by a large margin in automatic evaluation and human evaluation.

Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning (2021.emnlp-main)

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Runxin Xu, Fuli Luo, Zhiyuan Zhang, Chuanqi Tan, Baobao Chang, Songfang Huang, Fei Huang

Semantic Role Labeling Guided Multi-turn Dialogue ReWriter (2020.emnlp-main)

Challenge:	Recent pretrained language models extend from millions to billions of parameters.
Approach:	They propose a technique which forwards on a whole network while backwarding on resetting the gradients of the non-child network during the backward process.
Outcome:	The proposed technique outperforms the vanilla fine-tuning technique on various downstream tasks and can achieve better generalization performance by large margins.

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Kun Xu, Haochen Tan, Linfeng Song, Han Wu, Haisong Zhang, Linqi Song, Dong Yu

Paraphrasing

Challenge:	Existing attentive models attend to all words without prior focus, which results in inaccurate concentration on some dispensable words.
Approach:	They propose to use semantic role labeling to provide additional guidance for multi-turn dialogue rewriting models.
Outcome:	The proposed model outperforms existing models on multi-turn dialogue rewriting tasks.

Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction (2022.emnlp-main)

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Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng, Sharifah Mahani Aljunied

Pause or Fabricate? Training Language Models for Grounded Reasoning (2026.findings-acl)

Challenge:	Using incomplete annotations, we find that false negative samples are prevalent in the DocRED dataset . we reannotate 4,053 documents in the dataset by adding the missed relation triples back to the original DocRED.
Approach:	They propose to re-annotate 4,053 documents in the document-level relation extraction dataset by adding missing relation triples back to the original DocRED.
Outcome:	The proposed dataset improves on the existing DocRED dataset by 13 F1 points.

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Yiwen Qiu, Linjuan Wu, Yizhou Liu, Yuchen Yan, Jin Ma, Xu Tan, Yao Hu, Daoxin Zhang, Wenqi Zhang, Weiming Lu, Jun Xiao, Yongliang Shen

Towards Fine-Grained and Multi-Granular Contrastive Language-Speech Pre-training (2026.acl-long)

Challenge:	Large language models implicitly fabricate information when inputs are incomplete, causing confidence but unreliable conclusions.
Approach:	They propose a framework for grounded reasoning under incomplete information that decomposes reasoning into two stages . they propose stage-specific rewards to penalize hallucinations, enabling models to detect gaps, stop proactively, and resume reasoning after clarification.
Outcome:	The proposed framework improves premise detection and task success by 30% . it also reduces average response length by over 20% .

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Yifan Yang, Bing Han, Hui Wang, Wei Wang, Ziyang Ma, Long Zhou, Zengrui Jin, Guanrou Yang, Tianrui Wang, Xu Tan, Xie Chen

MuSe: Multi-Stage Graph Reasoning via Vision-Language Models (2026.acl-long)

Challenge:	Existing models for fine-grained speaking styles are limited in terms of accuracy, coverage, and naturalness.
Approach:	They propose a model that pre-trains with coarse captions and annotates with a pipeline that grounds captions in audio.
Outcome:	The proposed model outperforms existing models with fine-grained style annotations . it integrates global and fine-granular supervision, enabling unified representations based on the proposed model .

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Guanyu Wang, Xu Chu, Zhijie Tan, Xinrong Chen, Tong Mo, Weiping Li

Towards Order Fairness: Mitigating LLMs Order Sensitivity through Dual Group Advantage Optimization (2026.acl-long)

Challenge:	Graph Neural Networks (GNNs) and graph transformers are inadequate for tasks with limited generalization.
Approach:	They propose a multi-stage graph reasoning framework based on vision-language models that incrementally samples and visualizes task-relevant subgraphs.
Outcome:	The proposed framework outperforms existing benchmarks in Graph-related tasks.

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Xu Chu, Guanyu Wang, Zhijie Tan, Xinrong Chen, Ziyu Li, Tong Mo, Weiping Li

Unsupervised Pivot Translation for Distant Languages (P19-1)

Challenge:	Recent studies attempt to obtain optimal or suboptimal arrangements based on statistical results or using dataset-based search, but these methods increase inference overhead while leaving the model’s inherent order bias unresolved.
Approach:	They propose Dual Group Advantage Optimization (DGAO) which aims to improve model accuracy and order stability simultaneously.
Outcome:	The proposed method improves model accuracy and order stability while penalizing order-sensitive or incorrect responses.

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Yichong Leng, Xu Tan, Tao Qin, Xiang-Yang Li, Tie-Yan Liu

Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction (2026.findings-acl)

Challenge:	Unsupervised neural machine translation (NMT) is a popular method for transferring information between languages.
Approach:	They propose an unsupervised pivot translation method which translates a language to a distant language through multiple hops.
Outcome:	The proposed method improves translation on 20 languages and 294 distant languages on 20 different languages and language pairs.

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Xu Shen, Qi Zhang, Song Wang, Zhen Tan, Xinyu Zhao, Laura Yao, Vaishnav Tadiparthi, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Kwonjoon Lee, Tianlong Chen

KAPA: A Deliberative Agent Framework with Tree-Structured Knowledge Base for Multi-Domain User Intent Understanding (2025.findings-acl)

Challenge:	Large Language Model based multi-agent systems (MAS) excel at collaborative problem solving but remain brittle to cascading errors.
Approach:	They propose a metacognitive framework that enables step-level error detection and self-correction in Large Language Model based multi-agent systems (MAS) .
Outcome:	The proposed framework outperforms baselines on the Who When benchmark and delivers consistent gains on AgentErrorBench.

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Jiakai Tang, Shiqi Shen, ZhipengWang ZhipengWang, Gong Zhi, Xueyang Feng, Zexu Sun, Haoran Tan, Xu Chen

Knowledge Representation

Challenge:	Existing studies on the use of LLMs for estimating user intents are either too far from real human thought processes or require labeled samples.
Approach:	They propose a deliberative agent framework that leverages human thought process to build high-level domain knowledge and a tree-structured knowledge base to store refined experience and data.
Outcome:	The proposed framework is able to build high-level domain knowledge and efficiently store it across multiple steps.

A Study of Non-autoregressive Model for Sequence Generation (2020.acl-main)

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Yi Ren, Jinglin Liu, Xu Tan, Zhou Zhao, Sheng Zhao, Tie-Yan Liu

AgentAsk: Multi-Agent Systems Need to Ask (2026.acl-long)

Challenge:	Non-autoregressive (NAR) models generate all tokens in parallel, resulting in faster generation speed compared to autoregressive models.
Approach:	They propose to use knowledge distillation and source-target alignment to bridge the gap between NAR and autoregressive models in various tasks.
Outcome:	The proposed techniques can speed up NAR models in some tasks but not all . the proposed techniques reduce target token dependency while allowing for faster inference .

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Bohan Lin, Kuo Yang, Zelin Tan, Yingchuan Lai, Chen Zhang, Guibin Zhang, Xinlei Yu, Miao Yu, Xu Wang, Yudong Zhang, Yang Wang

MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)

Challenge:	Multi-agent systems fail to consistently outperform strong single-a agent baselines due to error propagation at inter-aggent message handoffs.
Approach:	They propose an edge-level error taxonomy that identifies four main errors in multi-agent interactions as data gaps, signal corruption, referential drift and capacity gaps as primary sources of failure.
Outcome:	The proposed module outperforms existing systems on five benchmarks and is architecture-agnostic.

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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen

Mobile-Bench: An Evaluation Benchmark for LLM-based Mobile Agents (2024.acl-long)

Challenge:	Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed .
Approach:	They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities.
Outcome:	The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech .

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Shihan Deng, Weikai Xu, Hongda Sun, Wei Liu, Tao Tan, Liujianfeng Liujianfeng, Ang Li, Jian Luan, Bin Wang, Rui Yan, Shuo Shang

Dense Information Flow for Neural Machine Translation (N18-1)

Challenge:	Existing benchmarks for LLM-based mobile agents are insufficient to evaluate their capabilities.
Approach:	They propose a benchmark to evaluate LLM-based mobile agents' planning capabilities . they expand UI operations by incorporating 103 APIs to accelerate task completion .
Outcome:	The proposed benchmarks are based on 103 collected APIs and real user queries . the data is categorized into three distinct groups: SAST, SAMT, and MAMT .

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Yanyao Shen, Xu Tan, Di He, Tao Qin, Tie-Yan Liu

RADO: Reasoning Audit-Driven Optimization for Rigorous Reasoning in High-Stakes Domains (2026.acl-long)

Challenge:	Recent advances in deep neural networks have improved learning performance for NMT . Residual connections allow features from previous layers to be accumulated to the next layer easily.
Approach:	They propose a densely connected NMT architecture that can train more efficiently for NMT.
Outcome:	The proposed architecture improves learning performance and attention quality on multiple datasets.

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Zhijie Tan, Xu Chu, Guanyu Wang, Ziyu Li, Weiping Li, Tong Mo

Reasoning

Challenge:	Current reinforcement learning paradigms rely on outcome-based rewards, overlooking latent logical fallacies in intermediate steps.
Approach:	They propose a specialized audit model augmented with external tools to identify local logical ruptures and calibrate reward signals.
Outcome:	The proposed framework improves accuracy and logical rigor in high-stakes domains.

EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms (2025.naacl-long)

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Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Dongsheng Li, Deqing Yang

Rethinking the Role of Prompting Strategies in LLM Test-Time Scaling: A Perspective of Probability Theory (2025.acl-long)

Challenge:	Existing work on extending specialized agents to multi-agent systems is dependent on human-designed frameworks, limiting the functional scope and scalability of agent systems.
Approach:	They propose a generic method to automatically extend specialized agents to multi-agent systems via evolutionary algorithm . they consider existing agent frameworks as the initial individual and apply evolutionary operators to generate multiple agents with diverse settings.
Outcome:	The proposed method can extend specialized agents to multi-agent systems . it can generate multiple agents with diverse settings, and improves performance across tasks .

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Yexiang Liu, Zekun Li, Zhi Fang, Nan Xu, Ran He, Tieniu Tan

Towards Understanding Omission in Dialogue Summarization (2023.acl-long)

Challenge:	Recent studies have shown that scaling test-time compute can also effectively improve reasoning.
Approach:	They propose a probabilistic method to efficiently predict scaling performance and identify the best prompting strategy under large sampling times.
Outcome:	The proposed method significantly improves the scaling performance of majority voting on large language models.

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Yicheng Zou, Kaitao Song, Xu Tan, Zhongkai Fu, Qi Zhang, Dongsheng Li, Tao Gui

FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition (2021.findings-emnlp)

Challenge:	Existing methods for dialogue summarization are far from satisfactory . omission is a major factor in affecting the quality of summarizing, but few studies have explored the problem .
Approach:	They propose a dataset that provides high-quality omission labels for dialogue summarization . they propose to use this dataset to detect omitted dialogue utterances .
Outcome:	The proposed dataset improves summarization quality by providing ground-truth omission labels . the proposed dataset and codes are publicly available .

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Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu

MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents (2025.findings-acl)

Challenge:	Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence.
Approach:	They propose a fast correction model that takes multiple ASR candidates as input for better correction accuracy.
Outcome:	The proposed model can reduce the word error rate (WER) with multiple candidates by 3.2% and 2.6%.

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Haoran Tan, Zeyu Zhang, Chen Ma, Xu Chen, Quanyu Dai, Zhenhua Dong

Challenge:	Recent studies have highlighted the significance of memory mechanisms in LLM-based agents, which enable them to store observed information and adapt to dynamic environments.
Approach:	They propose a dataset and benchmark to evaluate the memory capability of LLM-based agents from multiple aspects including their effectiveness, efficiency, and capacity.
Outcome:	The proposed benchmark incorporates factual memory and reflective memory as different levels, and proposes participation and observation as various interactive scenarios.

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

Class-Adaptive Self-Training for Relation Extraction with Incompletely Annotated Training Data (2023.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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Qingyu Tan, Lu Xu, Lidong Bing, Hwee Tou Ng

TranSFormer: Slow-Fast Transformer for Machine Translation (2023.findings-acl)

Challenge:	Existing relation extraction models rely on supervised machine learning, but many datasets are incompletely annotated, causing false negatives and errors during inference stage.
Approach:	They propose a class-adaptive re-sampling self-training framework that favored the pseudo-labels of classes with high precision and low recall scores.
Outcome:	The proposed framework outperforms existing methods on the Re-DocRED and ChemDisgene datasets when the training data are incompletely annotated.

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Bei Li, Yi Jing, Xu Tan, Zhen Xing, Tong Xiao, Jingbo Zhu

MatPlotAgent: Method and Evaluation for LLM-Based Agentic Scientific Data Visualization (2024.findings-acl)

Challenge:	Prior work has focused on treating subwords as basic units in developing such systems.
Approach:	They propose a slow-fast two-stream learning model that uses a “slow” branch to deal with subword sequences and a "fast" branch to cope with longer character sequences.
Outcome:	The proposed model shows consistent BLEU improvements (larger than 1 BLUE point) on several machine translation benchmarks.

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Zhiyu Yang, Zihan Zhou, Shuo Wang, Xin Cong, Xu Han, Yukun Yan, Zhenghao Liu, Zhixing Tan, Pengyuan Liu, Dong Yu, Zhiyuan Liu, Xiaodong Shi, Maosong Sun

Focal Training and Tagger Decouple for Grammatical Error Correction (2023.findings-acl)

Challenge:	Scientific data visualization is an essential process in research, but its use of large language models remains unexplored.
Approach:	They propose a model-agnostic LLM agent framework to automate scientific data visualization tasks.
Outcome:	The proposed framework improves performance of commercial and open-source models.

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Minghuan Tan, Min Yang, Ruifeng Xu

Syntactic Text Processing

Challenge:	Existing methods for Grammatical Error Correction (GEC) are categorized into sequence-to-sequence approaches, tagging-based approaches, and hybrid approaches.
Approach:	They propose to decouple error detection layer from label tagging layer and to down-weight label imbalance and tabbing entanglement loss using Focal Loss.
Outcome:	The proposed methods are effective over three latest Chinese Grammatical Error Correction datasets.

GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis (2026.findings-acl)

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Shaoting Tan, Ning Liu, Yuntao Du, Shuyue Wei, Wu Shuai, Qian Li, Yanyu Xu, Wei Zhang, Lizhen Cui, Haitao Yuan

From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents (2026.findings-acl)

Challenge:	Existing Large Language Models struggle to reason systematically under cost constraints . Existing approaches lack the knowledge-reasoning capability to reason under cost .
Approach:	They propose a knowledge-enhanced framework that leverages large language models to construct MDKGs . they propose three collaborative agents that handle language understanding and generation .
Outcome:	GraphDx improves diagnostic success rates from 50–68% to 79–93% while reducing test costs by 20–54%.

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Haoran Tan, Zeyu Zhang, Chen Ma, Tianze Liu, Quanyu Dai, Xu Chen

Challenge:	Existing plans for large language model-based agents are limited by their granularity and lack flexibility.
Approach:	They propose a self-adaptive hierarchical planning mechanism that mimics human planning strategies and generates self-adapted hierarchic plans tailored to the varying difficulty levels of different tasks.
Outcome:	The proposed method significantly improves task execution success rates while mitigating overthinking at the planning level, providing a flexible and efficient solution for multi-step complex decision-making tasks.

DeepRapper: Neural Rap Generation with Rhyme and Rhythm Modeling (2021.acl-long)

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Lanqing Xue, Kaitao Song, Duocai Wu, Xu Tan, Nevin L. Zhang, Tao Qin, Wei-Qiang Zhang, Tie-Yan Liu

PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching (2023.emnlp-industry)

Challenge:	Existing systems for rap generation focus on rhyming lyrics but ignore rhythmic beats . rap lyrics need to be semantically meaningful and fashionable to convey interesting stories .
Approach:	They develop a Transformer-based rap generation system that can model both rhymes and rhythms.
Outcome:	The proposed system generates high-quality raps with rhymes and rhythms . it is based on a Transformer-based language model .

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Zhenting Qi, Xiaoyu Tan, Shaojie Shi, Chao Qu, Yinghui Xu, Yuan Qi

FaStFact: Faster, Stronger Long-Form Factuality Evaluations in LLMs (2025.findings-emnlp)

Challenge:	Low-Rank Adaptation (LoRA) has been used to adapt Large Language Models to a variety of tasks, but it requires substantial computational resources to perform.
Approach:	They propose a low-rank adaptive learning approach that leverages LoRA's in-context learning capability through prompt matching via reinforcement learning in resource-constrained environments.
Outcome:	The proposed model improves LoRA performance on evaluation metrics and utilises consumer-grade GPU resources.

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Yingjia Wan, Haochen Tan, Xiao Zhu, Xinyu Zhou, Zhiwei Li, Qingsong Lv, Changxuan Sun, Jiaqi Zeng, Yi Xu, Jianqiao Lu, Yinhong Liu, Zhijiang Guo

MobileVLM: A Vision-Language Model for Better Intra- and Inter-UI Understanding (2024.findings-emnlp)

Challenge:	Prior evaluation pipelines fail to evaluate factuality of long-form LLMs due to inefficiency and costly human assessment.
Approach:	They propose a fast and strong evaluation pipeline that can evaluate factuality of long-form LLMs . they propose 'faStFact' to reduce cost of web searching and inference calling .
Outcome:	The proposed evaluation pipeline achieves highest alignment with human evaluation and efficiency among existing baselines.

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Qinzhuo Wu, Weikai Xu, Wei Liu, Tao Tan, Liujian Liujianfeng, Ang Li, Jian Luan, Bin Wang, Shuo Shang

AEQ-Bench: Measuring Empathy of Omni-Modal Large Models (2026.findings-acl)

Challenge:	Recent mobile AI agents based on VLMs lack basic mobile capabilities due to their pre-trained nature.
Approach:	They propose a mobile AI agent based on VLMs that includes additional pre-training stages to enhance both intra- and inter-UI understanding.
Outcome:	The proposed model outperforms existing VLMs on the Chinese mobile dataset Mobile3M .

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Xuan Luo, Lewei Yao, Lanqing Hong, Kai Chen, Dehua Tao, Daxin Tan, Yukun Deng, Ruifeng Xu, Jing Li

Zero-shot Cross-lingual Conversational Semantic Role Labeling (2022.findings-naacl)

Challenge:	Existing benchmarks focus on cognitive abilities, such as knowledge retrieval, complex reasoning, and instruction following, largely overlooking empathy evaluation.
Approach:	They propose to benchmark two core empathetic capabilities of omnimodal large models (OLMs) generating empatries by comprehending affective cues from multi-modal inputs and judging empathy of audio responses without relying on text transcription.
Outcome:	The proposed benchmark outperforms existing models with audio output capabilities but is unreliable for evaluating fine-grained paralinguistic expressiveness.

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Han Wu, Haochen Tan, Kun Xu, Shuqi Liu, Lianwei Wu, Linqi Song

Unveiling the Lexical Sensitivity of LLMs: Combinatorial Optimization for Prompt Enhancement (2024.emnlp-main)

Challenge:	Xu et al., 2021: conversational semantic role labeling is under-explored in non-Chinese languages due to the lack of multilingual CSRL annotations for the parser training.
Approach:	They propose a model that implicitly learns conversational structure-aware representations with hierarchical encoders and elaborately designed pre-training objectives.
Outcome:	The proposed model outperforms baselines on English CSRL tests by large margins . it will facilitate the research of non-Chinese dialogue tasks which suffer from ellipsis and anaphora .

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Pengwei Zhan, Zhen Xu, Qian Tan, Jie Song, Ru Xie

Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains (2025.findings-acl)

Challenge:	Large language models (LLMs) demonstrate exceptional instruct-following ability to complete downstream tasks.
Approach:	They propose a black-box combinatorial optimization framework that iteratively improves lexical choices in prompts by a search strategy related to word influence.
Outcome:	The proposed framework recovers the model's ability to instruct-follow and solve downstream tasks even when the variations are imperceptible to humans.

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Xu Chu, Zhijie Tan, Hanlin Xue, Guanyu Wang, Tong Mo, Weiping Li

UI-Copilot: Advancing Long-Horizon GUI Automation via Tool-Integrated Policy Optimization (2026.acl-long)

Challenge:	Existing Large Language Models (LLMs) generate brief answers without reasoning processes and explanations.
Approach:	They propose supervised fine-tuning and tree search to enhance LLMs’ reasoning capabilities on domain tasks.
Outcome:	The proposed model improves on stock investment recommendation and legal reasoning QA tasks.

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Zhengxi Lu, Fei Tang, Guangyi Liu, Jin Ma, Kaitao Song, Xu Tan, Wenqi Zhang, Weiming Lu, Jun Xiao, Yueting Zhuang, Yongliang Shen

Information Retrieval

Challenge:	Experimental results show that UI-Copilot-7B achieves state-of-the-art performance on challenging MemGUI-Bench, outperforming strong 7B-scale GUI agents such as GUI-Owl-7B and UITARS-1.5-7B.
Approach:	They propose a collaborative framework where the GUI agent focuses on task execution while a lightweight copilot provides on-demand assistance for memory retrieval and numerical computation.
Outcome:	The proposed framework outperforms GUI-Owl-7B and UI-TARS-1.5-7B on MemGUI-Bench and delivers 17.1% improvement on AndroidWorld over the base Qwen model.

Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning (2023.acl-short)

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Zhen-Ru Zhang, Chuanqi Tan, Haiyang Xu, Chengyu Wang, Jun Huang, Songfang Huang

Continual Few-shot Relation Extraction via Adaptive Gradient Correction and Knowledge Decomposition (2024.findings-acl)

Challenge:	Parameter-efficient fine-tuning only optimizes a few task-specific parameters with frozen pre-trained model.
Approach:	They propose to optimize a prefix vector inserted into Transformer layers to optimize the prefix . they propose to use a gate mechanism to adjust the prefixed to each layer .
Outcome:	The proposed approach improves on the SuperGLUE and NER datasets.

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Jianpeng Hu, Chengxiang Tan, JiaCheng Xu, XiangyunKong XiangyunKong

NUMCoT: Numerals and Units of Measurement in Chain-of-Thought Reasoning using Large Language Models (2024.findings-acl)

Challenge:	Existing methods to learn new relations with limited samples neglect the instability of embeddings in the process of different task training, which leads to catastrophic forgetting.
Approach:	They propose a method to analyze catastrophic forgetting by limiting embedding instability . they propose to decompose knowledge into general and task-related knowledge .
Outcome:	The proposed method outperforms the state-of-the-art model and improves the following degree of embeddings.

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Ancheng Xu, Minghuan Tan, Lei Wang, Min Yang, Ruifeng Xu

Reasoning

Challenge:	Existing LLMs are not able to handle numerals and units of measurement, but they can be improved by introducing perturbations.
Approach:	They propose to analyze existing LLMs on processing numerals and units of measurement by perturbing their datasets.
Outcome:	The proposed model improves on ancient Chinese arithmetic problems and can handle numeral and measurement conversions.

Beyond Error Propagation in Neural Machine Translation: Characteristics of Language Also Matter (D18-1)

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Lijun Wu, Xu Tan, Di He, Fei Tian, Tao Qin, Jianhuang Lai, Tie-Yan Liu

TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge (2022.findings-acl)

Challenge:	Neural machine translation suffers from exposure bias and error propagation problem.
Approach:	They conduct a series of analyses to deeply understand the accuracy drop problem . they find that the left part of the translated sentence is often better than its right part .
Outcome:	The results show that the left part of the translated sentence is often better than its right part in left-to-right decoding models.

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Chao-Hong Tan, Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling, Can Xu, Huang Hu, Xiubo Geng, Daxin Jiang

Mitigating Reversal Curse in Large Language Models via Semantic-aware Permutation Training (2024.findings-acl)

Challenge:	Generating natural and informative texts has been a long-standing problem in NLP.
Approach:	They propose to augment TExt Generation via Task-specific and Open-world Knowledge in a unified framework.
Outcome:	The proposed model can learn what and how to generate on two text generation tasks.

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Qingyan Guo, Rui Wang, Junliang Guo, Xu Tan, Jiang Bian, Yujiu Yang

Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training (2024.emnlp-main)

Challenge:	Large language models (LLMs) have achieved impressive performance across diverse tasks, but suffer from the "reversal curse" this limitation poses a challenge to the advancement of artificial general intelligence (AGI)
Approach:	They propose to use training data to permute training sentences into entities and feed them into the model.
Outcome:	The proposed method improves the performance of large language models (LLMs) on reversed questions and improves existing models.

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Zhou Zhang, Dongzeng Tan, Jiaan Wang, Yilong Chen, Jiarong Xu

Mitigating Cultural Bias in LLMs via Multi-Agent Cultural Debate (2026.findings-acl)

Challenge:	Emojis have gained immense popularity on social media platforms, serving as a common means to supplement or replace text.
Approach:	They propose a graph pre-train framework for text and emoji co-modeling that incorporates two tasks: node-level graph contrastive learning and edge-level link reconstruction learning.
Outcome:	The proposed framework improves on the Xiaohongshu and Twitter datasets with two types of downstream tasks.

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Qian Tan, Lei Jiang, Yuting Zeng, Shuoyang Ding, Xiaohua Xu

DiffusionNER: Boundary Diffusion for Named Entity Recognition (2023.acl-long)

Challenge:	Existing approaches to evaluate large language models fail to address cultural bias in non-Western languages . Chinese prompting shifts bias toward East Asian perspectives rather than eliminating it, authors say .
Approach:	They propose a Chinese–English bilingual benchmark and multi-agent vote frameworks that enable explicit "no bias" judgments.
Outcome:	The proposed framework achieves 57.6% average No Bias Rate on Chinese-English benchmark and 86.0% on Arabic CAMeL benchmark.

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Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Yueting Zhuang

ProxyQA: An Alternative Framework for Evaluating Long-Form Text Generation with Large Language Models (2024.acl-long)

Challenge:	Named Entity Recognition (NER) tasks are fundamental to many structured information extraction tasks.
Approach:	They propose a named entity recognition task that uses a boundary-denoising diffusion process to denoise noisy spans.
Outcome:	The proposed method achieves comparable or even better performance than previous state-of-the-art models on flat and nested datasets.

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Haochen Tan, Zhijiang Guo, Zhan Shi, Lu Xu, Zhili Liu, Yunlong Feng, Xiaoguang Li, Yasheng Wang, Lifeng Shang, Qun Liu, Linqi Song

V-Oracle: Making Progressive Reasoning in Deciphering Oracle Bones for You and Me (2025.acl-long)

Challenge:	Existing evaluation methods for large language models are labor-intensive and lack efficiency.
Approach:	They propose a framework dedicated to assessing long-text generation that includes in-depth human-curated meta-questions spanning various domains . they use a set of proxy-quests with pre-annotated answers to assess the content's quality by incorporating the generated texts as contextual background.
Outcome:	The proposed framework assesses the quality of long-text content by matching it with references through human evaluation or automated metrics.

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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Jiapeng Wang, YiFan Zhang, Zhuoma GongQue, Chong Sun, Yida Xu, Yadong Xue, Ye Tian, Zhimin Bao, Lan Yang, Chen Li, Honggang Zhang

PyramidCodec: Hierarchical Codec for Long-form Music Generation in Audio Domain (2024.findings-emnlp)

Challenge:	Deciphering oracle bone scripts using AI technology is not an overnight task due to the evolution of written language over millennia.
Approach:	They propose a framework that utilizes Large Multi-modal Models (LMMs) for interpreting Oracle Bone Script (OBS).
Outcome:	The proposed framework provides quantitative analyses and superior deciphering capability.

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Jianyi Chen, Zheqi Dai, Zhen Ye, Xu Tan, Qifeng Liu, Yike Guo, Wei Xue

Speech & Audio in NLP

Challenge:	Existing approaches to generate long music are inefficient and lack of structured representation.
Approach:	They propose a hierarchical discrete representation of audio for long audio-domain music generation using residual vector quantization on different levels of features.
Outcome:	The proposed method achieves competitive performance in terms of reconstruction quality and token per second (TPS) the proposed method facilitates training a language model that can generate well-structured long-form music for up to 3 minutes.

Altogether: Image Captioning via Re-aligning Alt-text (2024.emnlp-main)

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Hu Xu, Po-Yao Huang, Xiaoqing Tan, Ching-Feng Yeh, Jacob Kahn, Christine Jou, Gargi Ghosh, Omer Levy, Luke Zettlemoyer, Wen-tau Yih, Shang-Wen Li, Saining Xie, Christoph Feichtenhofer

On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)

Challenge:	Existing captioning models ignore existing alt-text metadata and lack transparency if training data is unknown.
Approach:	They propose an approach to edit and re-align alt-texts associated with images using human annotation.
Outcome:	The proposed approach improves image captions and improves text-to-image generation and zero-shot image classification tasks.

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Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie

Empowering Diffusion Models on the Embedding Space for Text Generation (2024.naacl-long)

Challenge:	under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors.
Approach:	They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients .
Outcome:	The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets.

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Zhujin Gao, Junliang Guo, Xu Tan, Yongxin Zhu, Fang Zhang, Jiang Bian, Linli Xu

Representation Learning

Challenge:	Recent work adapts diffusion models to textual data by diffusing on the embedding space.
Approach:	They propose an embedding diffusion model based on Transformer to solve the problem of embeddable space and denoising model.
Outcome:	The proposed model is more efficient than previous methods on seminal text generation tasks and is superior to existing models.

From Feedback to Checklists: Grounded Evaluation of AI-Generated Clinical Notes (2025.emnlp-industry)

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Karen Zhou, John Michael Giorgi, Pranav Mani, Peng Xu, Davis Liang, Chenhao Tan

Challenge:	Existing automated metrics fail to align with real-world physician preferences.
Approach:	They propose a pipeline that distills real user feedback into structured checklists for note evaluation that are interpretable, grounded in human feedback, and enforceable by LLM-based evaluators.
Outcome:	The proposed checklist outperforms baseline evaluations in coverage, diversity, and predictive power for human ratings.

Double Path Networks for Sequence to Sequence Learning (C18-1)

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Kaitao Song, Xu Tan, Di He, Jianfeng Lu, Tao Qin, Tie-Yan Liu

SaFER: A Robust and Efficient Framework for Fine-tuning BERT-based Classifier with Noisy Labels (2023.acl-industry)

Challenge:	Existing approaches for Sequence to Sequence learning have been developed . convolutional neural networks and self-attention networks are the most popular .
Approach:	They propose to integrate convolutional and self-attention layers into a double path network for sequence to sequence learning.
Outcome:	The proposed method significantly improves performance over state-of-the-art systems.

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Zhenting Qi, Xiaoyu Tan, Chao Qu, Yinghui Xu, Yuan Qi

EASYTOOL: Enhancing LLM-based Agents with Concise Tool Instruction (2025.naacl-long)

Challenge:	Existing noise-handling methods could not improve performance of BERT on noisy datasets . existing methods could only improve performance on noisy data, authors say .
Approach:	They propose a fine-tuning framework for BERT-based text classifiers that combats label noises without access to clean data for training or validation.
Outcome:	The proposed framework achieves superior performance on multiple text classification benchmarks.

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Siyu Yuan, Kaitao Song, Jiangjie Chen, Xu Tan, Yongliang Shen, Kan Ren, Dongsheng Li, Deqing Yang

A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation (2022.naacl-main)

Challenge:	EASYTOOL combines tools from diverse tool documentation into a single tool instruction.
Approach:	They propose a framework that transforms tool documentation into a unified tool instruction.
Outcome:	EASYTOOL combines extensive tool documentation into a concise tool instruction . it reduces token consumption and improves performance of LLM-based agents .

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Kexun Zhang, Rui Wang, Xu Tan, Junliang Guo, Yi Ren, Tao Qin, Tie-Yan Liu

Multilinguality

Challenge:	Non-autoregressive translation models suffer from the multi-modality problem when a source sentence corresponds to multiple correct translations.
Approach:	They propose to decompose the syntactic multi-modality problem into short- and long-range models and evaluate them on synthesized and real datasets.
Outcome:	The proposed loss functions can handle short- and long-range syntactic multi-modalities better than existing models.

REACT: Representation Extraction And Controllable Tuning to Overcome Overfitting in LLM Knowledge Editing (2025.emnlp-main)

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Haitian Zhong, Yuhuan Liu, Ziyang Xu, Guofan Liu, Qiang Liu, Shu Wu, Zhe Zhao, Liang Wang, Tieniu Tan

Mask the Correct Tokens: An Embarrassingly Simple Approach for Error Correction (2022.emnlp-main)

Challenge:	Large language model editing methods suffer from overfitting, where factual updates can propagate beyond their intended scope, overemphasizing the edited target even when it’s contextually inappropriate.
Approach:	They propose a framework for precise and controllable knowledge editing that utilizes two-phase representations and a linear transformation to compute a directional "belief shift" vector.
Outcome:	The proposed framework significantly reduces overfitting across nearly all evaluation metrics and on COUNTERFACT and MQuAKE.

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Kai Shen, Yichong Leng, Xu Tan, Siliang Tang, Yuan Zhang, Wenjie Liu, Edward Lin

Parallel Instance Query Network for Named Entity Recognition (2022.acl-long)

Challenge:	Text error correction methods usually use the source (incorrect) sentence as encoder input and generate the target (correct) sentences through the decoder.
Approach:	They propose a method to correct errors in text sequences by randomly masking out the correct tokens in the source sentence.
Outcome:	The proposed method improves accuracy on Mandarin and English datasets with autoregressive and non-autoregressive generation models.

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Yongliang Shen, Xiaobin Wang, Zeqi Tan, Guangwei Xu, Pengjun Xie, Fei Huang, Weiming Lu, Yueting Zhuang

Knowledge Representation

Challenge:	Named entity recognition is a fundamental task in natural language processing.
Approach:	They propose a method that sets up global and learnable instance queries to extract entities from a sentence in a parallel manner.
Outcome:	The proposed method outperforms existing state-of-the-art models on nested and flat datasets.

Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles (2024.findings-naacl)

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Weiting Tan, Haoran Xu, Lingfeng Shen, Shuyue Stella Li, Kenton Murray, Philipp Koehn, Benjamin Van Durme, Yunmo Chen

HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning (2025.emnlp-main)

Challenge:	Recent work shows that large language models can generalize to machine translation using zero-shot examples with in-context learning.
Approach:	They investigate the factors contributing to this gap by matching the writing styles of the target corpus.
Outcome:	The proposed methods can be enhanced without the need for parallel demonstration examples.

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Xingyu Tan, Xiaoyang Wang, Qing Liu, Xiwei Xu, Xin Yuan, Liming Zhu, Wenjie Zhang

Multilingual Neural Machine Translation with Language Clustering (D19-1)

Challenge:	Current RAG system retrieves evidence from knowledge graphs and text documents but has limitations in multi-hop reasoning, multi-entity questions, and source verification.
Approach:	They propose a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in large language models.
Outcome:	The proposed framework outperforms the current hybrid model-based model-driven system by 20.3% and 30.1% on seven benchmark datasets.

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Xu Tan, Jiale Chen, Di He, Yingce Xia, Tao Qin, Tie-Yan Liu

Multilinguality

Challenge:	Existing work on multilingual neural machine translation has been neglected due to its burdensome training process.
Approach:	They develop a framework that clusters languages into different groups and trains one multilingual model for each cluster.
Outcome:	The proposed model reduces the cost of training and improves translation accuracy.

Adaptive Data Flywheel: Applying MAPE Control Loops to AI Agent Improvement (2026.eacl-industry)

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Aaditya Shukla, Sidney Knowles, Meenakshi Madugula, David Farris, Ryan Angilly, Santiago Pombo, Lu An, Anbang Xu, Abhinav Balasubramanian, Tan Yu, Jiaxiang Ren, Rama Akkiraju

Robustness in NLP

Challenge:	NVInfo AI is a generative AI agent that can be deployed in production without full-scale retraining or infrastructure overhauls.
Approach:	They propose to implement a retrieval-augmented generation (RAG)-driven data flywheel in NVInfo AI, a mixture-of-experts knowledge assistant, for 30,000 employees.
Outcome:	The proposed system addresses failures in retrieval-augmented generation pipelines and enables continuous learning.

Taming the Real-world Complexities in CPT E/M Coding with Large Language Models (2025.emnlp-industry)

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Islam Nassar, Yang Lin, Yuan Jin, Rongxin Zhu, Chang Wei Tan, Zenan Zhai, Nitika Mathur, Thanh Tien Vu, Xu Zhong, Long Duong, Yuan-Fang Li

Relaxing the Constraints: A Dual-Importance Projection Mechanism for Lifelong Model Editing (2026.findings-acl)

Challenge:	Evaluation and Management (E/M) coding is performed by physicians and trained human coders who review clinical encounter notes and electronic health record data to assign appropriate codes.
Approach:	They propose a framework that automates evaluation and management coding tasks using the Current Procedural Terminology (CPT) taxonomy.
Outcome:	The proposed framework achieves an increase in coding accuracy of more than 36% over a commercial CPT E/M coding system and almost 5% over our strongest single-prompt baseline.

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Zhenghai Chen, Senbin Xu, Jiaxi Tan, Xinhua Wu, Yan Zhang, Xiawu Zheng, Shengchuan Zhang, Ke Li, Sicheng Zhao, Liujuan Cao, Rongrong Ji

MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training (2021.findings-acl)

Challenge:	Existing knowledge editing methods rely on strict orthogonal projection to preserve previously edited knowledge, but this constraint limits gradient expressiveness, resulting in degradation of model generalization and overall performance as the number of edits increases.
Approach:	They propose a method that leverages Singular Value Decomposition to identify critical gradient subspaces and introduces a dual mechanism comprising "accumulated importance" and "projection importance"
Outcome:	Extensive experiments on five mainstream LLMs show that the proposed method achieves an average comprehensive performance improvement of 10.36% and effectively maintains the model’s general capabilities on downstream tasks.

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Mingliang Zeng, Xu Tan, Rui Wang, Zeqian Ju, Tao Qin, Tie-Yan Liu

Upsample or Upweight? Balanced Training on Heavily Imbalanced Datasets (2025.naacl-long)

Challenge:	Symbolic music understanding is useful for many music applications, but lack of training data hinders representation learning.
Approach:	They propose a pre-trained model for music understanding that uses symbolic music data to train music representations.
Outcome:	The proposed model improves on four music understanding tasks.

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Tianjian Li, Haoran Xu, Weiting Tan, Kenton Murray, Daniel Khashabi

MoZIP: A Multilingual Benchmark to Evaluate Large Language Models in Intellectual Property (2024.lrec-main)

Challenge:	a lack of data across domains creates significant imbalances in training data sizes . a recent study shows that temperature sampling and scaling are equivalent but differ under stochastic gradient descent due to differences in gradient variance.
Approach:	They propose a method that upsamples low-resource languages and upweights their loss functions to address this disparity.
Outcome:	The proposed method competes effectively with existing data re-weighting techniques while offering computational efficiency.

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Shiwen Ni, Minghuan Tan, Yuelin Bai, Fuqiang Niu, Min Yang, Bowen Zhang, Ruifeng Xu, Xiaojun Chen, Chengming Li, Xiping Hu

Natural Language Interfaces

Challenge:	Large language models (LLMs) have demonstrated impressive performance in various natural language processing tasks.
Approach:	They propose a benchmark for the evaluation of large language models in the IP domain . they also propose supervised multilingual large language model called MoZi .
Outcome:	The proposed model outperforms four well-known LLMs on the MoZIP benchmark . the most powerful ChatGPT does not reach the passing level .

Beyond Itinerary Planning—A Real-World Benchmark for Multi-Turn and Tool-Using Travel Tasks (2026.acl-long)

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Xiang Cheng, Yulan Hu, Xiangwen Zhang, Lu Xu, Lide Tan, Zheng Pan, Xin Li, Yong Liu

xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)

Challenge:	Existing studies on LLM performance on travel planning have shown that existing settings are limited due to limited domain coverage, insufficient modeling of users’ implicit preferences in multi-turn conversations, and a lack of evaluation of agents’ capability boundaries.
Approach:	They propose a benchmark to evaluate LLMs' planning and tool-use abilities in real-world settings by collecting user queries, user preferences, and tools from real scenarios.
Outcome:	The proposed benchmark evaluates agents' capabilities in real-world settings and shows that even advanced models exhibit imbalanced performance across different capabilities.

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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong

Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agents (2025.findings-emnlp)

Challenge:	Autonomous agents powered by large language models (LLMs) have attracted significant research interest, but there are few standards for developing specialized models for agent tasks.
Approach:	They propose a series of large action models with dense and mixture-of-expert architectures that unifies, augments, and synthesizes diverse datasets to enhance agent generalizability and performance.
Outcome:	The proposed models outperform GPT-4, Claude-3, and many other models in terms of tool use and outperformed GPT-based models on multiple agent ability benchmarks.

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Rui Xu, Mingyu Wang, Xintao Wang, Dakuan Lu, Xiaoyu Tan, Wei Chu, Xu Yinghui

Reasoning

Challenge:	Recent advances in Large Language Models (LLMs)-based Role-Playing Language Agents (RPLAs) have attracted broad attention in various applications.
Approach:	They propose a benchmark for evaluating character thought generation using literature . they propose 'MIRROR' which generates character thoughts by retrieving memories, predicting character reactions, and synthesizing motivations.
Outcome:	The proposed benchmark outperforms existing methods in evaluating character thought generation.

CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)

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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen

Challenge:	a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages.
Approach:	They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models.
Outcome:	The proposed benchmarks show that the current models perform worse than the human ceiling.

HappyDB: A Corpus of 100,000 Crowdsourced Happy Moments (L18-1)

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Akari Asai, Sara Evensen, Behzad Golshan, Alon Halevy, Vivian Li, Andrei Lopatenko, Daniela Stepanov, Yoshihiko Suhara, Wang-Chiew Tan, Yinzhan Xu

Challenge:	Recent research has focused on developing technologies that help users incorporate the findings of the science of happiness into their daily lives.
Approach:	They crowd-sourced HappyDB, a corpus of 100,000 happy moments, and applied several state-of-the-art analysis techniques to analyze HappyDB.
Outcome:	The proposed technology can understand how people express their happy moments in text and analyze them using state-of-the-art techniques.

LLM×MapReduce: Simplified Long-Sequence Processing using Large Language Models (2025.acl-long)

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Zihan Zhou, Chong Li, Xinyi Chen, Shuo Wang, Yu Chao, Zhili Li, Haoyu Wang, Qi Shi, Zhixing Tan, Xu Han, Xiaodong Shi, Zhiyuan Liu, Maosong Sun

ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation (2022.acl-long)

Challenge:	Existing studies have focused on extending the context length of large language models (LLMs) due to their quadratic computational complexity and a lack of high-quality long training examples, most LLMs are trained with a limited window size.
Approach:	They propose a training-free framework that enables large language models to effectively process long texts using a divide-and-conquer strategy for comprehensive document understanding.
Outcome:	The proposed framework outperforms open-source and commercial long-context LLMs and is compatible with several models.

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Chang Liu, Xu Tan, Chongyang Tao, Zhenxin Fu, Dongyan Zhao, Tie-Yan Liu, Rui Yan

Joint Learning Event-Specific Probe and Argument Library with Differential Optimization for Document-Level Multi-Event Extraction (2025.findings-naacl)

Challenge:	generative dialogue models use dialogue histories to generate the response . however, generating a response based on the historical information is not easy .
Approach:	They propose a framework that utilizes simulated dialogue futures to enhance response generation.
Outcome:	The proposed framework can generate better responses over strong baselines on two open-domain dialogue datasets.

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Jianpeng Hu, Chao Xue, Chunqing Yu, JiaCheng Xu, Chengxiang Tan

ECO v1: Towards Event-Centric Opinion Mining (2022.findings-acl)

Challenge:	Existing methods for document-level multi-event extraction neglect the fine-grained difference between events in multi-documents, which leads to event confusion and missing.
Approach:	They propose an event-specific probe-based method to sniff multiple events by querying each corresponding argument library.
Outcome:	The proposed method outperforms the state-of-the-art method in the recall of multi-events.

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Ruoxi Xu, Hongyu Lin, Meng Liao, Xianpei Han, Jin Xu, Wei Tan, Yingfei Sun, Le Sun

Sentiment Analysis

Challenge:	Existing studies on event-centric opinion mining focus on entity-centric opinions . entity-centered opinions focus on sentimental polarity of events, while event-centered ones focus on content .
Approach:	They propose to perform event-centric opinion mining on event-argument structure and expression categorizing theory and benchmark it against a pioneer corpus.
Outcome:	The proposed task is feasible and challenging, and the results are beneficial for future studies.

Extract and Attend: Improving Entity Translation in Neural Machine Translation (2023.findings-acl)

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Zixin Zeng, Rui Wang, Yichong Leng, Junliang Guo, Shufang Xie, Xu Tan, Tao Qin, Tie-Yan Liu

Machine Translation

Challenge:	Existing methods to improve entity translation in Neural machine translation still suffer from inaccurate translation of entities due to the lack of entity training instances.
Approach:	They propose an extract-and-tend approach to enhance entity translation in NMT by extracting entities from a dictionary and attending to them with a prefix.
Outcome:	Experiments on En-Zh and En-Ru show that the proposed approach improves translation accuracy and translation quality.

The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts (2024.findings-acl)

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Lingfeng Shen, Weiting Tan, Sihao Chen, Yunmo Chen, Jingyu Zhang, Haoran Xu, Boyuan Zheng, Philipp Koehn, Daniel Khashabi

Closing the Loop: Learning to Generate Writing Feedback via Language Model Simulated Student Revisions (2024.emnlp-main)

Challenge:	Recent studies show that malicious prompt instructions could solicit objectionable content from LLMs.
Approach:	They compare how state-of-the-art LLMs respond to malicious prompts in different languages . they find that LLM's generate unsafe responses more often when a prompt is written in a lower-resource language .
Outcome:	The proposed model can generate unsafe responses more often when a malicious prompt is written in a lower-resource language, and less irrelevant responses when written in lower-source languages.

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Inderjeet Nair, Jiaye Tan, Xiaotian Su, Anne Gere, Xu Wang, Lu Wang

TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents (2026.findings-acl)

Challenge:	Recent advances in language models (LMs) have made it possible to automatically generate feedback that is actionable and well-aligned with human-specified attributes.
Approach:	They propose a tool that PROduces Feedback via learning from LM simulated student revisions and propose to iteratively optimize the feedback generator by directly maximizing the effectiveness of students’ overall revising performance.
Outcome:	The proposed approach surpasses baseline methods in effectiveness of improving students’ writing and demonstrates enhanced pedagogical values, even though it was not explicitly trained for this aspect.

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Kai Li, Xuanqing Yu, Ziyi Ni, Yi Zeng, Yao Xu, Zheqing Zhang, Xin Li, Jitao Sang, Xiaogang Duan, Xuelei Wang, Chengbao Liu, Jie Tan

Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents (2026.acl-long)

Challenge:	Existing memory frameworks provide limited support for temporally structured information across hierarchical levels, leading to fragmented memories and unstable long-horizon personalization.
Approach:	They propose a temporal–hierarchical memory framework that organizes conversations through a Temporal Memory Tree.
Outcome:	The proposed framework outperforms baselines while reducing the recalled memory length by 52.20%.

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Minzheng Wang, Run Luo, Yanbo Wang, Zichen Liu, Yuqiao Tan, Tao Tan, Nan Xu, Lu Wang, Wenji Mao

Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems (2025.emnlp-main)

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) is effective for closed-ended tasks, but it is not applicable to open-ended social language games.
Approach:	They propose a method that uses a time-scaled evolutionary perception mechanism to detect impasse by quantifying dual-scale value baseline divergence alongside match entropy.
Outcome:	Experiments on multiple social language games show that the proposed method outperforms baselines and avoids policy degeneration.

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Xu Shen, Yixin Liu, Yiwei Dai, Yili Wang, Rui Miao, Yue Tan, Shirui Pan, Xin Wang