Papers by Shuo Sun

WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia (2021.eacl-main)

Challenge:	MERaLiON-AudioLLM is the first general-purpose audio-based large language model for multitask learning.
Approach:	They introduce MERaLiON-AudioLLM, a general-purpose audio-based large language model for multitask learning with a focus on Singlish understanding.
Outcome:	The proposed model exhibits strong generalization across a diverse set of tasks . it is a leading solution for region-specific AI applications.

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Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong, Francisco Guzmán

Machine Translation

Challenge:	a new approach to extract parallel sentences from Wikipedia articles is proposed . the approach is based on multilingual sentence embeddings, but does not limit it to English .
Approach:	They propose to automatically extract parallel sentences from Wikipedia articles in 96 languages . they train neural MT baseline systems on the mined data and evaluate them on the TED corpus .
Outcome:	The proposed approach extracts parallel sentences from Wikipedia articles in 96 languages . the extracted sentences achieve strong BLEU scores for many language pairs .

RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework (2025.acl-long)

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Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

Text Generation

Challenge:	Existing evaluation metrics for RAG systems are lacking due to high costs of data construction and lack of factual accuracy.
Approach:	They propose a framework to evaluate RAG systems in specialized scenarios . they propose three new metrics to evaluate LLM-generated responses .
Outcome:	The proposed framework outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples.

CLIReval: Evaluating Machine Translation as a Cross-Lingual Information Retrieval Task (2020.acl-demos)

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Shuo Sun, Suzanna Sia, Kevin Duh

On LLM-Based Scientific Inductive Reasoning Beyond Equations (2025.emnlp-main)

Challenge:	evaluating machine translation (MT) with cross-lingual information retrieval is relatively time-consuming and subjective.
Approach:	They propose a toolkit that evaluates machine translation with a proxy task of cross-lingual information retrieval.
Outcome:	The proposed toolkit is based on the "metrics shared task" of WMT2019.

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Brian S. Lin, Jiaxin Yuan, Zihan Zhou, Shouli Wang, Shuo Wang, Cunliang Kong, Qi Shi, Yuxuan Li, Liner Yang, Zhiyuan Liu, Maosong Sun

Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering (2024.acl-long)

Challenge:	Existing research on inductive reasoning models emphasizes rule design without grounding them in specific scenarios.
Approach:	They propose to use LLMs to learn underlying patterns from limited examples in entirely new environments.
Outcome:	The proposed benchmark evaluates the inductive reasoning abilities of large language models in scientific settings.

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Zhengliang Shi, Shuo Zhang, Weiwei Sun, Shen Gao, Pengjie Ren, Zhumin Chen, Zhaochun Ren

Information Retrieval

Challenge:	Existing approaches to solve multi-hop question are constrained by the retriever and the noise in the retrieved documents.
Approach:	They propose a framework that integrates parametric knowledge of large language models with external documents to solve a multi-hop question.
Outcome:	The proposed framework is based on the parametric knowledge of LLMs and external documents to solve a multi-hop question.

AudioBench: A Universal Benchmark for Audio Large Language Models (2025.naacl-long)

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Bin Wang, Xunlong Zou, Geyu Lin, Shuo Sun, Zhuohan Liu, Wenyu Zhang, Zhengyuan Liu, AiTi Aw, Nancy F. Chen

Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications (2021.emnlp-main)

Challenge:	Existing evaluation regimes for audio large language models do not cover the breadth of their possible use cases.
Approach:	They propose to use AudioBench to evaluate audio large language models . they found that no single model excels consistently across all tasks .
Outcome:	The proposed evaluation targets speech understanding, audio scene understanding, and voice understanding (paralinguistic) . no single model excels consistently across all tasks, the paper found .

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Shuo Sun, Ahmed El-Kishky, Vishrav Chaudhary, James Cross, Lucia Specia, Francisco Guzmán

Information Retrieval

Challenge:	Sentence-level Quality estimation (QE) is traditionally a regression task . but large multilingual contextualized language models are expensive and infeasible for real-world applications.
Approach:	They evaluate several model compression techniques for QE and find they are inefficient . they argue that a full model parameterization is required to achieve SoTA results .
Outcome:	The proposed models are poorly expressive in a regression task, the authors argue . they show that reframing QE as a classification problem and evaluating models would improve their performance in real-world applications.

MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset (2022.lrec-1)

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Marina Fomicheva, Shuo Sun, Erick Fonseca, Chrysoula Zerva, Frédéric Blain, Vishrav Chaudhary, Francisco Guzmán, Nina Lopatina, Lucia Specia, André F. T. Martins

LoRA-Flow: Dynamic LoRA Fusion for Large Language Models in Generative Tasks (2024.acl-long)

Challenge:	Existing datasets for machine translation quality estimation and post-editing have several shortcomings.
Approach:	They propose a dataset for machine translation quality estimation and automatic post-editing . they report the performance of baseline systems trained on the MLQE-PE dataset .
Outcome:	The proposed dataset contains human labels for up to 10,000 translations per language pair.

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Hanqing Wang, Bowen Ping, Shuo Wang, Xu Han, Yun Chen, Zhiyuan Liu, Maosong Sun

If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs (2026.acl-long)

Challenge:	LoRA-Flow uses lightweight modules to customize large language models for downstream tasks . previous work on LoRA combination relied on task-level weights for each involved LoRA .
Approach:	They propose a LoRA-Flow approach that uses dynamic weights to adjust the impact of different LoRAs.
Outcome:	The proposed method outperforms baselines with task-level weights on six generative tasks.

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Siqi Fan, Xiusheng Huang, Yiqun Yao, Xuezhi Fang, Kang Liu, Peng Han, Shuo Shang, Aixin Sun, Yequan Wang

Challenge:	Existing benchmarks for large language models (LLMs) fail to capture these dynamics, focusing on static, open-ended evaluations.
Approach:	They propose a benchmark to assess lifelong learning in large language models . they use two episodic datasets rich in narrative structure and character interactions .
Outcome:	Experiments on LLMs show that non-parametric methods outperform parametric ones in managing stateful learning.

Soft Syntactic Reinforcement for Neural Event Extraction (2025.naacl-long)

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Anran Hao, Jian Su, Shuo Sun, Teo Yong Sen

Syntactic Text Processing

Challenge:	Recent event extraction methods rely on pre-trained language models but still suffer from errors due to a lack of syntactic knowledge.
Approach:	They propose a method to incorporate syntactic information into PLM-based models for event extraction (EE) this method uses a standard dependency corpus to select syntax-related dimensions of the model's representation.
Outcome:	The proposed method outperforms baseline models and existing syntactic reinforcement methods on sentence-level and document-level EE benchmark datasets.

LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System (2025.emnlp-demos)

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Yu Chao, Siyu Lin, Xiaorong Wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, Jie Zhou, Zhiyuan Liu, Maosong Sun

Challenge:	Generating high-quality long-form survey articles poses significant challenges to AI Agent systems.
Approach:	They propose a hierarchically modular agent system for long-form survey generation . they use atomic models to implement skeleton initialization, digest construction, and skelet refinement . human evaluations demonstrate system surpasses representative baselines .
Outcome:	The proposed system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning.

Improving Back-Translation with Uncertainty-based Confidence Estimation (D19-1)

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Shuo Wang, Yang Liu, Chao Wang, Huanbo Luan, Maosong Sun

Battle of the Large Language Models: Dolly vs LLaMA vs Vicuna vs Guanaco vs Bard vs ChatGPT - A Text-to-SQL Parsing Comparison (2023.findings-emnlp)

Challenge:	Despite the success of low-resource neural machine translation, there is a data scarcity problem in many languages . large-scale, high-quality, and widecoverage bilingual corpora do not exist for most language pairs .
Approach:	They propose to quantify confidence of NMT models based on model uncertainty . they propose to use uncertainty-based confidence measures to improve back-translation .
Outcome:	The proposed model outperforms conventional statistical machine translation (SMT) on Chinese-English and English-German translation tasks.

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Shuo Sun, Yuchen Zhang, Jiahuan Yan, Yuze Gao, Donovan Ong, Bin Chen, Jian Su

Programming Languages in NLP

Challenge:	a number of open-source large language models claim to be performing better than commercial ones . however, these models fall short of the performance achieved by closed-source models like GPT-3.5 .
Approach:	They evaluate six popular large language models against each other to evaluate their performance . authors say open-source models are not as effective as those built by commercial models .
Outcome:	a new set of models claim to match or surpass the language understanding abilities of commercial models . the results show that the models performed far below the performance of closed-source models compared to open-source ones .

Revealing the Attention Floating Mechanism in Masked Diffusion Models (2026.findings-acl)

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Xin Dai, Pengcheng Huang, Zhenghao Liu, Shuo Wang, Yukun Yan, Chaojun Xiao, Yu Gu, Ge Yu, Maosong Sun

ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code Generation (2025.acl-long)

Challenge:	Masked diffusion models (MDMs) leverage bidirectional attention and a denoising process.
Approach:	They investigate the attention behaviors of Masked diffusion models by revealing the phenomenon of Attention Floating.
Outcome:	The proposed model doubles the performance of autoregressive models in knowledge-intensive tasks.

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Xuanle Zhao, Xianzhen Luo, Qi Shi, Chi Chen, Shuo Wang, Zhiyuan Liu, Maosong Sun

KBAlign: Efficient Self Adaptation on Specific Textual Knowledge Bases (2025.findings-emnlp)

Challenge:	Existing open-source MLLMs fail to fully capture dense information embedded in charts . current models still face significant challenges in understanding and analyzing visual tasks such as captioning and question answering.
Approach:	They propose a chart-to-code MLLM which leverages Code LLMs as the language backbone to enhance the executability of the generated code.
Outcome:	The proposed model surpasses existing open-source models on chart-to-code benchmarks with only 7B parameters and provides lossless representations that contain all critical details.

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Zheni Zeng, Yuxuan Chen, Shi Yu, Ruobing Wang, Yukun Yan, Zhenghao Liu, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun

Semantic Text Processing

Challenge:	Existing methods for retrieval-augmented generation (RAG) are limited and fine-tuning incurs prohibitive costs of external signals.
Approach:	They propose a self-supervised framework that enhances RAG systems through efficient model adaptation.
Outcome:	The proposed framework achieves 90% of the performance gain obtained through GPT-4-supervised adaptation while relying entirely on self-annotation of much smaller models.

Are we Estimating or Guesstimating Translation Quality? (2020.acl-main)

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Shuo Sun, Francisco Guzmán, Lucia Specia

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

Challenge:	A carefully engineered ensemble of pre-trained multilingual language models won the QE shared task at WMT19.
Approach:	They propose to use pre-trained multilingual language models to train quality estimation for machine translation.
Outcome:	A carefully engineered ensemble of pre-trained language models wins the QE shared task at WMT19.

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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

Responsible & Trustworthy NLP

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 .

RADE: Reference-Assisted Dialogue Evaluation for Open-Domain Dialogue (2023.acl-long)

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Zhengliang Shi, Weiwei Sun, Shuo Zhang, Zhen Zhang, Pengjie Ren, Zhaochun Ren

Dialogue Systems & Conversational Agents

Challenge:	Evaluating open-domain dialogue systems is challenging because of the one-to-many problem.
Approach:	They propose a reference-based dialogue evaluation approach that leverages the pre-created utterance as reference other than the gold response to relieve the one-to-many problem.
Outcome:	The proposed method outperforms state-of-the-art evaluation methods on three datasets and two existing benchmarks.

AfriCLIRMatrix: Enabling Cross-Lingual Information Retrieval for African Languages (2022.emnlp-main)

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Odunayo Ogundepo, Xinyu Zhang, Shuo Sun, Kevin Duh, Jimmy Lin

An Exploratory Study on Multilingual Quality Estimation (2020.aacl-main)

Challenge:	Existing datasets for cross-lingual information retrieval are limited in many languages, especially those spoken in Africa.
Approach:	They propose to build a test collection for cross-lingual information retrieval in 15 diverse African languages.
Outcome:	AfriCLIRMatrix contains 6 million queries in English and 23 million relevance judgments automatically mined from Wikipedia inter-language links, covering many more African languages than any existing information retrieval test collection.

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Shuo Sun, Marina Fomicheva, Frédéric Blain, Vishrav Chaudhary, Ahmed El-Kishky, Adithya Renduchintala, Francisco Guzmán, Lucia Specia

Plum: Prompt Learning using Metaheuristics (2024.findings-acl)

Challenge:	Existing approaches to predict the quality of machine translation use language-specific models, but they lack labelled data for each language pair.
Approach:	They propose to use scores from translation models to estimate quality of machine translations by predicting the quality of a translation at test time.
Outcome:	The proposed models outperform single-language models in less balanced quality label distributions and low-resource settings.

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Rui Pan, Shuo Xing, Shizhe Diao, Wenhe Sun, Xiang Liu, KaShun Shum, Jipeng Zhang, Renjie Pi, Tong Zhang

Low-Resource NLP

Challenge:	Recent advances in prompt learning have led to a need for general prompt optimization methods.
Approach:	They propose a branch of discrete non-convex optimization methods with over 100 options as a promising approach to prompt learning.
Outcome:	The proposed methods can be used to discover more human-understandable prompts that were previously unknown in reasoning and image generation tasks.

From Unaligned to Aligned: Scaling Multilingual LLMs with Multi-Way Parallel Corpora (2025.emnlp-main)

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Yingli Shen, Wen Lai, Shuo Wang, Ge Gao, Kangyang Luo, Alexander Fraser, Maosong Sun

Can Large Language Models Translate Unseen Languages in Underrepresented Scripts? (2025.emnlp-main)

Challenge:	Experiments show that models trained on multi-way parallel data outperform those trained on unaligned data.
Approach:	They propose a large-scale, high-quality multi-way parallel corpus based on TED Talks that spans 113 languages with up to 50 languages aligned in parallel.
Outcome:	The proposed model outperforms models trained on unaligned multilingual data on six multilingual benchmarks.

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Dianqing Lin, null Aruukhan, Hongxu Hou, Shuo Sun, Wei Chen, Yichen Yang, Guo Dong Shi

Machine Translation

Challenge:	Large language models (LLMs) have demonstrated impressive performance in machine translation, but struggle with unseen low-resource languages.
Approach:	They propose a benchmark to evaluate translation for Mongolian and Yi using linguistic resources.
Outcome:	The proposed model can translate Mongolian (in traditional script) and Yi with the help of linguistic resources, but is limited in its ability to handle these languages effectively.

DeepNote: Note-Centric Deep Retrieval-Augmented Generation (2025.findings-emnlp)

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Ruobing Wang, Qingfei Zhao, Yukun Yan, Daren Zha, Yuxuan Chen, Shi Yu, Zhenghao Liu, Yixuan Wang, Shuo Wang, Xu Han, Zhiyuan Liu, Maosong Sun

Information Retrieval

Challenge:	. - (EN)
Approach:	. - (EN)
Outcome:	. - (EN)

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

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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

DetermLR: Augmenting LLM-based Logical Reasoning from Indeterminacy to Determinacy (2024.acl-long)

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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Hongda Sun, Weikai Xu, Wei Liu, Jian Luan, Bin Wang, Shuo Shang, Ji-Rong Wen, Rui Yan

MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization (2026.findings-acl)

Challenge:	Recent advances in large language models (LLMs) have revolutionized the landscape of reasoning tasks.
Approach:	They propose a new approach that rethinks the reasoning process as an evolution from indeterminacy to determinacy.
Outcome:	The proposed model surpasses all baselines on various logical reasoning benchmarks.

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Haidong Xin, Xinze Li, Zhenghao Liu, Yukun Yan, Shuo Wang, Cheng Yang, Yu Gu, Ge Yu, Maosong Sun

AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage (2026.acl-long)

Challenge:	Existing memory systems can support long-horizon human-LLM interactions by persisting historical interactions beyond limited context windows.
Approach:	They propose a framework that augments memory systems with a self-evolving meta-memory . meta-meso is iteratively distilling transferable knowledge utilization experiences . results show MetaMem outperforms strong baselines by over 3.6% .
Outcome:	The proposed framework outperforms baselines by over 3.6% in the long-horizon human-LLM interaction.

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Xuanle Zhao, Zilin Sang, Yuxuan Li, Qi Shi, Weilun Zhao, Shuo Wang, Duzhen Zhang, Xu Han, Zhiyuan Liu, Maosong Sun

Challenge:	Efficient reproduction of research papers requires deep domain expertise.
Approach:	They propose a framework that systematically mines implicit knowledge from the cited literature to reproduce experimental code in a complete, end-to-end manner.
Outcome:	The proposed framework surpasses baselines across all metrics and reproduces experimental code in a complete, end-to-end manner.

Unsupervised Quality Estimation for Neural Machine Translation (2020.tacl-1)

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Marina Fomicheva, Shuo Sun, Lisa Yankovskaya, Frédéric Blain, Francisco Guzmán, Mark Fishel, Nikolaos Aletras, Vishrav Chaudhary, Lucia Specia

Text Generation

Challenge:	Existing approaches require large amounts of expert annotated data, computation, and time for training.
Approach:	They propose an unsupervised approach to QE where no training is required . they use a dataset that enables work on both black-box and glass-box approaches .
Outcome:	The proposed approach rivals state-of-the-art supervised QE models in terms of correlation with human judgments of quality.

An Exploratory Study on Model Compression for Text-to-SQL (2023.findings-acl)

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Shuo Sun, Yuze Gao, Yuchen Zhang, Jian Su, Bin Chen, Yingzhan Lin, Shuqi Sun

Programming Languages in NLP

Challenge:	Text-to-SQL translates user queries into SQL statements that can retrieve relevant answers from relational databases.
Approach:	They propose to apply model compression techniques to sketch-based and sequence-to-sequence Text-toSQL models.
Outcome:	The proposed models have higher inference efficiency and respond better to model compression than sequence-to-sequence models.

Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization (2026.acl-long)

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Shaohua Duan, Pengcheng Huang, Xinze Li, Zhenghao Liu, Xiaoyuan Yi, Yukun Yan, Shuo Wang, Yu Gu, Ge Yu, Maosong Sun

Benchmarking Contextual and Paralinguistic Reasoning in Speech-LLMs: A Case Study with In-the-Wild Data (2025.findings-emnlp)

Challenge:	Recent studies have explored fine-tuning Large Language Models with synthetic data to enhance their long-context capabilities.
Approach:	They propose a framework that leverages a Multi-Armed Bandit rollout strategy to identify the most informative chunks from the given long context for sampling high-quality and diverse responses.
Outcome:	The proposed framework achieves 4% improvement on long-context reasoning benchmarks on Llama and Qwen.

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Qiongqiong Wang, Hardik Bhupendra Sailor, Tianchi Liu, Wenyu Zhang, Muhammad Huzaifah, Nattadaporn Lertcheva, Shuo Sun, Nancy F. Chen, Jinyang Wu, AiTi Aw

CheckRLM: Effective Knowledge–Thought Coherence Checking in Retrieval-Augmented Reasoning (2026.acl-long)

Challenge:	Recent speech-LLMs have shown impressive performance in tasks like transcription and translation, yet they remain limited in understanding the paralinguistic aspects of speech crucial for social and emotional intelligence.
Approach:	They propose a benchmark for evaluating speech-LLMs on contextual paralinguistic reasoning . the benchmark includes curated question answering datasets requiring both linguistic and empathetic understanding .
Outcome:	The proposed benchmark reveals a key gap in existing evaluations and offers insights into building more context-aware and emotionally intelligent LLMs.

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Dingling Xu, Ruobing Wang, Qingfei Zhao, Yukun Yan, Zhichun Wang, Daren Zha, Shi Yu, Zhenghao Liu, Shuo Wang, Xu Han, Maosong Sun

CLIRMatrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval (2020.emnlp-main)

Challenge:	Reasoning Language Models (RLMs) have improved performance on complex tasks by extending the reasoning chain, but they are prone to factual errors, especially in knowledge-intensive tasks.
Approach:	They propose a framework that improves the reliability of the reasoning process by timely checking and correcting factual errors.
Outcome:	The proposed framework outperforms baselines and shows that it mitigates error accumulation with lower costs.

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Shuo Sun, Kevin Duh

Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages (2024.acl-long)

Challenge:	Cross-Lingual Information Retrieval (CLIR) is a retrieval task in which search queries and candidate documents are written in different languages.
Approach:	They present a massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval extracted automatically from Wikipedia.
Outcome:	The proposed datasets are the largest and most comprehensive CLIR dataset to date.

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Yuanchi Zhang, Yile Wang, Zijun Liu, Shuo Wang, Xiaolong Wang, Peng Li, Maosong Sun, Yang Liu

Empirical Analysis of Decoding Biases in Masked Diffusion Models (2026.acl-long)

Challenge:	Contemporary large language models (LLMs) are pre-trained on multilingual corpora, but their performance lags behind in most languages compared to a few resource-rich languages.
Approach:	They propose a method that leverages the internal capabilities of large language models on resource-rich languages to enhance multilingual performance.
Outcome:	The proposed method improves multilingual performance while minimizing impact on original performance in resource-rich languages.

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Pengcheng Huang, Tianming Liu, Zhenghao Liu, Yukun Yan, Shuo Wang, Tong Xiao, Zulong Chen, Maosong Sun

Long-Chain Reasoning Distillation via Adaptive Prefix Alignment (2026.acl-long)

Challenge:	Existing MDMs employ uncertainty-based decoding strategies that limit their reasoning ability and ultimately degrade generation quality.
Approach:	They propose a framework that regularizes uncertainty-based decoding by incorporating two complementary priors to shape global decoding trajectories and promote content informativeness.
Outcome:	The proposed framework outperforms existing decoding strategies by more than 7% while achieving comparable performance to autoregressive models of similar parameter scales.

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Zhenghao Liu, Zhuoyang Wu, Xinze Li, Yukun Yan, Shuo Wang, Zulong Chen, Yu Gu, Ge Yu, Maosong Sun

On the Language Coverage Bias for Neural Machine Translation (2021.findings-acl)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, especially in solving complex mathematical problems.
Approach:	They propose a framework that exploits teacher CoTs for distillation through adaptive prefix alignment.
Outcome:	The proposed framework outperforms baseline models on multiple mathematical reasoning benchmarks by over 3%.

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Shuo Wang, Zhaopeng Tu, Zhixing Tan, Shuming Shi, Maosong Sun, Yang Liu

ManCC: A Task-Anchored Benchmark for Manchu–Classical Chinese Cross-Lingual Modeling (2026.findings-acl)

Challenge:	Language coverage bias is important for neural machine translation because of the target-original training data.
Approach:	They propose two approaches to alleviate the language coverage bias problem by explicitly distinguishing between the source-and target-original training data.
Outcome:	The proposed methods improve translation tasks on both back-and forward-translation and their tagged variants.

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Meiqi Wang, Xiaoxin Sun, Dongjie Wang, Ruixin Yu, Xiantao Heng, Shuo Wang, Zhen Huang, Peng Zhao, Suhua Wang, Minghao Yin

CoLLiE: Collaborative Training of Large Language Models in an Efficient Way (2023.emnlp-demo)

Challenge:	Mainstream research in natural language processing has focused on high-resource and modern languages.
Approach:	They propose a task-anchored benchmark for Manchu–Classical Chinese translation . they use a parallel corpus of 16,627 sentence pairs to evaluate the model .
Outcome:	The proposed benchmarks show that linguistic differences influence performance and broader language coverage facilitate low-resource transfer.

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Kai Lv, Shuo Zhang, Tianle Gu, Shuhao Xing, Jiawei Hong, Keyu Chen, Xiaoran Liu, Yuqing Yang, Honglin Guo, Tengxiao Liu, Yu Sun, Qipeng Guo, Hang Yan, Xipeng Qiu

Semantic Text Processing

Challenge:	Large language models (LLMs) are increasingly pivotal in a wide range of tasks . however, the resources required for training these models necessitate efficient solutions .
Approach:	They propose a library that facilitates collaborative training of large language models . they use 3D parallelism, parameter-efficient fine-tuning methods and optimizers .
Outcome:	The proposed library has proven superior training efficiency in comparison with prevalent solutions in pre-training and fine-tuning scenarios.

UltraLink: An Open-Source Knowledge-Enhanced Multilingual Supervised Fine-tuning Dataset (2024.acl-long)

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Haoyu Wang, Shuo Wang, Yukun Yan, Xujia Wang, Zhiyu Yang, Yuzhuang Xu, Zhenghao Liu, Liner Yang, Ning Ding, Xu Han, Zhiyuan Liu, Maosong Sun

Semantic Text Processing

Challenge:	Open-source large language models (LLMs) have gained strength across diverse fields, but the majority of studies focus on English.
Approach:	They propose a knowledge-grounded data augmentation approach to elicit more language-specific knowledge of LLMs by enhancing their ability to serve users from different countries.
Outcome:	The proposed method can prune the language-agnostic supervised fine-tuning dataset without any performance degradation.

SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages (2024.emnlp-main)

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Holy Lovenia, Rahmad Mahendra, Salsabil Akbar, Lester James Miranda, Jennifer Santoso, Elyanah Aco, Akhdan Fadhilah, Jonibek Mansurov, Joseph Marvin Imperial, Onno Kampman, Joel Moniz, Muhammad Habibi, Frederikus Hudi, Jann Montalan, Ryan Hadiwijaya, Joanito Lopo, William Nixon, Börje Karlsson, James Jaya, Ryandito Diandaru, Yuze Gao, Patrick Irawan, Bin Wang, Jan Christian Blaise Cruz, Chenxi Whitehouse, Ivan Parmonangan, Maria Khelli, Wenyu Zhang, Lucky Susanto, Reynard Ryanda, Sonny Hermawan, Dan Velasco, Muhammad Kautsar, Willy Hendria, Yasmin Moslem, Noah Flynn, Muhammad Adilazuarda, Haochen Li, Johanes Lee, R. Damanhuri, Shuo Sun, Muhammad Qorib, Amirbek Djanibekov, Wei Qi Leong, Quyet V. Do, Niklas Muennighoff, Tanrada Pansuwan, Ilham Firdausi Putra, Yan Xu, Tai Chia, Ayu Purwarianti, Sebastian Ruder, William Tjhi, Peerat Limkonchotiwat, Alham Aji, Sedrick Keh, Genta Winata, Ruochen Zhang, Fajri Koto, Zheng Xin Yong, Samuel Cahyawijaya

DongbaMIE: A Multimodal Information Extraction Dataset for Evaluating Semantic Understanding of Dongba Pictograms (2025.findings-emnlp)

Challenge:	Southeast Asia (SEA) is home to over 1,300 indigenous languages and 671 million people . prevailing AI models suffer from a significant lack of representation of texts, images, and audio datasets from SEA .
Approach:	They propose to provide a resource center that provides standardized corpora in nearly 1,000 SEA languages across three modalities.
Outcome:	a new benchmark assesses the quality of AI models on 36 SEA languages across 13 tasks . the results highlight the importance of SEA as a culturally diverse region .

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Xiaojun Bi, Shuo Li, Junyao Xing, Ziyue Wang, Fuwen Luo, Weizheng Qiao, Lu Han, Ziwei Sun, Peng Li, Yang Liu

Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations (2025.findings-emnlp)

Challenge:	Dongba pictographic is the only pictograph script still in use in the world.
Approach:	DongbaMIE is the first dataset focusing on multimodal information extraction of Dongbe pictographs.
Outcome:	The dataset contains 23,530 sentence-level and 2,539 paragraph-level high-quality text-image pairs.

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Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan, Yujiong Shen, Yi Lu, Zhiheng Xi, Yuming Yang, Wenming Tan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

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

Challenge:	Multimodal large language models have demonstrated remarkable performance in visual-language tasks, but their authenticity is often compromised by object hallucinations.
Approach:	They propose a multi-frequency perturbation method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference.
Outcome:	The proposed method significantly mitigates object hallucinations across various model architectures.

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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

A Template-based Method for Constrained Neural Machine Translation (2022.emnlp-main)

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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Shuo Wang, Peng Li, Zhixing Tan, Zhaopeng Tu, Maosong Sun, Yang Liu

Mitigating Linguistic Artifacts in Emotion Recognition for Conversations from TV Scripts to Daily Conversations (2024.lrec-main)

Challenge:	Existing methods to solve this problem can not satisfy the following three desiderata: (1) high translation quality, (2) high match accuracy, and (3) low latency.
Approach:	They propose a template-based method that can provide high translation quality and match accuracy and a low latency inference.
Outcome:	The proposed method outperforms baselines in lexically and structurally constrained translation tasks and can be used in a variety of applications.

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Donovan Ong, Shuo Sun, Jian Su, Bin Chen

Sentiment Analysis

Challenge:	Existing studies on Emotion Recognition in Conversations (ERC) focus on training and testing models on the same datasets and there is no prior work on adaptability.
Approach:	They propose to use contrastive learning to prioritize emotional features over a linguistic style and refining emotion predictions with pseudo-emotion intensity score to improve model's robustness and accuracy in diverse conversational contexts.
Outcome:	The proposed techniques reduce reliance on linguistic artifacts found in TV transcripts and improve model’s robustness and accuracy in diverse conversational contexts.

DNASpeech: A Contextualized and Situated Text-to-Speech Dataset with Dialogues, Narratives and Actions (2025.acl-long)

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Chuanqi Cheng, Hongda Sun, Bo Du, Shuo Shang, Xinrong Hu, Rui Yan

Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language Models (2025.emnlp-main)

Challenge:	Existing TTS datasets lack situated descriptive prompts aligned with speech data.
Approach:	They propose a contextualized and situated text-to-speech task to promote more accurate and customized speech generation using DNA prompts.
Outcome:	The proposed task promotes more accurate and customized speech generation using DNA prompts.

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Pu Jian, Junhong Wu, Wei Sun, Chen Wang, Shuo Ren, Jiajun Zhang