Papers by Yang Gu

MorphMark: Flexible Adaptive Watermarking for Large Language Models (2025.acl-long)

Challenge:	Multimodal Large Language Models (MLLMs) have shown promise in MER, but their internal decision-making mechanisms under modality conflict and missingness remain underexplored.
Approach:	They propose a multimodal large language model that can detect and control modality conflicts and missing subsets by a lightweight mechanism that detects and controls modality conflict.
Outcome:	The proposed framework improves performance across settings, showing it can handle conflict and missing behaviors.

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Zongqi Wang, Tianle Gu, Baoyuan Wu, Yujiu Yang

IW-Bench: Evaluating Large Multimodal Models for Converting Image-to-Web (2025.findings-acl)

Challenge:	Existing methods for tracing text origins struggle with a watermark effectiveness dilemma . weaker watermarks preserve text quality, while stronger ones enhance effectiveness .
Approach:	They propose a method that adjusts watermark strength in response to changes in a key factor . they first formalize the problem within a multi-objective trade-off analysis framework .
Outcome:	The proposed method improves watermark effectiveness but reduces text quality . the proposed method prioritizes flexibility and time and space efficiency .

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Hongcheng Guo, Wei Zhang, Junhao Chen, Yaonan Gu, Jian Yang, Junjia Du, Shaosheng Cao, Binyuan Hui, Tianyu Liu, Jianxin Ma, Chang Zhou, Zhoujun Li

WebClipper: Efficient Evolution of Web Agents with Graph-based Trajectory Pruning (2026.acl-long)

Challenge:	Existing models have been introduced to improve image comprehension, but there is no robust benchmark for imagetoweb conversion.
Approach:	They propose a benchmark to assess imagetoweb conversion proficiency of large multimodal models . they propose to measure layout information of web pages by parsing the Document Object Model tree .
Outcome:	The proposed benchmark measures the layout information of web pages—i.e., the positional relationships between elements—which has been overlooked by prior work.

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Junjie Wang, Zequn Xie, Dan Yang, Jie Feng, Yue Shen, Duolin Sun, Meixiu Long, Yihan Jiao, Zhehao Tan, Jian Wang, Peng Wei, Jinjie Gu

Structured Data in NLP

Challenge:	Open-source web agents rely on long tool-call trajectories with cyclic reasoning loops and exploration of unproductive branches.
Approach:	They propose a framework that compresses web agent trajectories via graph-based pruning.
Outcome:	The proposed framework reduces tool-call rounds by 20% while improving accuracy and efficiency while maintaining the same level of performance as existing models.

DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)

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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang

DrAgent: Empowering Large Language Models as Medical Agents for Multi-hop Medical Reasoning (2025.findings-emnlp)

Challenge:	Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs).
Approach:	They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories.
Outcome:	The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks.

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Fenglin Liu, Zheng Li, Hongjian Zhou, Qingyu Yin, Jingfeng Yang, Xin Liu, Zhengyang Wang, Xianfeng Tang, Shiyang Li, Xiang He, Ruijie Wang, Bing Yin, Xiao Gu, Lei Clifton, David A. Clifton

The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit (2025.acl-long)

Challenge:	commercial LLMs can be difficult to use in real-world clinical decision-making . a lightweight LLM can be used to collaborate with diverse clinical tools .
Approach:	They propose a lightweight LLM that can be used to build medical LLMs as agents . they use recursive curriculum learning to optimize the LLM in an easy-to-hard progression .
Outcome:	The proposed approach outperforms human experts in medical examinations on diverse datasets.

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Huixue Zhou, Hengrui Gu, Zaifu Zhan, Xi Liu, Kaixiong Zhou, Yongkang Xiao, Mingfu Liang, Srinivas Prasad Govindan, Piyush Chawla, Jiyan Yang, Xiangfei Meng, Huayu Li, Buyun Zhang, Liang Luo, Wen-Yen Chen, Yiping Han, Bo Long, Rui Zhang, Tianlong Chen

Green & Sustainable NLP

Challenge:	Existing frameworks for Large Language Models (LLMs) for Click-Through Rate prediction require a careful balance between computational efficiency and predictive accuracy.
Approach:	They propose a framework that integrates Retrieval-Augmented Generation with a novel multi-head early exit architecture to address both challenges.
Outcome:	The proposed framework reduces retrieval time while maintaining high model performance.

MISP-Meeting: A Real-World Dataset with Multimodal Cues for Long-form Meeting Transcription and Summarization (2025.acl-long)

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HangChen HangChen, Chao-Han Huck Yang, Jia-Chen Gu, Sabato Marco Siniscalchi, Jun Du

CMR Scaling Law: Predicting Critical Mixture Ratios for Continual Pre-training of Language Models (2024.emnlp-main)

Challenge:	Existing systems that can recognize spoken content, extract key information, and produce concise summaries are lacking in meeting transcription and summarization.
Approach:	They propose a multimodal dataset that integrates information from speech, vision, and text modalities to facilitate automatic meeting transcription and summarization (AMTS).
Outcome:	The proposed dataset reduces the character error rate (CER) by 36.60% to 20.27% and improves speech recognition and large language models.

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Jiawei Gu, Zacc Yang, Chuanghao Ding, Rui Zhao, Fei Tan

Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)

Challenge:	Large Language Models (LLMs) excel in diverse tasks but often underperform in specialized fields due to limited domain-specific or proprietary corpus.
Approach:	They propose a power-law relationship between loss, mixture ratio, and training tokens scale and formalize the trade-off between general and domain-specific capabilities.
Outcome:	The proposed model achieves the desired domain transfer while maintaining general ability and highest utilization of available resources.

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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen

Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training (2026.acl-long)

Challenge:	Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval.
Approach:	They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs).
Outcome:	The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models.

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Lei Liu, Hao Zhu, Xiaoyan Yang, Yue Shen, Zhixuan Chu, Jian Wang, Jinjie Gu, Kui Ren

Improving Zero-Shot Multilingual Translation with Universal Representations and Cross-Mapping (2022.findings-emnlp)

Challenge:	Large language models (LLMs) have impressive capabilities across a wide range of domains, but their generalpurpose pre-training objectives often leave them illsuited for specialized applications such as healthcare.
Approach:	They propose a perplexity-aware data scaling law that establishes a predictive relationship between the perplexities of domain-specific data and the test loss.
Outcome:	Experiments on medical and general-domain benchmarks show that the proposed scaling law consistently identifies near-optimal training subsets with significantly reduced data consumption.

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Shuhao Gu, Yang Feng

Muffin or Chihuahua? Challenging Multimodal Large Language Models with Multipanel VQA (2024.acl-long)

Challenge:	Existing model structure and training scheme cannot ensure universal representations and cross-mappings because of lacking explicit constraints.
Approach:	They propose a state mover’s distance model to model the difference of the representations output by the encoder and a agreement-based training scheme to minimize the proposed distance to learn universal representations.
Outcome:	The proposed model can translate between languages unseen during training, i.e., zero-shot translation.

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Yue Fan, Jing Gu, Kaiwen Zhou, Qianqi Yan, Shan Jiang, Ching-Chen Kuo, Yang Zhao, Xinze Guan, Xin Wang

ETR: Entropy Trend Reward for Efficient Chain-of-Thought Reasoning (2026.acl-long)

Challenge:	Multipanel images are a common form of visual representations, and humans can achieve approximately 99% accuracy on these questions.
Approach:	They propose a benchmark that tests multipanel visual reasoning models with 6,600 triplets of questions, answers, and multipanel images.
Outcome:	The proposed benchmark features 6,600 triplets of questions, answers, and multipanel images that challenge state-of-the-art Multimodal Large Language Models (MLLMs) human users can attain approximately 99% accuracy on these questions, compared with previous benchmarks.

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Xuan Xiong, Huan Liu, Li Gu, Zhixiang Chi, Yue Qiu, Yuanhao YU, Yang Wang

FedCoT: Federated Chain-of-Thought Distillation for Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods to shorten CoTs use length penalties or global entropy reduction . Existing approaches to CoT reasoning have significant practical drawbacks .
Approach:	They propose a method that shortens CoTs by length penalties or global entropy reduction . they integrate ETR into Group Relative Policy Optimization and evaluate it .
Outcome:	The proposed objective improves accuracy–efficiency trade-off by +9.9% while reducing CoT length by 67% across four benchmarks.

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Tao Fan, Weijing Chen, Yan Kang, Guoqiang Ma, Hanlin Gu, Yuanfeng Song, Lixin Fan, Qiang Yang

FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models (2025.acl-long)

Challenge:	Large Language Models (LLMs) have emerged as a transformative force in artificial intelligence, demonstrating exceptional proficiency across various tasks.
Approach:	They propose a federated framework for the Chain-of-Thought distillation of knowledge from LLMs to SLMs, while adhering to privacy requirements.
Outcome:	The proposed framework ensures secure knowledge transfer from an LLM on a high-powered server to an SLM on resource-constrained client while adhering to privacy requirements.

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Hongzhan Lin, Yang Deng, Yuxuan Gu, Wenxuan Zhang, Jing Ma, See-Kiong Ng, Tat-Seng Chua

Improving Automatic Grammatical Error Annotation for Chinese Through Linguistically-Informed Error Typology (2025.coling-main)

Challenge:	Existing fact-checking evaluation methods rely on static datasets and classification metrics, which fail to evaluate justification production and uncover the nuanced limitations of LLMs.
Approach:	They propose a framework that adaptively and dynamically assesses LLMs’ fact-checking capabilities by incorporating justification production alongside verdict prediction.
Outcome:	Experiments show that the framework differentiates among state-of-the-art LLMs, providing valuable insights into model strengths and limitations in model-centric fact-checking analysis.

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Yang Gu, Zihao Huang, Min Zeng, Mengyang Qiu, Jungyeul Park

Syntactic Text Processing

Challenge:	In educational settings, GEC systems provide immediate and consistent feedback to both native (L1) and non-native (L2) language learners.
Approach:	They propose a framework that provides detailed feedback on 12-16% of all errors by identifying them under a new error typology, specific enough to uncover subtle differences in error patterns between L1 and L2 writings.
Outcome:	The proposed framework can provide detailed feedback on 12-16% of all errors, revealing subtle differences in error patterns between L1 and L2 writings.

CoT-VTM: Visual-to-Music Generation with Chain-of-Thought Reasoning (2025.findings-acl)

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Xikang Guan, Zheng Gu, Jing Huo, Tianyu Ding, Yang Gao

Extract, Transform and Filling: A Pipeline Model for Question Paraphrasing based on Template (D19-55)

Challenge:	Existing methods for visual-to-music generation lack large-scale, high-quality visual-music paired datasets and lack of direct semantic correspondence between visuals and music.
Approach:	They propose a framework that distills Chain-of-Thought reasoning to enable visual-to-music generation without paired data.
Outcome:	The proposed framework achieves optimal performance on image-to-music and video-to music tasks.

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Yunfan Gu, Yang Yuqiao, Zhongyu Wei

Paraphrasing

Challenge:	Recent approaches for paraphrasing generate unpredictable results .
Approach:	They propose a question paraphrasing pipeline model based on templates that identifies template and retrieves candidate templates and fills them with original topic words.
Outcome:	The proposed model outperforms the seq2seq model on two datasets and is more promising when the training sample is small.

Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection (2026.findings-acl)

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Zhuoyang Wu, Xinze Li, Zhenghao Liu, Yukun Yan, Zhiyuan Liu, Minghe Yu, Cheng Yang, Yu Gu, Ge Yu, Maosong Sun

Beyond the Last Frame: Process-aware Evaluation for Generative Video Reasoning (2026.acl-long)

Challenge:	Existing studies treat SLMs as student models and use long-form Chains-of-Thought (CoTs) as supervision signals for Supervised Fine-Tuning (SFT). Existing research focuses on distilling reasoning ability from LLMs to enhance the mathematical reasoning performance of small-scale models.
Approach:	They propose a framework that refines teacher CoTs through an error-aware reflection process to enable the student model to construct more tailored teacher Cots.
Outcome:	Experiments on multiple mathematical reasoning benchmarks show that ORION improves performance by more than 2% over all baselines.

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Yifan Li, YuKai Gu, Yingqian Min, Zikang Liu, Yifan Du, Kun Zhou, Min Yang, Xin Zhao, Minghui Qiu

Refined Evaluation for End-to-End Grammatical Error Correction Using an Alignment-Based Approach (2025.coling-main)

Challenge:	Existing evaluation frameworks often rely on single-frame assessments, which can lead to outcome-hacking.
Approach:	They propose a process-aware evaluation paradigm that uses a hierarchical rubric to evaluate the validity of the intermediate steps and the final result.
Outcome:	The proposed model achieves POC@1.0 only about 20% and exhibits significant outcome-hacking.

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Junrui Wang, Mengyang Qiu, Yang Gu, Zihao Huang, Jungyeul Park

Syntactic Text Processing

Challenge:	errant is a new evaluation tool that can be used to evaluate end-to-end grammatical error correction systems.
Approach:	They propose a method to assess end-to-end grammatical error correction systems using alignment-based alignment methods that reproduce and improve results from existing evaluation tools.
Outcome:	The proposed method reproduces and improves results from existing evaluation tools, such as errant, even when applied to raw text input.

Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs (2026.acl-long)

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Binxing Xu, Hao Gu, Lujun Li, Hao Wang, Bei Liu, Jiacheng Liu, Qiyuan Zhu, Xintong Yang, Chao Li, Sirui Han, Yike Guo

Green & Sustainable NLP

Challenge:	Existing approaches to training LLMs at ultra-low precisions suffer from convergence instability and substantial training costs.
Approach:	They propose a progressive QAT framework with outlier channel splitting to address these issues . they use nested structure of integer quantization grids to enable a "train once, deploy any precision" paradigm .
Outcome:	The proposed framework outperforms baselines on both Llama2/3 and W2A16, with an 11 speedup over BF16.

Invisible Entropy: Towards Safe and Efficient Low-Entropy LLM Watermarking (2025.emnlp-main)

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Tianle Gu, Zongqi Wang, Kexin Huang, Yuanqi Yao, Xiangliang Zhang, Yujiu Yang, Xiuying Chen

RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis (2025.findings-acl)

Challenge:	Existing methods to watermark low-entropy content are expensive and risky . IE reduces parameter size by 99% while achieving performance on par with state-of-the-art methods .
Approach:	They propose a logit-based watermarking paradigm that uses entropy-based features to predict whether the next token is high or low.
Outcome:	The proposed method reduces parameter size by 99% while achieving performance on par with state-of-the-art methods.

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Pengzuo Wu, Yuhang Yang, Guangcheng Zhu, Chao Ye, Hong Gu, Xu Lu, Ruixuan Xiao, Bowen Bao, Yijing He, Liangyu Zha, Wentao Ye, Junbo Zhao, Haobo Wang

Probing the Safety Robustness of LLMs in Latent Space (2026.acl-long)

Challenge:	Existing benchmarks for large language models focus on simple, flat table structures.
Approach:	They propose a benchmark to evaluate the performance of both Large Language Models and Multimodal LLMs across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG.
Outcome:	The proposed benchmark evaluates the performance of LLMs and Multimodal LLM models across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG.

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Tianle Gu, Kexin Huang, Zongqi Wang, Yixu Wang, Jie Li, Xin Wang, Yang Yao, Yujiu Yang, Yan Teng, Yingchun Wang

Robustness in NLP

Challenge:	Despite substantial progress in safety alignment techniques, aligned large language models can still produce unsafe responses under minor internal perturbations.
Approach:	They introduce Activation Steering Attack (ASA) and leverage the Negative Log-Likelihood (NLL) as a diagnostic signal to probe the local sensitivity of safety behaviors in latent space.
Outcome:	The proposed method is model-agnostic and supervision-free, enabling a general and reproducible diagnostic metric for analyzing safety robustness.

Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense (2025.naacl-long)

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Yang Ouyang, Hengrui Gu, Shuhang Lin, Wenyue Hua, Jie Peng, Bhavya Kailkhura, Meijun Gao, Tianlong Chen, Kaixiong Zhou

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

Challenge:	Existing methods to defend against jailbreak attacks exploit vulnerabilities to elicit unintended or harmful outputs.
Approach:	They propose a method to defend against jailbreak attacks by patching specific layers within large language models through self-augmented datasets.
Outcome:	The proposed approach reduces harmfulness and attack success rate of jailbreak attacks without compromising utility for benign queries compared to previous methods.

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

From Long Videos to Engaging Clips: A Human-Inspired Video Editing Framework with Multimodal Narrative Understanding (2025.emnlp-industry)

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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Xiangfeng Wang, Xiao Li, Yadong Wei, null Songxueyu, Yang Song, null Xiaxiaoqiang, Fangrui Zeng, Zaiyi Chen, null Liuliu, Gu Xu, Tong Xu

Enhanced Reasoning for Biomedical Document-Level Relation Extraction via a Novel Cascade Language Model Framework (2026.acl-long)

Challenge:	Existing methods for video editing rely on textual cues from ASR transcripts and segment selection, often neglecting rich visual context.
Approach:	They propose a human-inspired automatic video editing framework that leverages multimodal narrative understanding to address these limitations.
Outcome:	The proposed framework outperforms existing baselines across general and advertisement-oriented editing tasks.

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Haohua Song, Wenhao Gu, Zhijing Li, null Yunwenyu, Tiantian Zhu, Xiao Yang, Zexuan Zhu

CompleQA: Benchmarking the Impacts of Knowledge Graph Completion Methods on Question Answering (2023.findings-emnlp)

Challenge:	Pre-trained language models (PLMs) are the leading paradigm in document-level relation extraction.
Approach:	They propose a cascade framework that leverages the complementary strengths of PLMs and LLMs through a detect-then-rethink paradigm.
Outcome:	The proposed framework improves on BioRED and CDR datasets and improves existing models.

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Donghan Yu, Yu Gu, Chenyan Xiong, Yiming Yang

From Evasion to Concealment: Stealthy Knowledge Unlearning for LLMs (2025.findings-acl)

Challenge:	Existing studies have focused on Knowledge Graph Completion as an end in itself, neglecting its potential impact on subsequent applications.
Approach:	They propose a benchmark to assess the impact of representative KGC methods on Knowledge Graph Question Answering (KGQA) they use a knowledge graph with 3 million triplets across 5 distinct domains to evaluate their results.
Outcome:	The proposed benchmark compares four well-known methods with two state-of-the-art systems to assess the impact of incomplete knowledge graphs on KGQA.

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Tianle Gu, Kexin Huang, Ruilin Luo, Yuanqi Yao, Xiuying Chen, Yujiu Yang, Yan Teng, Yingchun Wang

Robustness in NLP

Challenge:	Existing approaches to unlearning often treat nonsensical responses or template-based refusals as the unlearning target, making the process even more vulnerable to attacks and jailbreaks.
Approach:	They propose a method that uses inverted facts to remove the need for auxiliary models or retaining data while avoiding leakage.
Outcome:	Evaluated on the ToFU Knowledge Unlearning dataset using Llama2-7B-Chat and Phi-1.5, MEOW outperforms baselines in forgetting quality while preserving model utility.

SCAN: Structured Capability Assessment and Navigation for LLMs (2026.acl-long)

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Zongqi Wang, Tianle Gu, Chen Gong, Xin Tian, Siqi Bao, Yujiu Yang

SciCustom: A Framework for Custom Evaluation of Scientific Capabilities in Large Language Models (2026.acl-long)

Challenge:	Existing research has focused on approximating model rankings, but such benchmarks fail to provide users and developers with a comprehensive and fine-grained understanding of a specific model’s capabilities.
Approach:	They propose a framework that enables detailed characterization of LLM capabilities through comprehensive and fine-grained evaluation.
Outcome:	The proposed framework enables detailed characterization of large language models through comprehensive and fine-grained evaluation.

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Yiyang Gu, Junwei Yang, Junyu Luo, Ye Yuan, Bin Feng, Yingce Xia, Shufang Xie, Kaili Liu, Bohan Wu, Qi Shi, Haoran Li, Beier Xiao, Zhiping Xiao, Xiao Luo, Weizhi Zhang, Philip S. Yu, Zequn Liu, Ming Zhang

Pruning-then-Expanding Model for Domain Adaptation of Neural Machine Translation (2021.naacl-main)

Challenge:	Existing evaluations of large language models fail to reflect fine-grained capabilities . existing benchmarks are manually curated or domain-generic, limiting scalability and alignment with real use cases.
Approach:	They propose a framework that allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific scientific capabilities in LLMs.
Outcome:	The proposed framework reveals fine-grained differences in scientific capabilities that standard benchmarks overlook . it allows custom construction of benchmarks from large-scale scientific data to evaluate application-specific capabilities in LLMs.

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Shuhao Gu, Yang Feng, Wanying Xie

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

Challenge:	Existing methods for domain adaptation suffer from catastrophic forgetting, large domain divergence, and model explosion.
Approach:	They propose a method which prunes the model and keeps the important neurons or parameters responsible for both general-domain and in-domain translation.
Outcome:	The proposed method improves on different language pairs and domains compared with strong baselines.

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

Information Extraction & Text Mining

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

Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration (2026.acl-short)

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Hang Lv, Hongchao Gu, Ruiqing Yang, Liangyue Li, Zulong Chen, Defu Lian, Hao Wang, Enhong Chen

Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features (2024.findings-acl)

Challenge:	Existing methods for listwise reranking exhibit intrinsic position bias . existing methods are constrained by an inherent trade-off between efficiency and flexibility .
Approach:	They propose a training-free framework that mechanically decouples positional bias from ranking decisions.
Outcome:	a training-free framework decouples position bias from ranking decisions . evaluations show it outperforms training-based methods and outperformed expensive methods .

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Mengyu Bu, Shuhao Gu, Yang Feng

Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation (2021.acl-long)

Challenge:	Existing models do not differentiate between semantic and linguistic features, resulting in the entanglement of knowledge and linguistics within the model.
Approach:	They propose to exploit both semantic and linguistic features to enhance multilingual translation by disentangling encoder representations and integrating low-level linguistic encoders.
Outcome:	The proposed model improves zero-shot translation while maintaining performance in supervised translation on multilingual datasets.

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Yang Feng, Shuhao Gu, Dengji Guo, Zhengxin Yang, Chenze Shao

FedMKT: Federated Mutual Knowledge Transfer for Large and Small Language Models (2025.coling-main)

Challenge:	Neural machine translation models are usually based on attention-based encoder-decoder frameworks.
Approach:	They introduce a seer decoder into the encoder-decoder framework during training . they force the conventional decoded decodes to simulate the behavior of the seer .
Outcome:	The proposed method outperforms baselines on Chinese, English and German translation tasks.

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Tao Fan, Guoqiang Ma, Yan Kang, Hanlin Gu, Yuanfeng Song, Lixin Fan, Kai Chen, Qiang Yang

AutoSUIT Bench - Automated Security UnIt Test Benchmark for LLM Coding (2026.findings-acl)

Challenge:	Recent research in large language models (LLMs) has focused on enabling clients to fine-tune their locally deployed homogeneous LLMs collaboratively or on transferring knowledge from server-based LLM to small language models at downstream clients.
Approach:	They propose a parameter-efficient federated mutual knowledge transfer framework for large and small language models that allows for token alignment and selective knowledge transfer between client-side LLMs and a server-side SLM.
Outcome:	The proposed framework enhances the performance of both LLMs and SLMs with clients' unique domain insights while preserving the server's LLM and client's unique domain insight.

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Samuel Osebe, Fan Yang, Junyi Li, Yue Gu, Yongxin Wang, Satyapriya Krishna, Kai-Wei Chang, Aram Galstyan, Rahul Gupta, Weitong Ruan

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) are evolving rapidly on code generation tasks.
Approach:	They propose to automate the vulnerability code benchmark creation with iterative auto validation.
Outcome:	The proposed benchmark covers 232 CWE categories across C/C++, Java, and Python languages.

HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy (2026.acl-industry)

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Fan Xu, Xinyu Hu, Zhenghan Yu, Li Lin, Xu Zhang, Yang Zhang, Wei Zhou, Jinjie Gu, Xiaojun Wan

FrontCoder: Scaling Visual Fidelity in Front-End Code Generation (2026.findings-acl)

Challenge:	relying on large language models for information has raised concerns about reliability and accuracy of outputs.
Approach:	They propose a hallucination taxonomy with 11 categories for various NLG tasks and propose HAllucination Detection models which integrate hallucinism detection, span-level identification, and correction into a single inference process.
Outcome:	The proposed models outperform baselines on HaluEval, FactCHD, and FaithBench, confirming their robustness and versatility.

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Jun Feng, Jian Yang, Wei Zhang, Jing Wang, Keyi Chen, Xiaokun Yang, Weicheng Gu, Yihang Lou, Yan Bai, Xianglong Liu

Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment (P18-1)

Challenge:	Existing work on front-end code generation fails to provide visual fidelity and rendering quality for front- end developers.
Approach:	They propose a three-stage pipeline to enhance front-end code generation capabilities in LLMs . they use synthetic data, quality-controlled supervised fine-tuning, and reinforcement learning .
Outcome:	The proposed model achieves competitive performance with frontier models while maintaining generation efficiency.

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Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, Ivan Marsic

Sentiment Analysis

Challenge:	Existing approaches to classify human affect and subjective information from multiple data sources are limited by the lack of high-level feature associations.
Approach:	They propose a hierarchical multimodal architecture with attention and word-level fusion to classify utterance-level sentiment and emotion from text and audio data.
Outcome:	The proposed model outperforms state-of-the-art approaches on published datasets and visualizes and interprets synchronized attention over modalities.

Improving Domain Adaptation Translation with Domain Invariant and Specific Information (N19-1)

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Shuhao Gu, Yang Feng, Qun Liu

High-order Joint Constituency and Dependency Parsing (2024.lrec-main)

Challenge:	Neural machine translation models are based on the encoder-decoder architecture, which makes them overfitting to frequent observations.
Approach:	They propose a method to explicitly model out-of-domain information in an encoder-decoder framework . they propose combining out- of-domain training data with out-out-of domain data .
Outcome:	The proposed method outperforms baselines on multiple data sets.

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Yanggan Gu, Yang Hou, Zhefeng Wang, Xinyu Duan, Zhenghua Li

Syntactic Text Processing

Challenge:	Syntactic parsing aims to reveal how sentences are syntactically structured.
Approach:	They propose to produce compatible constituency and dependency trees simultaneously for input sentences . they adopt a much more efficient decoding algorithm and explore joint modeling at training phase .
Outcome:	The proposed model significantly improves matching ratio of whole trees compared to separate models . the proposed model adopts a much more efficient decoding algorithm .

GroupRank: A Groupwise Paradigm for Effective and Efficient Passage Reranking with LLMs (2026.findings-acl)

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Meixiu Long, Duolin Sun, Dan Yang, Yihan Jiao, Lei Liu, Jiahai Wang, Binbin Hu, Yue Shen, Jie Feng, Zhehao Tan, Junjie Wang, Lianzhen Zhong, Jian Wang, Peng Wei, Jinjie Gu

MMLU-ProX: A Multilingual Benchmark for Advanced Large Language Model Evaluation (2025.emnlp-main)

Challenge:	Existing rankers excel in lexical-matching scenarios, while they struggle with complex queries requiring deep reasoning.
Approach:	They propose a new paradigm that balances flexibility and context awareness to unlock the full potential of groupwise reranking.
Outcome:	The proposed approach achieves a state-of-the-art 65.2 NDCG@10 on BRIGHT and surpasses baselines by 2.1 points on R2MED while delivering a 6.4 inference speedup.

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Weihao Xuan, Rui Yang, Heli Qi, Qingcheng Zeng, Yunze Xiao, Aosong Feng, Dairui Liu, Yun Xing, Junjue Wang, Fan Gao, Jinghui Lu, Yuang Jiang, Huitao Li, Xin Li, Kunyu Yu, Ruihai Dong, Shangding Gu, Yuekang Li, Xiaofei Xie, Felix Juefei-Xu, Foutse Khomh, Osamu Yoshie, Qingyu Chen, Douglas Teodoro, Nan Liu, Randy Goebel, Lei Ma, Edison Marrese-Taylor, Shijian Lu, Yusuke Iwasawa, Yutaka Matsuo, Irene Li

Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models (2024.findings-acl)

Challenge:	Existing large language model evaluation benchmarks focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities.
Approach:	They propose a comprehensive benchmark covering 29 languages, built on an English benchmark.
Outcome:	The MMLU-ProX is a comprehensive benchmark covering 29 languages, built on an English benchmark.

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Yihong Dong, Xue Jiang, Huanyu Liu, Zhi Jin, Bin Gu, Mengfei Yang, Ge Li

XMoE: Sparse Models with Fine-grained and Adaptive Expert Selection (2024.findings-acl)

Challenge:	Considering the vast size and wide-ranging sources of LLMs’ training data, it could explicitly or implicitly include test data.
Approach:	They propose a Contamination Detection via output Distribution (CDD) which detects data contamination only by identifying the peakedness of LLM's output distribution.
Outcome:	The proposed method improves performance by 21.8%-30.2% on humanEval and TED: trustworthy evaluation via output distribution.

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Yuanhang Yang, Shiyi Qi, Wenchao Gu, Chaozheng Wang, Cuiyun Gao, Zenglin Xu

Green & Sustainable NLP

Challenge:	XMoE leverages small experts and a threshold-based router to selectively engage only essential parameters.
Approach:	They propose a novel MoE that leverages small experts to selectively engage only essential parameters.
Outcome:	The proposed model can reduce computation load at MoE layers by over 50% without sacrificing performance.

Addressing the Length Bias Challenge in Document-Level Neural Machine Translation (2023.findings-emnlp)

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Zhang Zhuocheng, Shuhao Gu, Min Zhang, Yang Feng

EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association (2025.acl-long)

Challenge:	Document-level neural machine translation (DNMT) models incorporate context information through increased maximum lengths of source and target sentences.
Approach:	They propose a sliding decoding strategy that limits the length of target sentences . they propose 'length-normalized attention mechanism' to aid the model in focusing on target information .
Outcome:	The proposed method can achieve state-of-the-art results on open datasets.

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Weiqi Wang, Limeng Cui, Xin Liu, Sreyashi Nag, Wenju Xu, Chen Luo, Sheikh Muhammad Sarwar, Yang Li, Hansu Gu, Hui Liu, Changlong Yu, Jiaxin Bai, Yifan Gao, Haiyang Zhang, Qi He, Shuiwang Ji, Yangqiu Song

QaRL: Rollout-Aligned Quantization-Aware RL for Fast and Stable Training under Training–Inference Mismatch (2026.findings-acl)

Challenge:	Goal-oriented script planning is used by humans to plan for typical activities . however, this capability remains underexplored due to several challenges .
Approach:	They propose a framework that enables product-enriched scripts by associating products with each step based on the semantic similarity between the actions and their purchase intentions.
Outcome:	The proposed framework can generate product-enriched scripts from 2.4 million scripts . human annotations are conducted to provide gold labels for a sampled subset .

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Hao Gu, Hao Wang, Jiacheng Liu, Lujun Li, Qiyuan Zhu, Bei Liu, Binxing Xu, Lei Wang, Xintong Yang, Sida Lin, Sirui Han, Yike Guo

Challenge:	Recent work has shown that reinforcement learning with simple rule-based reward functions (RLVR) can induce emergent reasoning behaviors and yield gains in challenging domains such as math problem solving.
Approach:	They propose a rollout-alignment-quantization-aware RL which aligns training-side forward with the quantized rollout to minimize mismatch.
Outcome:	The proposed approach outperforms quantized-rollout training by +5.5 on Qwen3-30B-A3B MoE for math problems while maintaining low-bit throughput.

Importance-based Neuron Allocation for Multilingual Neural Machine Translation (2021.acl-long)

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Wanying Xie, Yang Feng, Shuhao Gu, Dong Yu

Hybrid Attention based Multimodal Network for Spoken Language Classification (C18-1)

Challenge:	Existing approaches to multilingual neural machine translation tend to preserve general knowledge, but ignore language-specific knowledge.
Approach:	They propose to divide model neurons into general and language-specific parts based on their importance across languages.
Outcome:	The proposed model can preserve general knowledge but ignore language-specific knowledge on several languages, and is universal and cost-effective.

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Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, Ivan Marsic

Information Extraction & Text Mining

Challenge:	Using linguistic content and vocal characteristics for multimodal deep learning is difficult for computers to interpret human meaning .
Approach:	They propose a deep multimodal network with feature attention and modality attention to classify utterance-level speech data.
Outcome:	The proposed system achieves state-of-the-art or competitive results on three published multimodal datasets.

Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation (D19-1)

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Zhengxin Yang, Jinchao Zhang, Fandong Meng, Shuhao Gu, Yang Feng, Jie Zhou

Machine Translation

Challenge:	Context modeling is essential to generate coherent and consistent translation for document-level Neural Machine Translations.
Approach:	They propose a query-guided capsule network to cluster context information into different perspectives from which the target translation may concern.
Outcome:	The proposed model outperforms baseline models on multiple datasets of different domains.

Enhancing Neural Machine Translation with Semantic Units (2023.findings-emnlp)

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Langlin Huang, Shuhao Gu, Zhang Zhuocheng, Yang Feng

Machine Translation

Challenge:	Existing methods to model and leverage semantic units in natural language do not provide a complete understanding of the whole sentence.
Approach:	They propose a method which models the integral meanings of semantic units within a sentence . they propose 'word pair encoder' to help identify the boundaries of semantic unit boundaries .
Outcome:	The proposed method outperforms baselines and supports the semantic unit representation of subwords and tokens.

Plan, Generate and Complicate: Improving Low-resource Dialogue State Tracking via Easy-to-Difficult Zero-shot Data Augmentation (2024.findings-acl)

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Ming Gu, Yan Yang

Low-Resource NLP

Challenge:	Existing data augmentation methods rely on pre-defined user goals and neglect the importance of data complexity in this task.
Approach:	They propose an Easy-to-Difficult Zero-shot Data Augmentation framework for low-resource dialogue state tracking that utilizes large language models to automatically catch the relationships of different domains and then generate dialogue data.
Outcome:	The proposed framework outperforms existing methods on MultiWOZ and other similar datasets.

MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction (2024.emnlp-main)

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Qiao Sun, Liujia Yang, Minghao Ma, Nanyang Ye, Qinying Gu

Sentiment Analysis

Challenge:	Existing approaches within the pretraining-finetuning paradigm tend to meticulously craft complex tagging schemes and classification heads, or incorporate external semantic enhancements to enhance performance.
Approach:	They propose to integrate a minimalist tagging scheme and a novel token-level contrastive learning strategy to improve pretrained representations.
Outcome:	The proposed framework achieves comparable or superior performance compared to state-of-the-art techniques while featuring a more compact design and reduced computational overhead.

Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)

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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng

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

Challenge:	Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge.
Approach:	They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy.
Outcome:	The proposed model protects private data while enhancing the model's knowledge.

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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

LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit (2024.emnlp-industry)

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.

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Ruihao Gong, Yang Yong, Shiqiao Gu, Yushi Huang, Chengtao Lv, Yunchen Zhang, Dacheng Tao, Xianglong Liu

Weights-Rotated Preference Optimization for Large Language Models (2025.emnlp-main)

Challenge:	Existing quantization techniques have been categorized as 'simple' and 'highly efficient' however, their configurations vary from each other and cannot be fairly compared .
Approach:	They propose a plug-and-play compression toolkit to explore the impact of quantization.
Outcome:	The proposed toolkit explores the impact of quantization on large language models.

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Chenxu Yang, Ruipeng Jia, Mingyu Zheng, Naibin Gu, Zheng Lin, Siyuan Chen, Weichong Yin, Hua Wu, Weiping Wang

T2R-BENCH: A Benchmark for Real World Table-to-Report Task (2025.emnlp-main)

Challenge:	Existing methods to align large language models with high reward hacking are limited by the complexity of the parameter space and the complexity.
Approach:	They propose a weights-rotated preference optimization algorithm that constrains the output layer logits with the KL divergence inherited from DPO and fine-tunes the intermediate hidden states.
Outcome:	The proposed algorithm achieves a 3.27-point improvement on AlpacaEval 2 and surpasses the best baseline by 6.2 to 7.5 points on MT-Bench with merely 0.015% of the trainable parameters.

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Jie Zhang, Changzai Pan, Sishi Xiong, Kaiwen Wei, Yu Zhao, Xiangyu Li, Jiaxin Peng, Xiaoyan Gu, Jian Yang, Wenhan Chang, Zhenhe Wu, Jiang Zhong, Shuangyong Song, Xuelong Li

Structured Data in NLP

Challenge:	Existing table benchmarks lack the capacity to adequately assess the practical application of table reasoning in industrial applications.
Approach:	They propose a bilingual table-to-report task and a table-based benchmark to assess the quality of table reasoning.
Outcome:	The proposed task is based on a bilingual benchmark with 457 industrial tables and evaluation criteria to measure the quality of report generation.

EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solving (2026.findings-acl)

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Xiyuan Zhou, Xinlei Wang, Yirui He, Ruixi Zou, Yang Wu, Yuheng Cheng, Yulu Xie, Wenxuan Liu, Huan Zhao, Yan Xu, Jinjin Gu, Junhua Zhao

COAST: Enhancing the Code Debugging Ability of LLMs through Communicative Agent Based Data Synthesis (2025.findings-naacl)

Challenge:	Existing benchmarks focus on well-defined or abstract reasoning and fail to capture real-world engineering problems.
Approach:	They propose a hierarchical benchmark to evaluate large language models on engineering problems.
Outcome:	The proposed model performs well under well-defined conditions and is based on three levels of difficulty and covers diverse engineering subfields.

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Weiqing Yang, Hanbin Wang, Zhenghao Liu, Xinze Li, Yukun Yan, Shuo Wang, Yu Gu, Minghe Yu, Zhiyuan Liu, Ge Yu

HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs (2026.findings-acl)

Challenge:	Existing code debugging benchmarks focus on the Code Repair stage of the code generation process.
Approach:	They propose a framework to evaluate the debugging abilities of large language models by emulating the human debug process.
Outcome:	The proposed framework outperforms human-curated and GPT-4-generated training data, enabling 7B-scale LLMs to achieve comparable debugging performance to GPT-3.5.

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Ru Peng, Tianyu Zhao, Xijun Gu, Zhiting Fan, Haokai Xu, Jinyang Zhang, Yawen Zeng, Yihong Zhuang, Kexin Yang, Junyang Lin, Dayiheng Liu, Junbo Zhao

Challenge:	High-quality, diverse data are vital for large language models (LLMs) but remain scarce and costly.
Approach:	They define the first HSS domain system covering 14 mainstream fields and introduce HSS-Synth.
Outcome:	the proposed pipeline outperforms 14 leading baselines on 16 benchmarks.

Learning Dynamic Context Augmentation for Global Entity Linking (D19-1)

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Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, Xiang Ren

Knowledge Representation

Challenge:	Existing collective entity linking methods are expensive and often lack local context information.
Approach:	They propose a dynamic context-augmented inference model that can be used to make collective inference.
Outcome:	The proposed model can cope with different local EL models with different learning settings, base models, decision orders and attention mechanisms.

AEA: Adaptive Expert Allocation Improves Sentence Embeddings from Mixture-of-Experts LLM (2026.acl-long)

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Shufan Yang, Zifeng Cheng, Zhiwei Jiang, Qingfeng Qi, Yafeng Yin, Cong Wang, Ao Zhou, Qing Gu

Token-level Adaptive Training for Neural Machine Translation (2020.emnlp-main)

Challenge:	Existing methods to improve embeddings from Mixture-of-Experts models allocate a fixed number of experts uniformly across all layers and tokens, ignoring inter-layer and inter-token heterogeneity.
Approach:	They propose an Adaptive Expert Allocation framework that performs layer-wise and token-wise expert allocation to enhance embedding quality.
Outcome:	The proposed method improves embedding quality across multiple MoE models.

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Shuhao Gu, Jinchao Zhang, Fandong Meng, Yang Feng, Wanying Xie, Jie Zhou, Dong Yu

RWKV-CLIP: A Robust Vision-Language Representation Learner (2024.emnlp-main)

Challenge:	Existing token imbalance phenomenon in natural language as different tokens appear with different frequencies, which leads to different learning difficulties for tokens in Neural machine translation (NMT).
Approach:	They propose to assign tokens with different frequencies to target tokens during training to encourage the model to pay more attention to low-frequency tokens.
Outcome:	The proposed model yields consistent improvements on ZH-EN, EN-RO, and EN-DE translation tasks, especially on sentences that contain more low-frequency tokens.

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Tiancheng Gu, Kaicheng Yang, Xiang An, Ziyong Feng, Dongnan Liu, Weidong Cai, Jiankang Deng

Extending Context Window of Large Language Models from a Distributional Perspective (2024.emnlp-main)

Challenge:	Using large image-text datasets, large-scale image-data sets have been used for visionlanguage pre-training.
Approach:	They propose a framework that leverages Large Language Models to combine and refine information from web-based image-text pairs, synthetic captions, and detection tags.
Outcome:	The proposed framework can combine and refine information from web-based image-text pairs, synthetic captions, and detection tags.

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Yingsheng Wu, Yuxuan Gu, Xiaocheng Feng, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin

Unified Hallucination Detection for Multimodal Large Language Models (2024.acl-long)

Challenge:	Existing scaling methods for extending context window rely on empirical approaches and lack understanding of the internal distribution within RoPE resulting in suboptimal performance.
Approach:	They propose to optimize the context window extending task from the view of rotary angle distribution by minimizing disturbance between rotary angles to maintain consistency with the pre-training phase.
Outcome:	The proposed approach reduces by up to 72% of the distributional disturbance when extending LLaMA2’s context window to 8k, and reduces it by up 32% when extending to 16k.

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Xiang Chen, Chenxi Wang, Yida Xue, Ningyu Zhang, Xiaoyan Yang, Qiang Li, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen

Fair Text-Attributed Graph Representation Learning (2025.findings-emnlp)

Challenge:	despite significant strides in multimodal tasks, MLLMs are plagued by the critical issue of hallucination.
Approach:	They propose a meta-evaluation benchmark to facilitate evaluation of advancements in hallucination detection methods.
Outcome:	The proposed framework validates hallucinations robustly and provides strategic insights . MHaluBench is a meta-evaluation benchmark designed to facilitate evaluation .

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Ruilin Luo, Tianle Gu, Lin Wang, Yunfeng Zhou, Songtao Jiang, Lei Wang, Yujiu Yang

A Multi-Attention based Neural Network with External Knowledge for Story Ending Predicting Task (C18-1)

Challenge:	Text-Attributed Graphs (TAGs) inherit issues from Graph Neural Networks such as fairness.
Approach:	They propose to evolve LM-as-encoder to LM as-fair-encoding process to explore fairness in TAGRL.
Outcome:	The proposed process can be integrated with fairness-enhancing strategies on the GNNs decoder side.

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Qian Li, Ziwei Li, Jin-Mao Wei, Yanhui Gu, Adam Jatowt, Zhenglu Yang

Knowledge Representation

Challenge:	Existing studies on the topic of common sense story understanding focus on generating guesses for a missing event or concentrating on unsupervised learning.
Approach:	They propose to extend attention-based neural network with external knowledge resources to understand temporal stories and predict their endings.
Outcome:	The proposed model outperforms state-of-the-art models and external knowledge resources.

Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation (2020.coling-main)

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Shuhao Gu, Yang Feng

Machine Translation

Challenge:	Neural machine translation models suffer from catastrophic forgetting during continual training . models tend to overfit to frequent observations in the in-domain data but forget previously learned knowledge.
Approach:	They investigated the causes of catastrophic forgetting in NMT models by examining their parameters and modules.
Outcome:	The proposed model forgets previously learned knowledge and swings to fit new data . the results show that some parameters are important for both the general-domain and in-domain translation and the great change of them during continual training brings about the performance decline in general- domain.

How Numerical Precision Affects Arithmetical Reasoning Capabilities of LLMs (2025.findings-acl)

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Guhao Feng, Kai Yang, Yuntian Gu, Xinyue Ai, Shengjie Luo, Jiacheng Sun, Di He, Zhenguo Li, Liwei Wang

LLEOT: A Privacy-Enhancing Offsite Tuning Framework via Loss Landscape Elevation (2026.findings-acl)

Challenge:	Despite the success of transformer-based large language models, understanding and enhancing their mathematical capabilities remains a significant challenge.
Approach:	They propose to use numerical precision as a key factor that influences LLMs' effectiveness in arithmetical tasks to determine their effectiveness.
Outcome:	The proposed models perform better in arithmetic tasks than transformer-based models with standard numerical precision.

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Jin Zhong, Jinglin Liang, Tongtong Yang, Zijian Xie, Shuangping Huang, Hanlin Gu

MPR-GUI: Benchmarking and Enhancing Multilingual Perception and Reasoning in GUI Agents (2026.acl-long)

Challenge:	Existing approaches to fine-tune large language models are infeasible due to privacy regulations.
Approach:	They propose an offsite tuning framework that secures data privacy and model parameter and capability privacy.
Outcome:	The proposed framework secures data privacy and model parameter and capability privacy while preserving gradient alignment.

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Ruihan Chen, Qiming Li, Xiaocheng Feng, Weihong Zhong, Xiaoliang Yang, Yuxuan Gu, Zekun Zhou, Yunfei Lu, Haoyu Ren, Kun Chen, Dandan Tu, Bing Qin

Scaling Law for Document Neural Machine Translation (2023.findings-emnlp)

Challenge:	Existing GUI benchmarks lack fine-grained diagnostics to identify which capabilities lead to task failures.
Approach:	They propose a multilingual P R GUI Benchmark to assess LVLMs' language capabilities . they propose XLI to align non-English hidden states with English ones during inference .
Outcome:	The proposed benchmark reveals consistent gaps between English and non-English settings . it reduces the cross-lingual gaps with an average gain of 6.5% in non- English settings compared to static benchmarks .

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Zhang Zhuocheng, Shuhao Gu, Min Zhang, Yang Feng

RLShield: Dynamic Jailbreak Detection for LLMs via Reinforced Adaptive Learning (2026.findings-acl)

Challenge:	Neural machine translation (NMT) methods fail to capture discourse phenomena such as pronominal anaphora, lexical consistency, and document coherence as the input text exceeds a single sentence.
Approach:	They examine the effects of model scale, data scale, and sequence length on translation quality when model size is limited.
Outcome:	The proposed model scales and data scales are compared with the existing models and show that increasing sequence length improves translation quality when model size is limited.

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Zhao Tong, Pengfei Yang, Yimeng Gu, Haichao Shi, Qiang Liu, Xingcheng Xu, Shu Wu, Xiao-Yu Zhang