Papers by Sheng Yang

A Joint Model for Dropped Pronoun Recovery and Conversational Discourse Parsing in Chinese Conversational Speech (2021.acl-long)

Challenge:	Existing distributed training frameworks are plagued by over-reliance on prior profiling and poor generalization across models/hardware.
Approach:	They propose a model-driven multi-agent framework that leverages Large Language Models to enable automatic and explainable distributed training strategy configuration.
Outcome:	The proposed framework outperforms expert-designed training strategies within 20 iterations.

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Jingxuan Yang, Kerui Xu, Jun Xu, Si Li, Sheng Gao, Jun Guo, Nianwen Xue, Ji-Rong Wen

MaskTab: Scalable Masked Tabular Pretraining with Scaling Laws and Distillation for Industrial Classification (2026.findings-acl)

Challenge:	Existing work regards dropped pronoun recovery and conversational discourse parsing as two separate tasks and tackles them separately.
Approach:	They propose a neural model for dropped pronoun recovery and conversational discourse parsing in Chinese conversational speech.
Outcome:	The proposed model outperforms the state-of-the-art models on a new dataset . the proposed model is based on linguistic and semantic information from Chinese conversational speech .

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Bo Zheng, Yudong Chen, Zihua Xiong, Shuai Fang, Peidong He, Yang Yang, Sheng Guo

Logical Consistency as a Bridge: Improving LLM Hallucination Detection via Label Constraint Modeling between Responses and Self-Judgments (2026.acl-long)

Challenge:	Tabular data is high-dimensional, riddled with missing entries, and rarely labeled at scale.
Approach:	They propose a unified pre-training framework for industrial-scale tabular data . MaskTab encodes missing values via dedicated learnable tokens .
Outcome:	The proposed framework outperforms XGBoost and MaskTab-L on industrial-scale . it achieves +5.04% AUC and +8.28% KS over prior art under rigorous scaling .

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Hao Mi, Qiang Sheng, Shaofei Wang, Beizhe Hu, Yifan Sun, Zhengjia Wang, Hengqi Zeng, Yang Li, Danding Wang, Juan Cao

RECALL: REpresentation-aligned Catastrophic-forgetting ALLeviation via Hierarchical Model Merging (2025.emnlp-main)

Challenge:	Existing methods for hallucination detection focus on implicit neural uncertainty or explicit symbolic reasoning, ignoring factual hallucinosities.
Approach:	They propose a framework that bridges neural features and symbolic judgments for hallucination detection by leveraging a "meta-judgment" process to map symbolic labels back into the feature space.
Outcome:	Extensive experiments on 4 public datasets, across 4 LLMs, against 8 baselines demonstrate the superiority of LaaB.

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Bowen Wang, Haiyuan Wan, Liwen Shi, Chen Yang, Peng He, Yue Ma, Haochen Han, Wenhao Li, Tiao Tan, Yongjian Li, Fangming Liu, Gong Yifan, Sheng Zhang

Agentic Episodic Control (2026.findings-acl)

Challenge:	Existing models that require task labels or performance trade-offs are susceptible to catastrophic forgetting.
Approach:	They propose a representation-aware model merging framework for continual learning without access to historical data.
Outcome:	The proposed framework outperforms baselines in knowledge retention and generalization across five NLP tasks and multiple continual learning scenarios.

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Xidong Yang, Wenhao Li, Junjie Sheng, Yun Hua, Haosheng Chen, Chuyun Shen, Xiangfeng Wang

Green & Sustainable NLP

Challenge:	Existing methods for reinforcement learning (RL) are limited by poor data efficiency and weak generalization.
Approach:	They propose a novel architecture that integrates large language models into episodic RL.
Outcome:	The proposed architecture achieves 2–6 higher data efficiency than baselines and is the only method to solve complex tasks like UnlockLocal with over 90% success.

Multi-Domain Dialogue State Tracking with Disentangled Domain-Slot Attention (2023.findings-acl)

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Longfei Yang, Jiyi Li, Sheng Li, Takahiro Shinozaki

Natural Language Interfaces

Challenge:	Multi-domain dialogue state tracking is a challenge for task-oriented dialogue systems . domains and slots are aggregated into a single query to generate domain-slot specific representations .
Approach:	They propose to disentangle domain-slot attention for multi-domain dialogue state tracking by separating query about domains and slots from the attention component.
Outcome:	The proposed approach outperforms the standard multi-head attention with aggregated domain-slot query.

Semantic-Aware Logical Reasoning via a Semiotic Framework (2026.acl-long)

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Yunyao Zhang, Xinglang Zhang, Junxi Sheng, Wenbing Li, Junqing Yu, Yi-Ping Phoebe Chen, Wei Yang, Zikai Song

ObfusLM: Privacy-preserving Language Model Service against Embedding Inversion Attacks (2025.acl-long)

Challenge:	Existing studies largely overlook the interplay between logical complexity and semantic complexity, limiting their robustness under abstract propositions, ambiguous contexts, and conflicting stances.
Approach:	They propose a semiotic-square-guided framework that integrates automated deduction with reflective verification to manage logical complexity across deeper reasoning chains.
Outcome:	The proposed framework achieves state-of-the-art performance on RepublicQA with 6.25% average gain, and generalizes well to four mainstream logical reasoning benchmarks with an additional 7.05% improvement.

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Yu Lin, Ruining Yang, Yunlong Mao, Qizhi Zhang, Jue Hong, Quanwei Cai, Ye Wu, Huiqi Liu, Zhiyu Chen, Bing Duan, Sheng Zhong

StraGo: Harnessing Strategic Guidance for Prompt Optimization (2024.findings-emnlp)

Challenge:	Recent studies show that obfuscation techniques for MLaaS are susceptible to embedding inversion attacks (EIAs).
Approach:	They propose a model obfuscation framework that protects client inputs from embedding inversion attacks by obliviously obbing models.
Outcome:	The proposed framework outperforms existing works in utility by 10% with a nearly 80% resistance rate against embedding inversion attacks.

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Yurong Wu, Yan Gao, Bin Zhu, Zineng Zhou, Xiaodi Sun, Sheng Yang, Jian-Guang Lou, Zhiming Ding, Linjun Yang

Noisy Self-Knowledge Distillation for Text Summarization (2021.naacl-main)

Challenge:	Existing methods for prompt optimization often lead to prompt drifting, wherein newly generated prompts canadversely impact previously successful cases while addressing failures.
Approach:	They propose a method to mitigate prompt drifting by integrating in-context learning to formulate specific, actionable strategies for prompt optimization.
Outcome:	The proposed approach mitigates prompt drifting by leveraging insights from both successful and failed cases to identify critical factors for achieving optimization objectives.

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Yang Liu, Sheng Shen, Mirella Lapata

Summarization

Challenge:	Existing approaches to summarize text using a single reference and noisy datasets are ill-suited to summarising on single reference datasets.
Approach:	They propose to use self-knowledge distillation to improve text summarization by generating smoothed labels for students and teachers to reduce model uncertainty.
Outcome:	The proposed framework improves on pretrained and non-pretrained models on three benchmarks.

Transformer-GCRF: Recovering Chinese Dropped Pronouns with General Conditional Random Fields (2020.findings-emnlp)

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Jingxuan Yang, Kerui Xu, Jun Xu, Si Li, Sheng Gao, Jun Guo, Ji-Rong Wen, Nianwen Xue

Can LLMs be Good Graph Judge for Knowledge Graph Construction? (2025.emnlp-main)

Challenge:	Existing approaches to recover dropped pronouns ignore the dependencies between pronounes in neighboring utterances.
Approach:	They propose a framework that combines Transformer network and General Conditional Random Fields to model the dependencies between pronouns in neighboring utterances.
Outcome:	The proposed framework outperforms state-of-the-art models on three Chinese conversation datasets showing that it captures the dependencies between pronouns in neighboring utterances.

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Haoyu Huang, Chong Chen, Zeang Sheng, Yang Li, Wentao Zhang

Structured Data in NLP

Challenge:	Existing methods for converting unstructured text into structured Knowledge Graphs (KGs) have limitations such as large amount of noise, inaccurate knowledge, and hallucination .
Approach:	They propose a GraphJudge framework to reduce noise in real-world documents . they propose Graphjudge to fine-tune a LLM as a graph judge to enhance quality .
Outcome:	The proposed framework eliminates noise in real-world documents and improves the quality of generated KGs.

SpeechIQ: Speech-Agentic Intelligence Quotient Across Cognitive Levels in Voice Understanding by Large Language Models (2025.acl-long)

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Zhen Wan, Chao-Han Huck Yang, Yahan Yu, Jinchuan Tian, Sheng Li, Ke Hu, Zhehuai Chen, Shinji Watanabe, Fei Cheng, Chenhui Chu, Sadao Kurohashi

Forewarned is Forearmed: Pre-Synthesizing Jailbreak-like Instructions to Enhance LLM Safety Guardrail to Potential Attacks (2025.findings-emnlp)

Challenge:	SIQ quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models.
Approach:	They propose a human cognition-inspired evaluation pipeline for voice understanding large language models (LLM_Voice) that quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models.
Outcome:	The proposed framework quantifies voice understanding abilities and provides unified comparisons between cascaded methods and end-to-end models, identifies annotation errors in existing benchmarks, and detects hallucinations in LLM_Voice.

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Sheng Liu, Qiang Sheng, Danding Wang, Yang Li, Guang Yang, Juan Cao

Robustness in NLP

Challenge:	Despite advances in improving large language model (LLM) to refuse to answer malicious instructions, LLMs remain vulnerable to jailbreak attacks where attackers generate instructions with distributions differing from safety alignment corpora.
Approach:	They propose a framework that leverages embedding space distribution analysis to generate jailbreak-like instructions.
Outcome:	The proposed framework shows significant decreases in attack success rate on Qwen2.5, Llama3.1, and Llma3.2 without compromising their utility.

CoVoGER: A Multilingual Multitask Benchmark for Speech-to-text Generative Error Correction with Large Language Models (2025.emnlp-main)

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Zhengdong Yang, Zhen Wan, Sheng Li, Chao-Han Huck Yang, Chenhui Chu

Text Generation

Challenge:	Large language models can fix recognition or translation errors that traditional rescoring cannot fix.
Approach:	They propose a benchmark for GER that covers both ASR and speech-to-text translation across 15 languages and 28 language pairs.
Outcome:	The proposed benchmark is built on common voice 20.0 and CoVoST-2 with Whisper and SeamlessM4T.

The Staircase of Ethics: Probing LLM Value Priorities through Multi-Step Induction to Complex Moral Dilemmas (2025.emnlp-main)

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Ya Wu, Qiang Sheng, Danding Wang, Guang Yang, Yifan Sun, Zhengjia Wang, Yuyan Bu, Juan Cao

Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation (2022.findings-naacl)

Challenge:	Existing evaluations of LLMs' moral reasoning capabilities rely on single-step evaluations, ignoring how models adapt to evolving ethical challenges.
Approach:	They propose a framework to evaluate evolving moral judgments of large language models (LLMs) using multi-step moral dilemma questionnaires.
Outcome:	The proposed framework enables a fine-grained analysis of how LLMs adjust their moral reasoning across escalating dilemmas.

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Junwei Yang, Zequn Liu, Ming Zhang, Sheng Wang

Structured Data in NLP

Challenge:	Neural text generation is a novel technique to describe biomedical pathways without manually curation.
Approach:	They propose a new dataset Pathway2Text which contains 2,367 pairs of biomedical pathways and textual descriptions.
Outcome:	The proposed method improves on both Graph2Text and Text2Graph tasks and can be used as a benchmark for biomedical named entity recognition.

Learning from the Irrecoverable: Error-Localized Policy Optimization for Tool-Integrated LLM Reasoning (2026.acl-long)

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Qiao Liang, Yuke Zhu, Chao Ge, Lei Yang, Ying Shen, Bo Zheng, Sheng Guo

DYNTEXT: Semantic-Aware Dynamic Text Sanitization for Privacy-Preserving LLM Inference (2025.findings-acl)

Challenge:	Tool-integrated reasoning (TIR) enables LLM agents to solve tasks through planning, tool use, and iterative revision, but outcome-only reinforcement learning suffers from sparse, delayed rewards and weak step-level credit assignment.
Approach:	They propose a tool-integrated reasoning approach that localizes the first irrecoverable step and leverages it for fine-grained credit assignment.
Outcome:	The proposed algorithm outperforms strong Agentic RL benchmarks in math, science QA, and code execution with additional gains in Pass@K and Major@K scaling, rollout ranking quality, and tool-call efficiency.

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Juhua Zhang, Zhiliang Tian, Minghang Zhu, Yiping Song, Taishu Sheng, Siyi Yang, Qiunan Du, Xinwang Liu, Minlie Huang, Dongsheng Li

Recovering dropped pronouns in Chinese conversations via modeling their referents (N19-1)

Challenge:	Existing methods to protect privacy of sensitive data are differential privacy (DP) and DP is used to protect users from privacy leakage.
Approach:	They propose an LDP-based Dynamic Text sanitization for privacy-preserving LLM inference that dynamically constructs semantic-aware adjacency lists of sensitive tokens to sample non-sensitive tokens for perturbation.
Outcome:	The proposed model excels on three datasets.

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Jingxuan Yang, Jianzhuo Tong, Si Li, Sheng Gao, Jun Guo, Nianwen Xue

Dialogue Systems & Conversational Agents

Challenge:	Pronouns are often dropped in conversational genres as their referents can be easily understood from context.
Approach:	They propose an end-to-end neural network model to recover dropped pronouns in conversational data.
Outcome:	The proposed model improves on three different conversational genres.

Evolution in Simulation: AI-Agent School with Dual Memory for High-Fidelity Educational Dynamics (2025.findings-emnlp)

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Sheng Jin, Haoming Wang, Zhiqi Gao, Yongbo Yang, Bao Chunjia, Chengliang Wang

Challenge:	Large language models (LLMs) based Agents are increasingly pivotal in simulating complex human systems and interactions.
Approach:	They propose an AI-Agent School system that leverages agents for simulating educational dynamics.
Outcome:	The proposed system can simulate complex educational dynamics in simulated schools.

Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization (2023.emnlp-main)

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Tianshi Che, Ji Liu, Yang Zhou, Jiaxiang Ren, Jiwen Zhou, Victor Sheng, Huaiyu Dai, Dejing Dou

Retrieved Sequence Augmentation for Protein Representation Learning (2024.emnlp-main)

Challenge:	Prompt tuning of Large Language Models (LLMs) can incur performance degradation or low training efficiency.
Approach:	They propose a prompt tuning approach with Adaptive Optimization to enable efficient FL of LLMs.
Outcome:	The proposed approach improves performance and efficiency simultaneously and addresses client drift problems on both the device and server sides.

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Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Sheng Wang, Lingpeng Kong

FinMRAGBench: A Realistic and Complex Benchmark for Multi-Modal RAG in Financial Document Analysis (2026.findings-acl)

Challenge:	Using multiple sequence alignments (MSA) to extract evolutionary knowledge is limited.
Approach:	They propose to use multiple sequence alignments to augment protein representations . they propose to employ Retrieved Sequence Augmentation to enhance protein representation learning .
Outcome:	The proposed method surpasses MSA Transformer by 5% in structural and property prediction tasks while being 373 times faster.

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Shouqing Yang, Qi Zhang, Yuhang Yang, Ruikang Xu, Yuwei Hou, Zhulin Jia, Lirong Gao, Haobo Wang, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Gang Chen

Information Retrieval

Challenge:	Existing benchmarks for realistic financial analysis fail to capture realistic financial situations involving cross-document retrieval, multi-page evidence integration, and diverse analytical tasks.
Approach:	They propose a multi-modal financial RAG benchmark that evaluates large language models in realistic financial analysis settings.
Outcome:	The proposed framework achieves the strongest overall performance across all models.

AMPO: Automatic Multi-Branched Prompt Optimization (2024.emnlp-main)

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Sheng Yang, Yurong Wu, Yan Gao, Zineng Zhou, Bin Zhu, Xiaodi Sun, Jian-Guang Lou, Zhiming Ding, Anbang Hu, Yuan Fang, Yunsong Li, Junyan Chen, Linjun Yang

Generative Error Correction for Emotion-aware Speech-to-text Translation (2025.findings-acl)

Challenge:	Existing prompt engineering techniques are limited to producing single flow instructions, struggling with handling diverse patterns.
Approach:	They propose an automatic prompt optimization method that iteratively develops a multi-branched prompt using failure cases as feedback.
Outcome:	The proposed method achieves the best results across five tasks and demonstrates significant optimization efficiency due to adoption of a minimal search strategy.

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Zhengdong Yang, Sheng Li, Chenhui Chu

Text Generation

Challenge:	Despite recent advances in speech-to-text translation, the impact of the emotion content has been overlooked.
Approach:	They propose to use generative error correction (GER) to generate the translation based on the decoded N-best hypotheses and combine emotion and sentiment labels into the LLM finetuning process to enable the model to consider the emotion content.
Outcome:	The proposed model can translate speech in English-Chinese using GER and emotion and sentiment labels.

MetaFill: Text Infilling for Meta-Path Generation on Heterogeneous Information Networks (2022.emnlp-main)

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Zequn Liu, Kefei Duan, Junwei Yang, Hanwen Xu, Ming Zhang, Sheng Wang

Generative Giants, Retrieval Weaklings: Why do Multimodal Large Language Models Fail at Multimodal Retrieval? (2026.findings-acl)

Challenge:	Existing meta-path generation methods cannot fully exploit rich textual information in HINs.
Approach:	They propose a text-infilling-based approach to generate meta-paths from textual information in HINs.
Outcome:	The proposed approach can classify edges in the zero-shot setting, where existing methods cannot generate meta-paths.

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Hengyi Feng, Zeang Sheng, Meiyi Qiang, Yang Li, Wentao Zhang

Adversarial Attacks Against Automated Fact-Checking: A Survey (2025.emnlp-main)

Challenge:	Rapid advances in multimodal large language models have revolutionized cross-modality understanding.
Approach:	They propose a method that uses whitening transformations to adjust MLLM representation spaces . they propose ML models that are dominated by textual semantics and visual semantics .
Outcome:	The proposed approach improves zero-shot multimodal retrieval performance without fine-tuning efforts.

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Fanzhen Liu, Sharif Abuadbba, Kristen Moore, Surya Nepal, Cecile Paris, Jia Wu, Jian Yang, Quan Z. Sheng

Precision or Recall? An Analysis of Image Captions for Training Text-to-Image Generation Model (2024.findings-emnlp)

Challenge:	Existing fact-checking systems are vulnerable to adversarial attacks that manipulate or generate claims, evidence, or claim-evidence pairs.
Approach:	They examine the impact of adversarial attacks on existing AFC systems and examine their impact on existing ones.
Outcome:	The findings highlight the need for resilient fact-checking frameworks in limiting misinformation spread and supporting public trust.

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Sheng Cheng, Maitreya Patel, Yezhou Yang

AROMA: Autonomous Rank-one Matrix Adaptation (2025.emnlp-main)

Challenge:	Recent advances in text-to-image models have demonstrated remarkable capabilities in image synthesis.
Approach:	They analyze the critical role of caption precision and recall in text-to-image model training.
Outcome:	The proposed model trains with synthetic captions that show similar behavior to those trained on human-annotated captions.

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Hao Nan Sheng, Zhi-Yong Wang, Hing Cheung So, Mingrui Yang

Trainable Hard Negative Examples in Contrastive Learning for Unsupervised Abstractive Summarization (2024.findings-eacl)

Challenge:	Low-rank adaptation (LoRA) and adaptive low-rank adaption (AdaLoRa) are effective for large language models but are expensive as model sizes escalate into hundreds of billions of parameters.
Approach:	They propose a framework that automatically builds up rank-one components with very few trainable parameters that gradually diminish to zero.
Outcome:	The proposed framework significantly reduces parameters compared to LoRA and AdaLoRA while maintaining subspace independence.

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Haojie Zhuang, Wei Emma Zhang, Chang Dong, Jian Yang, Quan Sheng

MR-ALIGN: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models (2026.findings-acl)

Challenge:	Existing methods for contrastive learning rely on manual negative examples and are poor in quality and adaptability during training.
Approach:	They propose a framework that learns trainable negative examples for contrastive learning in unsupervised abstractive summarization by combining a negative example network and a representation network.
Outcome:	The proposed approach eliminates the need for manual negative example design and improves on two benchmark datasets.

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Xinming Wang, Jian Xu, Bin Yu, Sheng Lian, yi Chen, Boran Wang, Yingjian Zhu, Hongzhu Yi, Hong-Ming Yang, Han Hu, Cheng-Lin Liu, Xu-Yao Zhang

Aligning Large Multimodal Models with Factually Augmented RLHF (2024.findings-acl)

Challenge:	Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited.
Approach:	They propose a Meta-Reasoning informed alignment framework that quantifies state-transition probabilities along the model’s thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments.
Outcome:	Empirical evaluations of four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning.

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Zhiqing Sun, Sheng Shen, Shengcao Cao, Haotian Liu, Chunyuan Li, Yikang Shen, Chuang Gan, Liangyan Gui, Yu-Xiong Wang, Yiming Yang, Kurt Keutzer, Trevor Darrell

SpiderFlow: Efficient Topology-Aware Scheduling for LLM Training Across Decentralized GPU Clusters (2026.acl-long)

Challenge:	Large Multimodal Models (LMMs) are built across modalities and the misalignment between two modality can result in "hallucination" . developing LMMs faces challenges such as a lack of data and a limited number of data sets.
Approach:	They propose a new algorithm that augments the reward model with additional factual information such as image captions and ground-truth multi-choice options.
Outcome:	The proposed approach improves on the LLaVA-Bench dataset with the 96% performance level of the text-only GPT-4 and an improvement of 60% on MMHAL-BENCH over other baselines.

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Zihan Chang, Shuibing He, Bo Zhou, Sheng Xiao, Siling Yang, Rui Wang, Zhe Pan

Information Extraction & Text Mining

Challenge:	Existing approaches to training large language models lack topologyaware task scheduling mechanisms and model parallelization strategies.
Approach:	They propose a topology-aware scheduling system specifically designed for decentralized GPU clusters . they propose heuristic methods at the inter-cluster level with ILP-based optimization within clusters.
Outcome:	The proposed system reduces job completion time by 1.2-1.3 and improves throughput by 1.12-1.25 . it also reduces scheduling overhead by 20-90 on average compared to state-of-the-art scheduling systems.

Towards Scalable Lightweight GUI Agents via Multi-role Orchestration (2026.findings-acl)

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Ziwei Wang, Junjie Zheng, Leyang Yang, Sheng Zhou, Xiaoxuan Tang, Fang Zhouhua, Zhiwei Liu, Dajun Chen, Yong Li, Jiajun Bu

Challenge:	Advanced GUI agents suffer from prohibitive deployment costs on resource-constrained devices.
Approach:	They propose a lightweight GUI agent with GUI-specific knowledge and task scalability . LAMO-3B supports monolithic execution and MAS-style orchestration .
Outcome:	The proposed GUI agent LAMO-3B supports monolithic execution and MAS-style orchestration.

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.

LeTS: Learning to Think-and-Search via Process-and-Outcome Reward Hybridization (2025.emnlp-main)

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Qi Zhang, Shouqing Yang, Lirong Gao, Hao Chen, Xiaomeng Hu, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Haobo Wang, Junbo Zhao

CLLE: A Benchmark for Continual Language Learning Evaluation in Multilingual Machine Translation (2022.findings-emnlp)

Challenge:	Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL).
Approach:	They propose a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation.
Outcome:	The proposed framework can boost LLMs’ reasoning ability by integrating external knowledge sources through retrieval-augmented generation (RAG) The proposed model can mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation.

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Han Zhang, Sheng Zhang, Yang Xiang, Bin Liang, Jinsong Su, Zhongjian Miao, Hui Wang, Ruifeng Xu

Multilinguality

Challenge:	Existing benchmarks for Continual Language Learning (CLL) are limited due to the complexity of the task and the lack of unified benchmarks.
Approach:	They propose a Continual Language Learning Evaluation benchmark CLLE in multilingual translation.
Outcome:	The proposed method is effective when compared with other strong benchmarks.

Calibrating Pseudo-Labeling with Class Distribution for Semi-supervised Text Classification (2025.emnlp-main)

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Weiyi Yang, Richong Zhang, Junfan Chen, Jiawei Sheng

Information Extraction & Text Mining

Challenge:	Existing studies develop effective pseudo-labeling methods, but they struggle with unlabeled data that have imbalanced classes mismatched with the labeled data.
Approach:	They propose to use pseudo-labeling to train text classification models with few labeled data and massive unlabeled data.
Outcome:	Empirical results show that the proposed model outperforms state-of-the-art methods on 3 common benchmarks.

Learning From the Source Document: Unsupervised Abstractive Summarization (2022.findings-emnlp)

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Haojie Zhuang, Wei Emma Zhang, Jian Yang, Congbo Ma, Yutong Qu, Quan Z. Sheng