Papers by Lu Jiang

ScaleBox: Enabling High-Fidelity and Scalable Code Verification for Large Language Models (2026.acl-demo)

Challenge:	Pre-trained language models perform well on learning sentence semantics when fine-tuned with supervised data.
Approach:	They conduct a thorough examination of pretrained model based unsupervised sentence embeddings.
Outcome:	The proposed approach improves on whitening-based vector normalization with less than 10 lines of code.

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Jiasheng Zheng, Xin Zheng, Boxi Cao, Pengbo Wang, Zhengzhao Ma, Qiming Zhu, Jiazhen Jiang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

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

Challenge:	Existing code sandboxes fail to provide accurate verification and efficiency under high-concurrency workloads.
Approach:	They propose a high-fidelity code verification system that provides sandbox feedback for RL training and evaluation.
Outcome:	The proposed system outperforms heuristic-matching baselines on LiveCodeBench and training stability on high-concurrency workloads.

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

Teach Small Models to Reason by Curriculum Distillation (2025.emnlp-main)

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

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Wangyi Jiang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Hit the Nail on the Head: Parameter-Efficient Multi-task Tuning via Human Language Intervention (2024.findings-emnlp)

Challenge:	Large Reasoning Models (LRMs) show strong System-2-style reasoning, but at the cost of significant computational overhead.
Approach:	They propose a two-stage curriculum distillation framework which builds a robust internal problem-solving student model and then teaches the student model to externalize this knowledge as explicit reasoning.
Outcome:	The proposed model outperforms single-stage baselines on mathematical benchmarks and significantly outperformed LRMs on complex tasks.

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Wenxuan Lu, Songhao Jiang, Wang Yijing, Tianning Zang

ChatMap: Mining Human Thought Processes for Customer Service Chatbots via Multi-Agent Collaboration (2025.findings-acl)

Challenge:	Recent studies show that PEFT on small pre-trained language models improves multitasking capabilities.
Approach:	They propose a multi-task learning framework that enables transfer of prior knowledge across tasks . they attach task descriptions to input samples and map them to task embeddings .
Outcome:	The proposed method improves performance on a T5 model and in decoder-only models .

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Xinyi Jiang, Tianyi Hu, Yuheng Qin, Guoming Wang, Zhou Huan, Kehan Chen, Gang Huang, Rongxing Lu, Siliang Tang

Inference-Time Policy Adapters (IPA): Tailoring Extreme-Scale LMs without Fine-tuning (2023.emnlp-main)

Challenge:	Existing methods for enhancing dialogue performance rely on summarizing behavior . e-commerce chatbots need to align their dialogue strategies with human behavior to achieve coherent, human-like conversations with customers.
Approach:	They propose a method to extract core patterns from dialogue data and integrate them into models by mining service thought processes using a multi-agent aPproach.
Outcome:	The proposed method outperforms manual methods and outperfies baselines on Taobao in China.

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Ximing Lu, Faeze Brahman, Peter West, Jaehun Jung, Khyathi Chandu, Abhilasha Ravichander, Prithviraj Ammanabrolu, Liwei Jiang, Sahana Ramnath, Nouha Dziri, Jillian Fisher, Bill Lin, Skyler Hallinan, Lianhui Qin, Xiang Ren, Sean Welleck, Yejin Choi

Immediate Inference: The Missing Foundation in Large Language Model Logical Reasoning (2026.acl-long)

Challenge:	Extreme-scale language models have shown exceptional performance on a variety of language tasks, but the degree of control offered by these models through pure prompting is limited.
Approach:	They propose an inference-time policy adapter which tailors a large base model without fine-tuning it.
Outcome:	The proposed model outperforms baseline methods on five challenging text generation tasks and even over GPT-4.

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Sihang Jiang, Zhiyu Lu, Keyi Wang, Jiaqing Liang, Yanghua Xiao, Xiaojun Meng, Jiansheng Wei

mCLIP: Multilingual CLIP via Cross-lingual Transfer (2023.acl-long)

Challenge:	Recent work on LLMs has focused on fine-grained skill decomposition and consistency probing at the propositional level.
Approach:	They propose a benchmark evaluating immediate inference that evaluates elemental operations over categorical propositions and proposes a model that uses immediate inferential reasoning.
Outcome:	The proposed benchmark demonstrates that models lack robust operator grounding, oscillating between structural reasoning and surface pattern matching, inconsistent handling of quantifiers and negation.

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Guanhua Chen, Lu Hou, Yun Chen, Wenliang Dai, Lifeng Shang, Xin Jiang, Qun Liu, Jia Pan, Wenping Wang

Multilinguality

Challenge:	Existing multilingual vision-language pretrained models are biased towards English due to the lack of sufficient non-English image-text pairs.
Approach:	They propose to train a retrieval-efficient dual-stream multilingual VLP model by aligning CLIP model and a multilingual text encoder through a novel Triangle Cross-modal Knowledge Distillation method.
Outcome:	Empirical results show that mCLIP achieves new state-of-the-art performance for both zero-shot and finetuned multilingual image-text retrieval tasks.

Few-Shot Multimodal Named Entity Recognition Based on Mutlimodal Causal Intervention Graph (2024.lrec-main)

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Feihong Lu, Xiaocui Yang, Qian Li, Qingyun Sun, Ke Jiang, Cheng Ji, Jianxin Li

Impossible Distillation for Paraphrasing and Summarization: How to Make High-quality Lemonade out of Small, Low-quality Model (2024.naacl-long)

Challenge:	Existing methods for multimodal named entity recognition are limited due to limited resources.
Approach:	They propose a Few-shot Multimodal Named Entity Recognition task to address these relation types by constructing a multimodal graph and a new multimodal causal intervention strategy.
Outcome:	The proposed model improves on two multimodal named entity recognition datasets.

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Jaehun Jung, Peter West, Liwei Jiang, Faeze Brahman, Ximing Lu, Jillian Fisher, Taylor Sorensen, Yejin Choi

LiteVL: Efficient Video-Language Learning with Enhanced Spatial-Temporal Modeling (2022.emnlp-main)

Challenge:	Impossible Distillation is a framework for paraphrasing and sentence summarization that can be trained from a low-quality teacher model.
Approach:	They propose a framework that distills a high-quality dataset from a low-quality teacher . they hypothesize and verify the paraphrastic proximity intrinsic to pre-trained LMs .
Outcome:	The proposed framework outperforms baseline models on unconstrained paraphrase generation and sentence summarization benchmarks.

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Dongsheng Chen, Chaofan Tao, Lu Hou, Lifeng Shang, Xin Jiang, Qun Liu

Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation (2024.lrec-main)

Challenge:	Recent large-scale video-language pre-trained models have shown appealing performance on downstream tasks.
Approach:	They propose a video-text model that adapts a pre-trained image-language model into a text-based model without heavy pre-training.
Outcome:	The proposed model outperforms existing models on video-text retrieval and video question answering tasks without heavy pre-training.

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Zhigang Chen, Benjia Zhou, Jun Li, Jun Wan, Zhen Lei, Ning Jiang, Quan Lu, Guoqing Zhao

LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)

Challenge:	Previous Sign Language Translation methods have relied on gloss annotations to improve performance, but labeling high-quality glosses is labor-intensive and inefficient.
Approach:	They propose to integrate Large Language Model (LLM) into SLT by factorizing learning into two stages to improve the learning curve.
Outcome:	The proposed approach improves on three SLT datasets conducted under the gloss-free setting.

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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang

CKnowEdit: A New Chinese Knowledge Editing Dataset for Linguistics, Facts, and Logic Error Correction in LLMs (2025.acl-long)

Challenge:	Currently, most research focuses on the bidding algorithms used within auction mechanisms.
Approach:	They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process.
Outcome:	The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process.

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Jizhan Fang, Tianhe Lu, Yunzhi Yao, Ziyan Jiang, Xin Xu, Huajun Chen, Ningyu Zhang

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

Challenge:	CKnowEdit is the first-ever knowledge editing dataset designed to correct linguistic, factual, and logical errors in Large Language Models.
Approach:	They propose a Chinese knowledge editing dataset to correct linguistic, factual, and logical errors in Large Language Models.
Outcome:	The proposed dataset highlights the challenges that LLMs face in mastering Chinese . CKnowEdit can correct linguistic, factual, and logical errors in Chinese, the authors show .

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Green & Sustainable NLP

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

Few-shot Named Entity Recognition via Superposition Concept Discrimination (2024.lrec-main)

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Jiawei Chen, Hongyu Lin, Xianpei Han, Yaojie Lu, Shanshan Jiang, Bin Dong, Le Sun

Structured Pruning for Efficient Generative Pre-trained Language Models (2023.findings-acl)

Challenge:	Few-shot named entity recognition (NER) aims to identify entities of target types with limited number of illustrative instances.
Approach:	They propose a superposition concept discriminator which solves the intrinsic generalization problem by an active learning paradigm.
Outcome:	The proposed model significantly improves few-shot named entity recognition (FS-NER) with minimal additional efforts.

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Chaofan Tao, Lu Hou, Haoli Bai, Jiansheng Wei, Xin Jiang, Qun Liu, Ping Luo, Ngai Wong

Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment (2022.acl-long)

Challenge:	Large-scale generative Pre-trained Language Models (PLMs) are limited in their deployment in real-world applications.
Approach:	They propose to prune the feed-forward networks of generative pre-trained language models to smaller widths without designing extra operators.
Outcome:	The proposed method achieves 1.51x/6.96x inference speedup on GPU/CPU with 67% size reduction.

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Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang

PaperRegister: Boosting Flexible-grained Paper Search via Hierarchical Register Indexing (2026.acl-long)

Challenge:	Existing methods to predict missing facts in knowledge graphs are limited in language alignment . SS-AGA uses seed alignment as an edge type to fuses all KGs as a whole graph .
Approach:	They propose a self-supervised adaptive graph alignment method that fuses all KGs as a whole graph by regarding alignment as 'a new edge type' they propose SS-AGA method that uses relation-aware attention weights to capture potential alignment pairs in a new paradigm.
Outcome:	The proposed method can predict missing facts in a knowledge graph (KG) but language alignment is scarce and new alignment identification is noisy.

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Zhuoqun Li, Xuanang Chen, Hongyu Lin, Yaojie Lu, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun

Information Retrieval

Challenge:	Existing paper search systems lack detailed information to support finer-grained queries.
Approach:	They propose a paper-based index that transforms abstract-based corpus index into hierarchical index tree and offline can support paper search queries.
Outcome:	The proposed system achieves the SOTA performance and excels in fine-grained scenarios.

ECHA: Jailbreaking LVLMs via the Mismatch between Implicit Semantic Reconstruction and Explicit Safety Alignment (2026.findings-acl)

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Chenxing Xu, Junyong Jiang, Zehu Zhang, Lu Dong

A Table-to-Text Framework with Heterogeneous Multidominance Attention and Self-Evaluated Multi-Pass Deliberation (2023.findings-emnlp)

Challenge:	Existing safety guardrails fail to intercept latent intent, whereas LVLMs can implicitly synthesize holistic malicious semantics from fragmented visual cues.
Approach:	They propose an Emoji Chain Hinting Attack (ECHA) framework that decouples sensitive concepts into semantically related emoji chains and structural text masks.
Outcome:	The proposed framework outperforms existing baselines and bypasses safety guardrails in over 81% of instances with a single attempt.

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Xi Chen, Xinjiang Lu, Haoran Xin, Wenjun Peng, Haoyang Duan, Feihu Jiang, Jingbo Zhou, Hui Xiong

Structured Data in NLP

Challenge:	Table-to-text works have been widely applied in different domains, such as weather forecast and financial report generation.
Approach:	They propose a table-to-text approach on top of Self-evaluated multi-pass Generation and Heterogenous Multidominance Attention to explore the hierarchical structure.
Outcome:	The proposed method outperforms several SOTA methods quantitatively and qualitatively on three public datasets.

Compression of Generative Pre-trained Language Models via Quantization (2022.acl-long)

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Chaofan Tao, Lu Hou, Wei Zhang, Lifeng Shang, Xin Jiang, Qun Liu, Ping Luo, Ngai Wong

Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning (2026.acl-long)

Challenge:	Existing methods to compress generative pre-trained language models fail on generative tasks due to homogeneous word embeddings and limited memory.
Approach:	They propose a token-level contrastive distillation method to learn distinguishable word embeddings and a module-wise dynamic scaling method to make quantizers adaptive to different modules.
Outcome:	The proposed method outperforms the state-of-the-art compression methods on generative PLMs by a clear margin.

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Zekai Lin, Chao Xue, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Lei Jiang, Yu Lu, Bob Simons, Shuang Liang, Minlong Peng

AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

Challenge:	Recent approaches to fine-tuning of large language models suffer from task interference and catastrophic forgetting.
Approach:	They propose a fine-tuning framework that adapts isolation decisions based on online estimates of parameter importance.
Outcome:	The proposed framework reduces interference and forgetting while releasing outdated parameters to recover plasticity.

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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang

Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models (2026.acl-long)

Challenge:	Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents.
Approach:	They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
Outcome:	The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.

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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim

NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation (2023.findings-emnlp)

Challenge:	Incomplete learning is widespread and heterogeneous in large language models . authors identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between SFT supervision and pre-training knowledge, internal inconsistencies within SFT data, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns.
Approach:	They propose a diagnostic-first framework that maps incomplete learning to causes . they identify five recurrent sources of incomplete learning: missing prerequisite knowledge, conflicts between supervision and pre-training knowledge, internal inconsistencies, left-side forgetting during sequential fine-tuning, and insufficient optimization for rare or complex patterns.
Outcome:	The proposed framework maps incomplete learning to causes using observable training and inference signals.

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Peter West, Ronan Bras, Taylor Sorensen, Bill Lin, Liwei Jiang, Ximing Lu, Khyathi Chandu, Jack Hessel, Ashutosh Baheti, Chandra Bhagavatula, Yejin Choi

ChatGPT Is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models (2024.lrec-main)

Challenge:	a new commonsense knowledge model, NovaCOMET, combines knowledge and general task models.
Approach:	They propose an open commonsense knowledge model that combines knowledge and general task models.
Outcome:	The proposed model matches or exceeds existing knowledge models on commonsense reasoning tasks.

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Ning Bian, Xianpei Han, Le Sun, Hongyu Lin, Yaojie Lu, Ben He, Shanshan Jiang, Bin Dong

Natural Language Interfaces

Challenge:	acquiring and representing commonsense in machines has posed a long-standing challenge (Li et al., 2021; Zhang e t al, 2022; Zhou e al. 2023) .
Approach:	They use a commonsense-based LLM to evaluate ChatGPT's commonsensing abilities by analyzing 11 datasets and generating knowledge descriptions.
Outcome:	The proposed model can achieve good QA accuracies while still struggling with certain domains of datasets.

GhostBERT: Generate More Features with Cheap Operations for BERT (2021.acl-long)

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Zhiqi Huang, Lu Hou, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu

SARA: Salience-Aware Reinforced Adaptive Decoding for Large Language Models in Abstractive Summarization (2025.acl-long)

Challenge:	Existing studies show that some parameters in pre-trained language models can be pruned away without severe accuracy degradation.
Approach:	They propose a method which generates more features with very cheap operations from the remaining features and can be applied to unpruned BERT models to enhance their performance.
Outcome:	Empirical results on the GLUE benchmark on three backbone models (i.e., BERT, RoBERTa and ELECTRA) verify the efficacy of the proposed method.

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Nayu Liu, Junnan Zhu, Yiming Ma, Zhicong Lu, Wenlei Xu, Yong Yang, Jiang Zhong, Kaiwen Wei

Global Eye: Breaking the “Fixed Thinking Pattern” during the Instruction Expansion Process (2025.acl-long)

Challenge:	Existing decoding strategies neglect the explicit use of salient contextual information and rely on static hyperparameters to fix the balance between contextual and prior knowledge.
Approach:	They propose a salience-aware reinforced adaptive decoding (SARA) which incorporates salient contextual information and allows the model to determine reliance on source document's context, salient context, and model's prior knowledge based on pointwise mutual information.
Outcome:	The proposed model improves the quality and faithfulness of summaries across LLMs without modifying their weights.

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Wenxuan Lu, Wei Liu, Jian Luan, Bin Wang, Songhao Jiang, Tianning Zang

Teaching According to Talents! Instruction Tuning LLMs with Competence-Aware Curriculum Learning (2025.findings-emnlp)

Challenge:	Existing methods focus on constructing multi-perspective prompts to expand instructions, overlooking the “Fixed Thinking Pattern” issue of Large Language Models.
Approach:	They propose a method that analyzes the statistical characteristics of newly generated instructions and updates the prompts after a fixed number of instruction expansions.
Outcome:	The proposed method surpasses open-source LLMs and GPT3.5 in several metrics.

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Yangning Li, Tingwei Lu, Yinghui Li, Yankai Chen, Wei-Chieh Huang, Wenhao Jiang, Hui Wang, Hai-Tao Zheng, Philip S. Yu

CLAIM: Mitigating Multilingual Object Hallucination in Large Vision-Language Models with Cross-Lingual Attention Intervention (2025.acl-long)

Challenge:	Efficient instruction tuning aims to enhance the ultimate performance of large language models (LLMs) current methods suffer from the curriculum rigidity, resulting in a fixed and potentially sub-optimal learning trajectory.
Approach:	a framework for efficient instruction tuning is proposed to address the issue of curriculum rigidity . current methods rely on static heuristic difficulty metrics and fail to adapt to evolving capabilities .
Outcome:	Efficient instruction tuning aims to enhance the ultimate performance of large language models . current methods suffer from the curriculum rigidity, resulting in a fixed learning trajectory .

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Zekai Ye, Qiming Li, Xiaocheng Feng, Libo Qin, Yichong Huang, Baohang Li, Kui Jiang, Yang Xiang, Zhirui Zhang, Yunfei Lu, Duyu Tang, Dandan Tu, Bing Qin

Reasoning

Challenge:	Large Vision-Language Models (LVLMs) have impressive multimodal abilities but remain prone to multilingual object hallucination.
Approach:	They propose a cross-lingual attention intervention method to mitigate multilingual object hallucination in LVLMs by aligning attention patterns.
Outcome:	The proposed method improves 13.56% (up to 30%) on the POPE and 21.75% on the hallucination subsets across languages.

RORA: Robust Free-Text Rationale Evaluation (2024.acl-long)

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Zhengping Jiang, Yining Lu, Hanjie Chen, Daniel Khashabi, Benjamin Van Durme, Anqi Liu

Code Execution with Pre-trained Language Models (2023.findings-acl)

Challenge:	Existing metrics rely on degree to which rationale supports a label, but they fail to evaluate rationales that inadvertently leak the label.
Approach:	They propose a RObust free-text RAtionale evaluation against label leakage that quantifies the new information supplied by a rationale to justify the label.
Outcome:	The proposed evaluation outperforms existing methods in evaluating human-written, synthetic, or model-generated rationales, particularly demonstrating robustness against label leakage.

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Chenxiao Liu, Shuai Lu, Weizhu Chen, Daxin Jiang, Alexey Svyatkovskiy, Shengyu Fu, Neel Sundaresan, Nan Duan

Threshold Filtering Packing for Supervised Fine-Tuning: Training Related Samples within Packs (2025.naacl-long)

Challenge:	Pre-trained code intelligence models ignore the execution trace and only rely on source code and syntactic structures to understand code execution.
Approach:	They develop a mutation-based data augmentation technique to create a Python dataset and task for code execution that challenges existing models.
Outcome:	The proposed model outperforms existing models on code execution and shows its potential for zero-shot code-to-code search and text-to code generation.

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Jiancheng Dong, Lei Jiang, Wei Jin, Lu Cheng

Wukong-Reader: Multi-modal Pre-training for Fine-grained Visual Document Understanding (2023.acl-long)

Challenge:	Randomly concatenating data points can lead to cross-contamination due to the significant difference in their subject matter.
Approach:	They propose a method that randomly concatenates data of varying lengths until reaching the designed maximum length to optimize context length and reduce padding.
Outcome:	The proposed method significantly improves performance on GSM8K and HumanEval, and also improves fairness and accuracy by 15%.

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Haoli Bai, Zhiguang Liu, Xiaojun Meng, Li Wentao, Shuang Liu, Yifeng Luo, Nian Xie, Rongfu Zheng, Liangwei Wang, Lu Hou, Jiansheng Wei, Xin Jiang, Qun Liu

ModRWKV: Transformer Multimodality in Linear Time (2025.emnlp-main)

Challenge:	Existing solutions for visual document understanding lack granularity of document textlines.
Approach:	They propose a supervised pre-training program to leverage structural knowledge nested in document textlines to achieve fine-grained alignment between visual regions and texts.
Outcome:	The proposed system performs better on various VDU tasks in English and Chinese.

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Jiale Kang, Ziyin Yue, Qingyu Yin, Rui Jiang, Weile Li, Zening Lu, Zhouran Ji

BinaryBERT: Pushing the Limit of BERT Quantization (2021.acl-long)

Challenge:	Currently, multimodal studies are based on large language models with quadratic-complexity Transformer architectures.
Approach:	They propose a decoupled multimodal framework built upon the RWKV7 architecture as its LLM backbone and a lightweight architecture to achieve multi-source information fusion.
Outcome:	The proposed framework achieves multi-source information fusion through dynamically adaptable heterogeneous modality encoders.

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Haoli Bai, Wei Zhang, Lu Hou, Lifeng Shang, Jin Jin, Xin Jiang, Qun Liu, Michael Lyu, Irwin King

Reason Only When Needed: Efficient Generative Reward Modeling via Model-Internal Uncertainty (2026.findings-acl)

Challenge:	Recent pre-trained language models have achieved remarkable performance improvement in various tasks, but the improvement generally comes at the cost of increasing model size and computation.
Approach:	They propose a binary quantization technique which initializes binaryBERT by splitting from a ternary network.
Outcome:	The proposed model achieves state-of-the-art performance on the GLUE and SQUAD benchmarks while being 24x smaller.

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Chao Xue, Yao Wang, Mengqiao Liu, Di Liang, Xingsheng Han, Peiyang Liu, Xianjie Wu, Chenyao Lu, Lei Jiang, Yu Lu, Haibo Shi, Shuang Liang, Minlong Peng, Flora D. Salim

Visual Prompt Tuning for Few-Shot Text Classification (2022.coling-1)

Challenge:	Existing approaches to generating reward models rely on voting-based mechanisms to evaluate CoT outputs.
Approach:	They propose an efficient generative reward modeling framework grounded in model-internal uncertainty.
Outcome:	The proposed framework reduces inference cost while improving answer accuracy.

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Jingyuan Wen, Yutian Luo, Nanyi Fei, Guoxing Yang, Zhiwu Lu, Hao Jiang, Jie Jiang, Zhao Cao

When Efficiency Meets Safety: A Benchmark Security Analysis of KV Cache Compression in Large Language Models (2026.acl-long)

Challenge:	Existing work on pretraining models for text classification uses image encoders instead of visual prompts.
Approach:	They propose a method to deploy large-scale pre-trained models in the prompt-tuning paradigm in few-shot learning.
Outcome:	The proposed method outperforms the most recent prompt-tuning methods on five public text classification datasets.

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Xiaoxiao Ma, Kuofeng Gao, Zeyi Lu, Wenxi Jiang, Hao Fang, Hao Wu, Bin Chen, Shu-Tao Xia

Slot Transferability for Cross-domain Slot Filling (2021.findings-acl)

Challenge:	Key-Value (KV) caching is widely used in large language models to enable long-context inference efficiently, yet its security implications remain underexplored.
Approach:	They propose a history-aware, per-head feedback merging strategy that prevents safety degradation while maintaining efficiency.
Outcome:	The proposed strategy prevents safety degradation while maintaining efficiency.

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Hengtong Lu, Zhuoxin Han, Caixia Yuan, Xiaojie Wang, Shuyu Lei, Huixing Jiang, Wei Wu

Semantic Parsing

Challenge:	Existing work on slot filling uses labeled data from source domains to train a model for target domains.
Approach:	They propose a model-agnostic Slot Transferability Measure (STM) to evaluate the transferability from a source slot to a target slot.
Outcome:	The proposed method outperforms state-of-the-art models on multiple datasets and models.

Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning (2021.acl-long)

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Pan Lu, Ran Gong, Shibiao Jiang, Liang Qiu, Siyuan Huang, Xiaodan Liang, Song-Chun Zhu

Optimizing Decomposition for Optimal Claim Verification (2025.acl-long)

Challenge:	Existing methods for solving geometric problems are either small in scale or not publicly available.
Approach:	They propose a large-scale benchmark for geometric problem solving using formal language and symbolic reasoning.
Outcome:	The proposed approach parses geometry problems into formal language and performs symbolic reasoning step by step.

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Yining Lu, Noah Ziems, Hy Dang, Meng Jiang

Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)

Challenge:	Existing decomposition and verification paradigms ignore their interactions and potential misalignment.
Approach:	They propose a reinforcement learning framework that leverages verifier feedback to learn a policy for dynamically decomposing claims to verifier-preferred atomicity.
Outcome:	The proposed framework outperforms existing decomposition policies in verification confidence tests . it improves accuracy and confidence by 0.12 on average across varying verifiers, datasets, and atomcities of input claims.

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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao

Named Entity and Relation Extraction with Multi-Modal Retrieval (2022.findings-emnlp)

Challenge:	Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications.
Approach:	They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions.
Outcome:	The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions.

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Xinyu Wang, Jiong Cai, Yong Jiang, Pengjun Xie, Kewei Tu, Wei Lu

From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge (2025.emnlp-main)

Challenge:	Existing approaches to name entity recognition and relation extraction are knowledge-based and may not be highly relevant.
Approach:	They propose a multi-modal named entity recognition framework that leverages image information to improve the performance of NER and relation extraction.
Outcome:	The proposed framework can achieve state-of-the-art on four multi-modal named entity recognition datasets and one multi-module relation extraction dataset.

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Dawei Li, Bohan Jiang, Liangjie Huang, Alimohammad Beigi, Chengshuai Zhao, Zhen Tan, Amrita Bhattacharjee, Yuxuan Jiang, Canyu Chen, Tianhao Wu, Kai Shu, Lu Cheng, Huan Liu

Challenge:	Recent advances in Large Language Models (LLMs) inspire the "LLM-as-a-judge" paradigm . traditional methods of assessment and evaluation fail in dynamic and open-ended scenarios .
Approach:	They propose a paradigm where LLMs are leveraged to perform scoring, ranking, or selection for machine learning evaluation scenarios.
Outcome:	The proposed model-based judgment and evaluation paradigms are based on large language models and are compared to the current model-driven evaluation paradigm.

The Linguistic Connectivities Within Large Language Models (2025.findings-acl)

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Dan Wang, Boxi Cao, Ning Bian, Xuanang Chen, Yaojie Lu, Hongyu Lin, Jia Zheng, Le Sun, Shanshan Jiang, Bin Dong, Xianpei Han

JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models (2024.naacl-long)

Challenge:	Recent studies have discovered notable disparities in their performance across different languages.
Approach:	They conduct a systematic investigation into the behaviors of large language models across 27 different languages on 3 different scenarios and reveals a Linguistic Map correlates with the richness of available resources and linguistic family relations.
Outcome:	The proposed model demonstrates that there are significant disparities in performance across languages across 27 different languages on 3 different scenarios.

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Jillian Fisher, Ximing Lu, Jaehun Jung, Liwei Jiang, Zaid Harchaoui, Yejin Choi

READoc: A Unified Benchmark for Realistic Document Structured Extraction (2025.findings-acl)

Challenge:	Existing methods to protect the identity and privacy of online authorship are lacking supervision data for diverse authorship and domains.
Approach:	They propose an unsupervised inference-time approach to authorship obfuscation that uses a user-controlled, inference time algorithm to oblige the authorship.
Outcome:	The proposed method outperforms state-of-the-art methods while performing competitively against a propriety model two orders of magnitudes larger.

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Zichao Li, Aizier Abulaiti, Yaojie Lu, Xuanang Chen, Jia Zheng, Hongyu Lin, Xianpei Han, Shanshan Jiang, Bin Dong, Le Sun

YuLan-Mini: Pushing the Limits of Open Data-efficient Language Model (2025.acl-long)

Challenge:	Document Structured Extraction (DSE) is a field of document structure analysis that aims to extract structured content from raw documents.
Approach:	They propose a benchmark to evaluate document structured extraction systems by converting unstructured PDFs into semantically rich Markdown.
Outcome:	The proposed benchmark is based on 3,576 diverse and real-world documents from arXiv, GitHub, and Zenodo.

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Hu Yiwen, Huatong Song, Jie Chen, Jia Deng, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Zican Dong, Yang Lu, Xu Miao, Xin Zhao, Ji-Rong Wen

Unexpected Phenomenon: LLMs’ Spurious Associations in Information Extraction (2024.findings-acl)

Challenge:	prevailing pre-training approaches for large language models involve several complexities.
Approach:	They propose a low-cost training recipe and a robust optimization approach to mitigate training instability . they also propose synthesis, curriculum, and data selection pipelines to integrate data .
Outcome:	The proposed model achieves top-tier performance among models with similar parameter scale . it is comparable to industry-leading models that require significantly more data .

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Weiyan Zhang, Wanpeng Lu, Jiacheng Wang, Yating Wang, Lihan Chen, Haiyun Jiang, Jingping Liu, Tong Ruan

Benchmarking Language Model Creativity: A Case Study on Code Generation (2025.naacl-long)

Challenge:	Information extraction (IE) tasks require a limited number of example instructions to achieve effective performance.
Approach:	They propose two strategies to find spurious associations in large language models (LLMs) they use forward label extension and backward label validation to leverage extended labels to improve model performance.
Outcome:	The proposed methods improve performance on Chinese and English datasets and 9.55%, 11.42%, and 21.27% in F1 scores on SciERC, ACE05, and DuEE datasets.

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Yining Lu, Dixuan Wang, Tianjian Li, Dongwei Jiang, Sanjeev Khudanpur, Meng Jiang, Daniel Khashabi

AdvAug: Robust Adversarial Augmentation for Neural Machine Translation (2020.acl-main)

Challenge:	Recent studies on LLM creativity evaluation focus on open-ended generation tasks . however, the degree to which LLMs possess and utilize creativity for problem-solving remains unclear .
Approach:	They propose a framework for quantifying LLM creativity that incorporates design ingredients . they introduce DENIAL PROMPTING which pushes LLMs to develop more creative solutions .
Outcome:	The proposed framework quantifies creativity in LLMs on Codeforces problems . it also finds that even the most creative model fails to demonstrate human-like creativity .

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Yong Cheng, Lu Jiang, Wolfgang Macherey, Jacob Eisenstein

Robustness in NLP

Challenge:	Recent work in neural machine translation has led to dramatic improvements in both research and commercial systems.
Approach:	They propose a adversarial augmentation method for Neural Machine Translation that minimizes vicinal risk over virtual sentences . they use a novel vicinity distribution for adversarials to describe a smooth interpolated embedding space .
Outcome:	The proposed method outperforms the current method on Chinese-English, English-French, and English-German translation benchmarks.

RecMind: Large Language Model Powered Agent For Recommendation (2024.findings-naacl)

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Yancheng Wang, Ziyan Jiang, Zheng Chen, Fan Yang, Yingxue Zhou, Eunah Cho, Xing Fan, Yanbin Lu, Xiaojiang Huang, Yingzhen Yang

MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)

Challenge:	Existing recommendations systems are limited in generalizing to new tasks due to model scale and data size constraints.
Approach:	They propose an LLM-powered autonomous recommender agent, RecMind, which is capable of leveraging external knowledge to provide zero-shot personalized recommendations.
Outcome:	The proposed model outperforms existing zero/few-shot LLM-based recommendation baseline methods in various tasks and achieves comparable performance to a fully trained recommendation model P5.

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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie

Simple or Complex? Complexity-controllable Question Generation with Soft Templates and Deep Mixture of Experts Model (2021.findings-emnlp)

Challenge:	Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models.
Approach:	They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts.
Outcome:	The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings .

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Sheng Bi, Xiya Cheng, Yuan-Fang Li, Lizhen Qu, Shirong Shen, Guilin Qi, Lu Pan, Yinlin Jiang

SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization (2023.emnlp-main)

Challenge:	Existing work on complex questions does not consider controlling complexity of generated questions.
Approach:	They propose an end-to-end neural complexity-controllable question generation model that incorporates a mixture of experts as the selector of soft templates to capture question similarity while avoiding the expensive construction of actual templates.
Outcome:	The proposed model is superior to state-of-the-art methods in both automatic and manual evaluations on two benchmark QA datasets.

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Hyunwoo Kim, Jack Hessel, Liwei Jiang, Peter West, Ximing Lu, Youngjae Yu, Pei Zhou, Ronan Bras, Malihe Alikhani, Gunhee Kim, Maarten Sap, Yejin Choi

Natural Language Interfaces

Challenge:	a dataset of 1.5 million conversations distilled from everyday spoken situations is limited in scale due to its associated costs.
Approach:	They propose to make SODA a publicly available, million-scale high-quality social dialogue dataset . they contextualize social commonsense knowledge from a knowledge graph to distill broad spectrum of social interactions .
Outcome:	The proposed dataset is the first publicly available, million-scale high-quality social dialogue dataset.

Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation (2021.findings-emnlp)

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An Yan, Zexue He, Xing Lu, Jiang Du, Eric Chang, Amilcare Gentili, Julian McAuley, Chun-Nan Hsu

ProsocialDialog: A Prosocial Backbone for Conversational Agents (2022.emnlp-main)

Challenge:	Radiology report generation aims at generating descriptive text from radiology images automatically.
Approach:	They propose a weakly supervised contrastive loss method that generates descriptive text from radiology images automatically.
Outcome:	The proposed method outperforms previous work on correctness and text generation metrics for two public benchmarks.

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Hyunwoo Kim, Youngjae Yu, Liwei Jiang, Ximing Lu, Daniel Khashabi, Gunhee Kim, Yejin Choi, Maarten Sap

Are All Prompt Components Value-Neutral? Understanding the Heterogeneous Adversarial Robustness of Dissected Prompt in LLMs (2026.eacl-long)

Challenge:	Existing dialogue systems fail to respond properly to potentially unsafe user utterances . existing systems either ignore or passively agree with unsafe content .
Approach:	They introduce a dataset to teach conversational agents to respond to problematic content following social norms.
Outcome:	The proposed dataset shows that ProsocialDialog generates more socially acceptable dialogues than existing models.

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Yujia Zheng, Tianhao Li, Haotian Huang, Tianyu Zeng, Jingyu Lu, Chuangxin Chu, Yuekai Huang, Ziyou Jiang, Qian Xiong, Yuyao Ge, Mingyang Li

LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking (2021.acl-long)

Challenge:	Existing studies treat prompts as flat text, overlooking their internal structure, and different components within a prompt contribute unequally to robustness.
Approach:	They propose a framework that decomposes prompts into functional components and a method that selectively modifies components to expose component-wise vulnerabilities.
Outcome:	The proposed framework exposes component-wise vulnerabilities while ensuring linguistic plausibility through perplexity-based filtering.

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Hang Jiang, Sairam Gurajada, Qiuhao Lu, Sumit Neelam, Lucian Popa, Prithviraj Sen, Yunyao Li, Alexander Gray

Versatile Framework for Song Generation with Prompt-based Control (2025.findings-emnlp)

Challenge:	Existing work deals with EL in the context of longer text, such as a sentence.
Approach:	They propose a neuro-symbolic approach that uses interpretable rules based on first-order logic to achieve better performance with black-box neural approaches.
Outcome:	The proposed approach achieves better performance than heuristics-based approaches on short-text EL . it can easily blend existing rule templates with multiple types of features, and even with scores resulting from previous EL methods.

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Yu Zhang, Wenxiang Guo, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, Jingyu Lu, Rongjie Huang, Ruiyuan Zhang, Zhiqing Hong, Ziyue Jiang, Zhou Zhao

BrainLoc: Brain Signal-Based Object Detection with Multi-modal Alignment (2025.findings-emnlp)

Challenge:	Existing methods for song generation fail to generate vocals with prompt-based control and proper alignment.
Approach:	VersBand is a multi-task song generation framework for synthesizing high-quality songs with prompt-based control.
Outcome:	Experimental results show that VersBand performs better than baseline models across multiple song generation tasks.

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Jiaqi Duan, Xiaoda Yang, Kaixuan Luan, Hongshun Qiu, Weicai Yan, Xueyi Zhang, Youliang Zhang, Zhaoyang Li, Donglin Huang, JunYu Lu, Ziyue Jiang, Xifeng Yang

TernaryBERT: Distillation-aware Ultra-low Bit BERT (2020.emnlp-main)

Challenge:	BrainLoc is a lightweight object detection model guided by fMRI signals.
Approach:	They propose a brain-based object detection model guided by fMRI signals . they employ a multi-modal alignment strategy that enhances fmr feature extraction .
Outcome:	The proposed model improves fMRI-based object detection accuracy and convenience.

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Wei Zhang, Lu Hou, Yichun Yin, Lifeng Shang, Xiao Chen, Xin Jiang, Qun Liu

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

Challenge:	Transformer-based pre-training models like BERT are computationally expensive and limited to resource-constrained devices.
Approach:	They propose a method which ternarizes the weights in a fine-tuned BERT model.
Outcome:	The proposed method outperforms the other methods on the GLUE and SQUAD benchmarks while being 14.9x smaller.

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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

Multilinguality

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.

RATIONALYST: Pre-training Process-Supervision for Improving Reasoning (2025.acl-long)

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Dongwei Jiang, Guoxuan Wang, Yining Lu, Andrew Wang, Jingyu Zhang, Chuyu Liu, Benjamin Van Durme, Daniel Khashabi

Controlled Low-Rank Adaptation with Subspace Regularization for Continued Training on Large Language Models (2025.acl-long)

Challenge:	RATIONALYST is a model for process-supervision of reasoning based on pretraining on rationale annotations extracted from unlabeled data.
Approach:	They propose a model for process-supervision of reasoning based on pre-training on rationale annotations extracted from unlabeled data.
Outcome:	RATIONALYST improves reasoning accuracy by 3.9% on representative reasoning benchmarks.

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Yuheng Lu, Bingshuo Qian, Caixia Yuan, Huixing Jiang, Xiaojie Wang

Lightweight Contenders: Navigating Semi-Supervised Text Mining through Peer Collaboration and Self Transcendence (2025.findings-naacl)

Challenge:	Existing approaches to mitigate catastrophic forgetting can be broadly categorized into data-based, architecture-based and learning-based methods.
Approach:	They propose a subspace regularization method on LoRA structure that imposes constraints on direction of updating matrix’s null space.
Outcome:	The proposed method reduces scale of output change while introducing minimal constraint on model capacity.

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Qianren Mao, Weifeng Jiang, Junnan Liu, Chenghua Lin, Qian Li, Xianqing Wen, Jianxin Li, Jinhu Lu

MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations (2021.emnlp-main)

Challenge:	Existing frameworks for semi-supervised text mining with lightweight models are limited by label data scarcity.
Approach:	They propose a framework for semi-supervised text mining with lightweight models . it incorporates online distillation to train lightweight student models by imitating the Teacher model .
Outcome:	The proposed framework exhibits notable performance enhancements over existing frameworks.

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Xinyin Ma, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Weiming Lu

Representation Learning

Challenge:	Recent advances in entity retrieval ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions.
Approach:	They propose a novel approach that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method.
Outcome:	The proposed approach achieves state-of-the-art performance on ZESHEL and improves quality of candidates on three standard Entity Linking datasets.

MemTR: Enhancing Tool-Calling Reliability via Uncertainty-Triggered FFN-Space Retracing (2026.findings-acl)

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Hongtao Duan, Lu Jiang, Minying Zhang, Xiaobing Zhu, Tianpeng Bu, Hao Jiang, Xinyu Wei, Lulu hu

Enabling Multimodal Generation on CLIP via Vision-Language Knowledge Distillation (2022.findings-acl)

Challenge:	Existing tool-calling methods rely on costly tool-use training data or only constrain syntax, leaving tool selection and argument value errors largely unsolved.
Approach:	They propose a method that decodes tool evidence from the tool library and mixes it into the output at the uncertain layer.
Outcome:	The proposed method reduces tool calling failures by 2%–9% with only 1%–2% runtime overhead.

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Wenliang Dai, Lu Hou, Lifeng Shang, Xin Jiang, Qun Liu, Pascale Fung

Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark (2025.coling-main)

Challenge:	Recent large-scale vision-language pre-training models are powerful in multimodal classification and retrieval tasks.
Approach:	They propose to augment a vision-language pre-training model with a textual pre-trained language model . the model achieves 44.5% zero-shot accuracy on multimodal generation tasks .
Outcome:	The proposed model achieves 44.5% zero-shot accuracy on open-ended visual question answering and image captioning tasks.

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Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang, Boli Chen, Yong Jiang, Haitao Zheng, Wenlian Lu, Pengjun Xie, Fei Huang

Natural Language Interfaces

Challenge:	Recent work has noted that due to the extremely high cost of iterative updates of LLMs, they are often unable to answer dynamic questions well.
Approach:	They propose a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest dynamic questions on the Chinese Internet.
Outcome:	The proposed benchmark will be one of the key data resources for improving LLMs’ Chinese question-answering ability in the future.

Beyond A Single AI Cluster: A Survey of Decentralized LLM Training (2025.emnlp-main)

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Haotian Dong, Jingyan Jiang, Rongwei Lu, Jiajun Luo, Jiajun Song, Bowen Li, Ying Shen, Zhi Wang

Text Clustering

Challenge:	Decentralized LLM training leverages dispersed resources at varying scales.
Approach:	They propose a resource-driven paradigm that leverages dispersed resources across clusters, datacenters and even regions.
Outcome:	The proposed model scales are 175 billion to 660 billion parameters, and the exponential growth in computational requirements poses significant challenges.

Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)

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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang

Speech & Audio in NLP

Challenge:	Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level.
Approach:	They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context.
Outcome:	The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set.

A Role-Selected Sharing Network for Joint Machine-Human Chatting Handoff and Service Satisfaction Analysis (2021.emnlp-main)

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Jiawei Liu, Kaisong Song, Yangyang Kang, Guoxiu He, Zhuoren Jiang, Changlong Sun, Wei Lu, Xiaozhong Liu

TabDSR: Decompose, Sanitize, and Reason for Complex Numerical Reasoning in Tabular Data (2025.findings-emnlp)

Challenge:	Recent efforts to predict chatbot failure hatches vital apprehensions due to complexity of human conversation.
Approach:	They propose a model that integrates dialogue satisfaction estimation and handoff prediction in one multi-task learning framework.
Outcome:	The proposed model integrates dialogue satisfaction estimation and handoff prediction in one multi-task learning framework.

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Changjiang Jiang, Fengchang Yu, Haihua Chen, Wei Lu, Jin Zeng

Reasoning

Challenge:	Large language models often underperform due to complex queries, noisy data, and limited numerical capabilities.
Approach:	They propose a framework that integrates seamlessly with mainstream LLMs to improve tabular reasoning.
Outcome:	The proposed framework outperforms existing methods in state-of-the-art analysis.

Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases (2020.findings-emnlp)

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Chuxu Zhang, Lu Yu, Mandana Saebi, Meng Jiang, Nitesh Chawla

Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References (2024.naacl-long)

Challenge:	Existing methods for multi-hop relation reasoning require limited data for each query relation, resulting in limited interpretation.
Approach:	They propose a few-shot multi-hop relation learning model that uses reinforcement learning to model sequential steps of multi-hopping reasoning and performs heterogeneous structure encoding and knowledge-aware search space pruning.
Outcome:	Empirical results show that the proposed model outperforms state-of-the-art models over few-shot relations.

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Tianyi Tang, Hongyuan Lu, Yuchen Jiang, Haoyang Huang, Dongdong Zhang, Xin Zhao, Tom Kocmi, Furu Wei

Evaluating Large Language Models on Wikipedia-Style Survey Generation (2024.findings-acl)

Challenge:	Existing evaluation benchmarks with limited references may not accurately reflect the quality of the model’s hypotheses.
Approach:	They propose a method to enrich evaluation benchmarks by diversifying the expression of a single reference into multiple high-quality ones to cover the semantic space of the reference sentence as much as possible.
Outcome:	The proposed method can enhance evaluation benchmarks by diversifying the expression of reference into multiple high-quality ones to cover the semantic space of the reference sentence as much as possible.

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Fan Gao, Hang Jiang, Rui Yang, Qingcheng Zeng, Jinghui Lu, Moritz Blum, Tianwei She, Yuang Jiang, Irene Li

Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding (2023.emnlp-industry)

Challenge:	Recent studies have shown that large language models can perform well in general tasks, but their effectiveness and limitations in domainspecific tasks remain unclear.
Approach:	They examine the proficiency of Large Language Models (LLMs) in generating succinct survey articles specific to the niche field of NLP in computer science.
Outcome:	The LLMs perform better in generating succinct survey articles specific to the niche field of NLP in computer science, compared to human-authored surveys, but they exhibit bias in evaluation.

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Zheng Chen, Ziyan Jiang, Fan Yang, Eunah Cho, Xing Fan, Xiaojiang Huang, Yanbin Lu, Aram Galstyan

Robustness in NLP

Challenge:	Defective queries impact the robustness of conversational AI systems such as Alexa, Siri or Google Assistant.
Approach:	They propose a Personalized Query Rewriting system that takes into account individual preferences or unique error patterns identified from a user's historical interactions with the conversational AI.
Outcome:	The proposed approach has been proven on a large-scale real-world dataset and online A/B experiments.

AdaptFlow: Adaptive Workflow Optimization via Meta-Learning (2025.findings-emnlp)

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Runchuan Zhu, Bowen Jiang, Lingrui Mei, Fangkai Yang, Lu Wang, Haoxiang Gao, Fengshuo Bai, Pu Zhao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios (2024.findings-acl)

Challenge:	Existing approaches to large language models rely on static templates or manual workflows.
Approach:	AdaptFlow is a language-based meta-learning framework inspired by model-agnostic meta- learning.
Outcome:	AdaptFlow outperforms manual and automated workflows on question answering, code generation and mathematical reasoning benchmarks.

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Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, Qun Liu

Gazetteer-Enhanced Attentive Neural Networks for Named Entity Recognition (D19-1)

Challenge:	Existing benchmarks focus on simple synthesized queries that do not reflect real-world complexity, thereby offering limited perspectives in evaluating tool utilization.
Approach:	They propose a benchmark to evaluate LLMs’ ability in tool utilization within real-world scenarios.
Outcome:	The proposed benchmark improves LLMs’ ability in tool utilization within real-world scenarios and eliminates the restriction of pre-defined toolset.

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Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun, Bin Dong, Shanshan Jiang

Large Language Models for Data Annotation and Synthesis: A Survey (2024.emnlp-main)

Challenge:	Named entity recognition (NER) is a fundamental NLP task.
Approach:	They propose a gazetteer-based attentive neural network which can enhance region-based NER . they first model the mention-context association and then an auxiliary gazetteers .
Outcome:	The proposed approach can achieve state-of-the-art on ACE2005 named entity recognition benchmark.

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Zhen Tan, Dawei Li, Song Wang, Alimohammad Beigi, Bohan Jiang, Amrita Bhattacharjee, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu

Robust Neural Machine Translation with Doubly Adversarial Inputs (P19-1)

Challenge:	Existing surveys focus on LLMs' specific utility for data annotation and synthesis.
Approach:	They propose to use large language models to generate annotations from raw data . they also propose to review learning strategies for models utilizing LLM-generated annotations .
Outcome:	The proposed models can be used to improve the efficacy of machine learning models by generating and labeling raw data with relevant information.

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Yong Cheng, Lu Jiang, Wolfgang Macherey

Robustness in NLP

Challenge:	Neural machine translation (NMT) models suffer from noisy perturbations in the input . a gradient-based method to craft adversarial examples informed by the translation loss is proposed .
Approach:	They propose an approach to improve the robustness of NMT models by attacking the translation model with adversarial source examples and defending the model with a target input.
Outcome:	The proposed approach improves translation performance and robustness on clean inputs and higher on noisy data.

Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks (2026.acl-long)

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Wenqi Zhang, Mengna Wang, Gangao Liu, Huixin Xu, Yiwei Jiang, Yongliang Shen, Guiyang Hou, Zhe Zheng, Hang Zhang, Xin Li, Jiajun Liu, Weiming Lu, Peng Li, Yueting Zhuang

Reasoning

Challenge:	Recent advances in reasoning models have demonstrated remarkable capabilities on mathematical and coding tasks, but their effectiveness in embodied domains remains largely unexplored.
Approach:	They propose a reasoning model for interactive embodied tasks that synthesizes 9.3k coherent Observation-Thought-Action trajectories containing 64k ego-centric images and 90k diverse reasoning processes.
Outcome:	The proposed model outperforms existing visual reasoning models by +9%, 24%, and +13% on long-horizon tasks.

ClarifyDelphi: Reinforced Clarification Questions with Defeasibility Rewards for Social and Moral Situations (2023.acl-long)

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Valentina Pyatkin, Jena D. Hwang, Vivek Srikumar, Ximing Lu, Liwei Jiang, Yejin Choi, Chandra Bhagavatula

Challenge:	Changing contexts can flip the moral judgment of an action.
Approach:	They propose an interactive system that learns to ask clarification questions to elicit salient contexts of a social or moral situation.
Outcome:	The proposed system generates more relevant, informative and defeasible questions compared to baselines.

AutoTaskEval: Towards Domain-Specific and Fine-Grained Evaluation for LLMs (2026.acl-long)

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Qingqing Lyu, Linjuan Wu, Yongliang Shen, Hengwei Liu, Hao Li, Shengpei Jiang, Yin Zhang, Weiming Lu

Challenge:	Existing automated approaches operate within fixed task schemas and often fail to autonomously discover new evaluation dimensions.
Approach:	They propose an automated framework that constructs domain-specific benchmarks directly from unstructured corpora using Bloom’s Taxonomy.
Outcome:	The proposed framework uncovers a broader and more fine-grained task space than expert-curated benchmarks while producing high-quality instances that preserve established model-level evaluation trends.

ASR-EC Benchmark: Evaluating Large Language Models on Chinese ASR Error Correction (2025.emnlp-industry)

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Victor Junqiu Wei, Weicheng Wang, Di Jiang, Yuanfeng Song, Lu Wang

ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring (2026.acl-industry)

Challenge:	Automatic Speech Recognition (ASR) systems have a substantial number of erroneous recognition due to environmental noise, ambiguity, etc.
Approach:	They use a benchmark dataset to analyze ASR errors in the Chinese language . they then apply large language models to correct ASR error correction .
Outcome:	The proposed method is based on a dataset of ASR errors in the Chinese language . it shows prompting is not effective for ASR error correction .

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Deyi Ji, Junyu Lu, Xuanyi Liu, Liqun Liu, Hailong Zhang, Peng Shu, Huan Yu, Jie Jiang, Tianrun Chen, Lanyun Zhu

Symbolic Knowledge Distillation: from General Language Models to Commonsense Models (2022.naacl-main)

Challenge:	Existing regulatory policies create label inconsistencies and reasoning ambiguities in historical datasets.
Approach:	They propose a policy-adaptive governance system that enables evolving reinforcement through multi-agent adversarial umpiring.
Outcome:	The proposed system outperforms fine-tuning baselines on industrial and public datasets . it enables evolving reinforcement through multi-agent adversarial umpiring .

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Peter West, Chandra Bhagavatula, Jack Hessel, Jena Hwang, Liwei Jiang, Ronan Le Bras, Ximing Lu, Sean Welleck, Yejin Choi

Towards Robust k-Nearest-Neighbor Machine Translation (2022.emnlp-main)

Challenge:	Prior studies suggested pre-trained language models possess limited understanding of commonsense knowledge despite otherwise stellar performance on leaderboards.
Approach:	They propose a framework that uses larger models to teach smaller models by distilling knowledge symbolically as text in addition to the neural model.
Outcome:	The proposed framework is based on a general language model teacher's commonsense knowledge graphs and a neural commonsensing model surpassing the teacher model's in all three criteria.

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Hui Jiang, Ziyao Lu, Fandong Meng, Chulun Zhou, Jie Zhou, Degen Huang, Jinsong Su

Leveraging Contextual Information for Effective Entity Salience Detection (2024.findings-naacl)

Challenge:	k-Nearest-Neighbor Machine Translation (kNN-MT) is a popular research paradigm in machine translation.
Approach:	They propose a confidence-enhanced kNN-MT model with robust training to reduce noise . they introduce NMT confidence to refine the modeling of important components of kN-MT .
Outcome:	The proposed model improves on four benchmark datasets and is robust to training.

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Rajarshi Bhowmik, Marco Ponza, Atharva Tendle, Anant Gupta, Rebecca Jiang, Xingyu Lu, Qian Zhao, Daniel Preotiuc-Pietro

Task-Oriented Clustering for Dialogues (2021.findings-emnlp)

Challenge:	Prior work on salient entity detection focused on machine learning models that require heavy feature engineering.
Approach:	They propose to fine-tune medium-sized language models with a cross-encoder style architecture to achieve significant performance gains over feature engineering approaches.
Outcome:	The proposed model fine-tunes medium-sized pre-trained language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches.

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Chenxu Lv, Hengtong Lu, Shuyu Lei, Huixing Jiang, Wei Wu, Caixia Yuan, Xiaojie Wang

RAISE: Reinforced Adaptive Instruction Selection For Large Language Models (2025.findings-emnlp)

Challenge:	Existing methods for task-oriented dialogue clustering are difficult to apply directly due to inherent differences between them.
Approach:	They propose a Dialogue Task Clustering Network model for task-oriented clustering . they use context-aware utterance representations and cross-dialogue utterrance cluster representations .
Outcome:	The proposed model outperforms baselines on three public datasets on all metrics.

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Qingsong Lv, Yangning Li, Zihua Lan, Zishan Xu, Jiwei Tang, Tingwei Lu, Yinghui Li, Wenhao Jiang, Hong-Gee Kim, Hai-Tao Zheng, Philip S. Yu

ReviewGrounder: Improving Review Substantiveness with Rubric-Guided, Tool-Integrated Agents (2026.acl-long)

Challenge:	Existing selection methods rely on static, heuristic quality scores and are executed only once before training.
Approach:	They propose a dynamic selection framework that integrates selection into every training step.
Outcome:	The proposed framework integrates selection into every training step.

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Zhuofeng Li, Yi Lu, Dongfu Jiang, Haoxiang Zhang, Yuyang Bai, Chuan Li, Yu Wang, Shuiwang Ji, Jianwen Xie, Yu Zhang

Improved Sparse Upcycling for Instruction Tuning (2025.coling-main)

Challenge:	Rapid rise in AI conference submissions has driven increasing exploration of large language models (LLMs) for peer review support.
Approach:	They propose a peer review benchmarking tool based on paper-specific rubrics and a rubric-guided framework that decomposes reviewing into drafting and grounding stages.
Outcome:	The proposed framework outperforms baselines with stronger/larger backbones in both alignment with human judgments and rubric-based review quality across 8 dimensions.

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Wangyi Jiang, Yaojie Lu, Hongyu Lin, Xianpei Han, Le Sun

Structured Optimal Brain Pruning for Large Language Models (2024.emnlp-main)

Challenge:	Existing methods for sparse upcycling lead to performance degradation in instruction tuning scenarios.
Approach:	They propose a representation-based approach to convert dense language models into sparsely activated ones by initializing router weights from language models.
Outcome:	The proposed architecture improves model capabilities and routing consistency across multiple benchmarks.

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Jiateng Wei, Quan Lu, Ning Jiang, Siqi Li, Jingyang Xiang, Jun Chen, Yong Liu

NeuroLogic A*esque Decoding: Constrained Text Generation with Lookahead Heuristics (2022.naacl-main)

Challenge:	Existing pruning methods for Large Language Models rely on unstructured pruning or require special hardware to accelerate computation.
Approach:	They propose a retraining-free structured pruning method called SoBP . they evaluate the effectiveness of SoBP across 14 models from 3 LLM families .
Outcome:	The proposed method outperforms current state-of-the-art pruning methods on 8 datasets.

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Ximing Lu, Sean Welleck, Peter West, Liwei Jiang, Jungo Kasai, Daniel Khashabi, Ronan Le Bras, Lianhui Qin, Youngjae Yu, Rowan Zellers, Noah A. Smith, Yejin Choi