Papers by Yu Yuan

CRITICTOOL: Evaluating Self-Critique Capabilities of Large Language Models in Tool-Calling Error Scenarios (2025.emnlp-main)

Challenge:	Existing methods encode the triples of entities as embeddings and learn to align the embeddables, which prevents the direct interaction between the original information of the cross-KG entities.
Approach:	They propose to transform the triples into unified textual sequences and model the EA task as a bi-directional textual entailment task between the sequences of cross-KG entities.
Outcome:	The proposed approach outperforms the state-of-the-art methods on five cross-lingual datasets and allows the mutual enhancement of the heterogeneous information.

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Shiting Huang, Zhen Fang, Zehui Chen, Siyu Yuan, Junjie Ye, Yu Zeng, Lin Chen, Qi Mao, Feng Zhao

CLaMP 3: Universal Music Information Retrieval Across Unaligned Modalities and Unseen Languages (2025.findings-acl)

Challenge:	a number of tools are used to perform complex tasks, but the tool utilization process can cause errors.
Approach:	They propose a critique evaluation benchmark for tool learning that analyzes function-calling errors on tool evaluation benchmarks.
Outcome:	The proposed critique evaluation benchmark holds diverse tool-use errors with varying complexities, which better reflects real-world scenarios.

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Shangda Wu, Guo Zhancheng, Ruibin Yuan, Junyan Jiang, SeungHeon Doh, Gus Xia, Juhan Nam, Xiaobing Li, Feng Yu, Maosong Sun

Spec-VLA: Speculative Decoding for Vision-Language-Action Models with Relaxed Acceptance (2025.emnlp-main)

Challenge:	Music information retrieval (MIR) is a field that aims at developing computational tools for processing, organizing, and accessing music data.
Approach:	They propose a framework that aligns music modalities with multilingual text in a shared representation space.
Outcome:	Experiments show CLaMP 3 performs state-of-the-art on multiple MIR tasks . it surpasses baselines and shows excellent generalization in multimodal and multilingual contexts .

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Songsheng Wang, Rucheng Yu, Zhihang Yuan, Chao Yu, Feng Gao, Yu Wang, Derek F. Wong

PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit (2022.naacl-demo)

Challenge:	Visual Language Models (VLMs) have significant parameter size and autoregressive (AR) decoding nature impose considerable computational demands on VLA models.
Approach:	They propose a framework to relax acceptance utilizing the relative distances represented by the action tokens of the VLA model.
Outcome:	Empirical results show that the proposed framework improves the speed of the prediction task by 44%.

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Hui Zhang, Tian Yuan, Junkun Chen, Xintong Li, Renjie Zheng, Yuxin Huang, Xiaojie Chen, Enlei Gong, Zeyu Chen, Xiaoguang Hu, Dianhai Yu, Yanjun Ma, Liang Huang

Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis (2026.acl-long)

Challenge:	PaddleSpeech is an open-source speech toolkit that supports speech-to-text and text-to speech tasks.
Approach:	They describe the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-to speech tasks.
Outcome:	The proposed framework achieves competitive or state-of-the-art performance on various speech datasets and implements the most popular methods.

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Zehua Pei, Hui-Ling Zhen, Lancheng Zou, Xianzhi Yu, Wulong Liu, Sinno Jialin Pan, Mingxuan Yuan, Bei Yu

SocAoG: Incremental Graph Parsing for Social Relation Inference in Dialogues (2021.acl-long)

Challenge:	Large language models (LLMs) are fast but require expensive pre-training . a new approach to scale large language models into MoEs reduces inference costs .
Approach:	They propose an analytical post-training framework that rapidly restructures FFNs into sparse MoE architectures using only a small calibration dataset.
Outcome:	The proposed framework outperforms existing methods on a small calibration dataset.

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Liang Qiu, Yuan Liang, Yizhou Zhao, Pan Lu, Baolin Peng, Zhou Yu, Ying Nian Wu, Song-Chun Zhu

Structured Data in NLP

Challenge:	Existing studies focus on identifying entities' relations from the semantics of dialogues-they utilize either the attention mechanism or a refined token graph to locate informative words.
Approach:	They propose a sequential structure prediction task to incrementally parse SocAoG for dynamic inference upon any incoming utterance.
Outcome:	Empirical results show that the proposed model infers social relations more accurately than the state-of-the-art methods.

Evaluating Text Generation Quality Using Spectral Distances of Surprisal (2025.findings-emnlp)

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Zhichen Liu, Yongyuan Li, Yang Xu, Yu Wang, Yingfang Yuan, Zuhao Yang

Text Generation

Challenge:	Existing metric fails to capture text surprisal, but FACE-2 produces stronger agreement with human preferences.
Approach:	They propose a new automatic evaluation metric for open-ended text generation . they propose metric that extracts the dynamic patterns (spectrum) of text surprisal .
Outcome:	The proposed metric outperforms existing methods in revealing the model scaling effect . it produces stronger agreement with human preferences from a large human-annotated dataset .

Know-MRI: A Knowledge Mechanisms Revealer&Interpreter for Large Language Models (2025.acl-demo)

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Jiaxiang Liu, Boxuan Xing, Chenhao Yuan, ChenxiangZhang ChenxiangZhang, Di Wu, Xiusheng Huang, Haida Yu, Chuhan Lang, Pengfei Cao, Jun Zhao, Kang Liu

Explainability & Interpretability in NLP

Challenge:	Existing interpretation methods only support tasks with specific inputs, limiting their practical applications.
Approach:	They propose an extensible module that matches different input data with interpretation methods and consolidates the interpreting outputs.
Outcome:	The proposed module can match different input data with interpretation methods and consolidate the interpreting outputs.

Conditional Supervised Contrastive Learning for Fair Text Classification (2022.findings-emnlp)

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Jianfeng Chi, William Shand, Yaodong Yu, Kai-Wei Chang, Han Zhao, Yuan Tian

Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents (2025.findings-emnlp)

Challenge:	Recent advances in natural language processing have demonstrated societal bias in existing NLP models.
Approach:	They propose to use contrastive learning to learn fair representations for text classification . they conduct experiments on two text datasets to demonstrate their methods are stable .
Outcome:	The proposed methods balancing task performance and bias mitigation are stable in different hyperparameter settings.

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Long Li, Weiwen Xu, Jiayan Guo, Ruochen Zhao, Xingxuan Li, Yuqian Yuan, Boqiang Zhang, Yuming Jiang, Yifei Xin, Ronghao Dang, Yu Rong, Deli Zhao, Tian Feng, Lidong Bing

Challenge:	Existing methods for idea generation either trivially prompt LLMs or expose LLM to extensive literature without indicating useful information.
Approach:	They propose a chain-of-ideas agent that organizes literature in a chains structure . they propose evaluating idea-generation methods from different perspectives .
Outcome:	The proposed agent outperforms existing methods and matches human quality in idea generation.

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026.findings-acl)

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Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, Ngai Wong

Explainability & Interpretability in NLP

Challenge:	Existing literature on mechanistic interpretation (MI) treats it as an observational science, leaving practical applications underexplored.
Approach:	They propose a survey structured around the pipeline to identify and improve MI models.
Outcome:	The proposed framework enables tangible improvements in Alignment, Capability, and Efficiency.

Investigating the Influence of Bilingual MWU on Trainee Translation Quality (L18-1)

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Yu Yuan, Serge Sharoff

MCIL: Multimodal Counterfactual Instance Learning for Low-resource Entity-based Multimodal Information Extraction (2024.lrec-main)

Challenge:	a method for automatic extraction of bilingual multiword units (BMWUs) from a parallel corpus has been shown to be useful for estimating human translation quality.
Approach:	They applied a method for automatic extraction of bilingual multiword units from a parallel corpus in order to investigate their contribution to translation quality in terms of adequacy and fluency.
Outcome:	The method is based on generalized additive modelling and it shows that normalized BMWU ratios can be useful for estimating human translation quality.

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Baohang Zhou, Ying Zhang, Kehui Song, Hongru Wang, Yu Zhao, Xuhui Sui, Xiaojie Yuan

Can Large Language Models Understand Context? (2024.findings-eacl)

Challenge:	Existing methods to perform multimodal information extraction only investigated entity-based tasks under supervised learning with adequate labeled data.
Approach:	They propose to investigate the entity-based MIE tasks under the low-resource settings by decomposing the features into image, entity, and context factors.
Outcome:	The proposed method is able to perform on two public MIE benchmark datasets and the experimental results confirm it.

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Yilun Zhu, Joel Moniz, Shruti Bhargava, Jiarui Lu, Dhivya Piraviperumal, Site Li, Yuan Zhang, Hong Yu, Bo-Hsiang Tseng

SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs (2026.acl-long)

Challenge:	Existing evaluation methodologies for Large Language Models (LLMs) have been inadequate to evaluate their ability to understand contextual features.
Approach:	They propose a benchmark to assess large language models' ability to understand context by adapting existing datasets to suit their evaluation.
Outcome:	The proposed model performs better under the in-context learning pretraining scenario than state-of-the-art models.

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Sihang Zhao, Kangrui Yu, Youliang Yuan, Pinjia He, Hongyi Wen

Structured Data in NLP

Challenge:	Existing educational LLMs are vulnerable to pedagogical jailbreaks where students use answer-inducing prompts to elicit solutions rather than scaffolded instructions.
Approach:	They propose a graph-augmented tutoring pipeline that infers prerequisite concepts from queries and identifies mastery gaps.
Outcome:	The proposed method improves safety under two pedagogical jailbreak scenarios while maintaining near-ceiling helpfulness under the same evaluation protocol.

i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)

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Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang

Low-Hallucination and Efficient Coreference Resolution with LLMs (2025.findings-emnlp)

Challenge:	i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data.
Approach:	They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data.
Outcome:	i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks.

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Yujian Gan, Yuan Liang, Jinxia Xie, Yanni Lin, Juntao Yu, Massimo Poesio

ℛ3: Advertisement Compliance ℛectification via Group-ℛelative Experience Extractor and Curriculum ℛeinforcement (2026.acl-industry)

Challenge:	Large Language Models have shown promising results in coreference resolution, but they face a critical issue: hallucinations.
Approach:	They propose a low-hallucination and efficient solution to the problem of hallucinations . they propose efficient constrained decoding for coreference resolution .
Outcome:	The proposed approach achieves better performance on the English OntoNotes development set.

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Yuan Chen, Zhenyu Hu, Mengge Xue, Cao Te, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

M-SENA: An Integrated Platform for Multimodal Sentiment Analysis (2022.acl-demo)

Challenge:	Existing methods of content moderation are infeasible due to over-editing and compromise the advertiser’s original semantic intent.
Approach:	They propose a framework to harmonize compliance with original intent preservation that integrates a data-driven framework and a curriculum to enforce compliance while maximizing semantic consistency.
Outcome:	The proposed framework outperforms state-of-the-art baselines on industrial datasets and on online A/B testing on industrial video.

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Huisheng Mao, Ziqi Yuan, Hua Xu, Wenmeng Yu, Yihe Liu, Kai Gao

Two Streams, One Sarcasm: Orthogonal Expert Tuning for Holistic Multimodal Sarcasm Understanding (2026.acl-long)

Challenge:	M-SENA is an open-source platform for multimodal sentiment analysis.
Approach:	They propose to use a platform for multimodal sentiment analysis to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations.
Outcome:	The proposed framework provides reliable benchmarks and baseline results of different modality features and MSA benchmarks.

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Diandian Guo, Cong Cao, Fangfang Yuan, Pin Xu, Cheng Hu, Zhicheng Zhang, Yu Liu, Yanbing Liu

VocabTailor: Dynamic Vocabulary Selection for Downstream Tasks in Small Language Models (2026.findings-acl)

Challenge:	Existing benchmarks for multimodal satirical cognition hinder evaluation of multimodal Sarcasm Understanding . lack of a unified benchmark for holistic satire cognition hampers evaluation of MSU .
Approach:	They propose a framework to decouple experts into orthogonal shared perception and private execution streams to physically block gradient interference between tasks.
Outcome:	The proposed framework achieves superior performance on DocMSU-PLUS.

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Hanling Zhang, Yayu Zhou, Tongcheng Fang, Zhihang Yuan, Guohao Dai, Wanli Ouyang, Yu Wang

KidsArtBench: Multi-Dimensional Children’s Art Evaluation with Attribute-Aware MLLMs (2026.eacl-long)

Challenge:	Existing static vocabulary pruning designs that reduce memory usage suffer from rigid, one-size-fits-all designs that cause information loss during the prefill stage and lack flexibility.
Approach:	They propose a decoupled dynamic vocabulary selection framework that addresses memory constraints through offloading embedding and implements a hybrid static-dynamic vocabulary selection strategy for LM Head.
Outcome:	The proposed framework reduces memory usage by 99% with minimal or no degradation in performance.

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Mingrui Ye, Chanjin Zheng, Zengyi Yu, Chenyu Xiang, Zhixue Zhao, Zheng Yuan, Helen Yannakoudakis

Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning (2022.naacl-main)

Challenge:	Multimodal Large Language Models (MLLMs) show impressive capabilities across visual–language tasks, but their capacity to evaluate artistic expression remains limited.
Approach:	They propose an attribute-specific multi-LoRA approach where each attribute corresponds to a distinct evaluation dimension in the scoring rubric.
Outcome:	The proposed approach increases correlation from 0.468 to 0.653 on Qwen2.5-VL-7B, with the largest gains on perceptual dimensions and narrowed gaps on higher-order attributes.

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Hongyi Yuan, Zheng Yuan, Sheng Yu

TaoType: Predicting Fine-Grained Typing Intent for Faster Search (2026.acl-industry)

Challenge:	Generative methods for biomedical entity linking (EL) use synonyms knowledge from knowledge bases (KB) this is not trivial to inject into a generative method, but it is cost-effective.
Approach:	They propose to inject synonyms knowledge into a generative model of biomedical EL by constructing synthetic samples with synonyms and definitions from KB and requiring the model to recover concept names.
Outcome:	The proposed method achieves state-of-the-art results on several biomedical EL tasks without candidate selection.

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Yipeng Yu, Yichen Yuan, Chengxiao Feng, Xu Liu

MPTc-Bench: Measuring Cross-market Generative Ability of Vision-Language Models via Movie Poster Transcreation (2026.findings-acl)

Challenge:	Large-scale industrial ranking systems operate under stringent real-time performance requirements.
Approach:	They propose a client-side framework that determines whether a user’s query is complete at each typing . this method leverages client-based typing behavior for real-time early prediction .
Outcome:	The proposed framework achieves offline precision/recall/accuracy of 0.7936/0.8196/0.7742 and decreases online response time by 640.5193.65 milliseconds.

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Youyuan Lin, Yuan Li, Yahan Yu, Fei Cheng, Shin’ya Nishida, Chenhui Chu

Licon: A Diverse, Controllable and Challenging Linguistic Concept Learning Benchmark (2023.findings-emnlp)

Challenge:	Recent work adapts textual transcreation to image editing and formulates image transcreations to better match a target audience while preserving meaning.
Approach:	They propose a two-stage planner-editor pipeline in which an VLM planner specifies executable edits and an image editor renders them.
Outcome:	The proposed model can transcreate a visual asset for a different market while preserving its identity while matching market-specific design preferences and multilingual typography.

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Shenglong Yu, Ying Zhang, Wenya Guo, Zhengkun Zhang, Ru Zhou, Xiaojie Yuan

Visual Data in NLP

Challenge:	Existing methods for Concept Learning focus on visual information, but visual information cannot present abstract concepts exactly, which struggles the introduction of novel concepts related to known concepts.
Approach:	They propose a benchmark where concepts in diverse forms are defined by linguistic descriptions and an entailment-based concept learning method to model the relationship among concepts.
Outcome:	The proposed benchmark is based on the existing visual concepts learning benchmarks and will be released to the public soon.

Beyond Single View: A Comprehensive Benchmark for Medical Multimodal Large Language Models on Multi-Image Understanding (2026.acl-long)

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Dexuan Xu, Yuan Jiayin, Jianing Wang, Yanyuan Chen, Hanpin Wang, Yu Huang

Following Length Constraints in Instructions (2025.emnlp-main)

Challenge:	Existing benchmarks for multimodal large language models are limited to multiview diagnostics.
Approach:	They propose a benchmark specifically designed for medical multi-image understanding that evaluates MLLMs across four dimensions.
Outcome:	The proposed model performs better in multi-image contexts than open-source models . the model perform better when processing increased visual loads than closed-source ones .

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Weizhe Yuan, Ilia Kulikov, Ping Yu, Kyunghyun Cho, Sainbayar Sukhbaatar, Jason E Weston, Jing Xu

Challenge:	Existing instruction following models fail to follow length constraints in their evaluations.
Approach:	They propose to train models that can be controlled at inference time with instructions containing desired length constraints.
Outcome:	The proposed models outperform standard instruction following models in length instructed evaluations.

RBPtool: A Deep Language Model Framework for Multi-Resolution RBP-RNA Binding Prediction and RNA Molecule Design (2025.emnlp-main)

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Jiyue Jiang, Yitao Xu, Zikang Wang, Yihan Ye, Yanruisheng Shao, Yuheng Shan, Jiuming Wang, Xiaodan Fan, Jiao Yuan, Yu Li

Mulan: A Multi-Level Alignment Model for Video Question Answering (2023.findings-emnlp)

Challenge:	RNA-binding proteins play key roles in post-transcriptional gene regulation . existing methods focus on shallow sequence features or coarse structural representations . large language models allow for precise modeling and biologically informed de novo RNA design .
Approach:	They extend RPI15223 into a multi-resolution, structure-level RBP-RNA dataset and introduce RBPtool, a framework that fuses sequence and structural information.
Outcome:	The proposed framework achieves state-of-the-art performance on public benchmarks and the RPI15223 dataset while supporting fine-grained level predictions.

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Yu Fu, Cong Cao, Yuling Yang, Yuhai Lu, Fangfang Yuan, Dakui Wang, Yanbing Liu

Visual Data in NLP

Challenge:	Existing methods focus on visual-language alignment at the video level, but they do not account for fine-grained semantic interaction between video and text.
Approach:	They propose a multi-level Alignment Model for Video Question Answering that establishes alignment between visual and textual modalities at the object-level, frame-level and video-level.
Outcome:	The proposed model outperforms state-of-the-art methods even with a small amount of extra visual-language pre-training data and a reduced number of trainable parameters.

DREAM: Improving Video-Text Retrieval Through Relevance-Based Augmentation Using Large Foundation Models (2025.naacl-long)

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Yimu Wang, Shuai Yuan, Bo Xue, Xiangru Jian, Wei Pang, Mushi Wang, Ning Yu

LLaMAX: Scaling Linguistic Horizons of LLM by Enhancing Translation Capabilities Beyond 100 Languages (2024.findings-emnlp)

Challenge:	Recent advances in video-text retrieval models have limited training data annotations.
Approach:	They propose a Video-Text Retrieval Paradigm with Relevance-based Augmentation which enhances video and text data using large foundation models to learn more generalized features.
Outcome:	The proposed method improves video-text retrieval performance over existing methods.

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Yinquan Lu, Wenhao Zhu, Lei Li, Yu Qiao, Fei Yuan

Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)

Challenge:	Large Language Models (LLMs) exhibit exceptional translation capabilities in high-resource language tasks, yet their effectiveness in low-resourced languages is suboptimal.
Approach:	They conduct extensive multilingual continual pre-training on the LLaMA series models and develop LLiMAX for translation support across more than 100 languages.
Outcome:	The proposed model achieves higher translation performance than existing open-source models and performs on-par with specialized translation model on the Flores-101 benchmark.

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Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang

Bypassing Neural Evaluations for Fast Audio Editing via Adaptive Trajectory Extrapolation (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability.
Approach:	They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality.
Outcome:	The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks.

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Xiaoqian Liu, Zhengkun Ge, Jianjin Wang, Haoran Zhang, Yuan Ge, Kaiyan Chang, Chen Xu, Tong Xiao, Zhengtao Yu, Linfeng Zhang, JingBo Zhu

FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (D18-1)

Challenge:	Recent advances in audio diffusion models have significantly improved text-to-audio editing via inversion techniques, but these models typically rely on dense, fixed-step sampling trajectories to maintain structural integrity.
Approach:	They propose a model-agnostic Adaptive Trajectory Extrapolation framework that accelerates inversion-based editing process by dynamically evaluating only the most critical generative phases.
Outcome:	The proposed framework achieves a 3.9 speedup with negligible loss in fidelity.

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Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, Maosong Sun

Low-Resource NLP

Challenge:	Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans.
Approach:	They propose a Few-Shot Relation Classification Dataset consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers.
Outcome:	The proposed methods perform well on the most competitive few-shot learning models, especially as compared with humans.

On the Emotion Understanding of Synthesized Speech (2026.acl-long)

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Yuan Ge, Haishu Zhao, AoKai Hao, Junxiang Zhang, Bei Li, Xiaoqian Liu, Chenglong Wang, Jianjin Wang, Bingsen Zhou, Bingyu Liu, JingBo Zhu, Zhengtao Yu, Tong Xiao

CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models (2025.findings-naacl)

Challenge:	Existing models for emotion understanding do not capture fundamental features of synthesized speech.
Approach:	They evaluate emotion recognition models on synthesized speech using SER models and generative models.
Outcome:	The proposed model can't generalize to synthesized speech because of speech token prediction . generative models tend to infer emotion from textual semantics while ignoring paralinguistic cues.

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Shangda Wu, Yashan Wang, Ruibin Yuan, Guo Zhancheng, Xu Tan, Ge Zhang, Monan Zhou, Jing Chen, Xuefeng Mu, Yuejie Gao, Yuanliang Dong, Jiafeng Liu, Xiaobing Li, Feng Yu, Maosong Sun

Bring Invariant to Variant: A Contrastive Prompt-based Framework for Temporal Knowledge Graph Forecasting (2024.lrec-main)

Challenge:	Current music information retrieval systems struggle to meet linguistic diversity challenges . current systems struggle with text queries in non-English languages .
Approach:	They propose a music information retrieval system that supports both ABC notation and MIDI . CLaMP 2 includes a multilingual text encoder and a multiple-modal music encoder .
Outcome:	The proposed system achieves state-of-the-art results in multilingual semantic search and music classification across modalities.

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Ying Zhang, Xinying Qian, Yu Zhao, Baohang Zhou, Kehui Song, Xiaojie Yuan

AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis (2023.findings-acl)

Challenge:	Existing methods for temporal knowledge graph forecasting are insufficient structural contexts to learn effective representations.
Approach:	They propose a Contrastive Prompt-based framework with Entity background information for TKG forecasting that brings time-invariant entity background information to time-variant structural information.
Outcome:	The proposed framework is effective and stays competitive in inference with limited structural information.

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Ru Zhou, Wenya Guo, Xumeng Liu, Shenglong Yu, Ying Zhang, Xiaojie Yuan

Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation (2023.findings-acl)

Challenge:	Existing methods to extract aspects from text-image pairs and recognize their sentiments are noisy and coarsely establishing image-aspect alignment will interfere with aspect-relevant semantic and sentiment information.
Approach:	They propose an Aspect-oriented method to detect aspect-relevant semantic and sentiment information by selecting textual tokens and image blocks that are semantically related to the aspects.
Outcome:	The proposed method is superior to existing methods in the field of sentiment analysis.

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Fei Yuan, Yinquan Lu, Wenhao Zhu, Lingpeng Kong, Lei Li, Yu Qiao, Jingjing Xu

Rethinking Text-based Protein Understanding: Retrieval or LLM? (2025.emnlp-main)

Challenge:	Existing monolithic models for multilingual neural machine translation encounter parameter interference and inefficient inference for large models.
Approach:	They propose a detachable multi-way model that assigns each language to an individual branch . they use data from OPUS to build a translation benchmark covering 433 languages .
Outcome:	The proposed model outperforms existing models in OPUS and is faster than existing models.

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Juntong Wu, Zijing Liu, He Cao, Li Hao, Bin Feng, Zishan Shu, Ke Yu, Li Yuan, Yu Li

Structured Attention for Unsupervised Dialogue Structure Induction (2020.emnlp-main)

Challenge:	Recent studies have focused on integrating protein-related knowledge into large language models through continued pretraining and multi-modal alignment.
Approach:	They propose a retrieval-enhanced method which significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios.
Outcome:	The proposed method significantly outperforms fine-tuned LLMs for protein-to-text generation and shows accuracy and efficiency in training-free scenarios.

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Liang Qiu, Yizhou Zhao, Weiyan Shi, Yuan Liang, Feng Shi, Tao Yuan, Zhou Yu, Song-Chun Zhu

V-MAGE: A Game Evaluation Framework for Assessing Vision-Centric Capabilities in Multimodal Large Language Models (2026.findings-acl)

Challenge:	Using structured attention, a model can learn dialogue structure in unsupervised fashion.
Approach:	They propose to incorporate structured attention layers into a Variational Recurrent Neural Network model with discrete latent states to learn dialogue structure in an unsupervised fashion.
Outcome:	The proposed model learns semantic structures similar to templates used to generate a dialogue corpus on two-party datasets and on multi-party dialogues, disentangling dialogues without human annotation.

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Xiangxi Zheng, Linjie Li, Zhengyuan Yang, Ping Yu, Alex Jinpeng Wang, Rui Yan, Yuan Yao, Lijuan Wang

Look before You Leap: Dual Logical Verification for Knowledge-based Visual Question Generation (2024.lrec-main)

Challenge:	Existing static image-text benchmarks are insufficient for evaluating multimodal large language models’ dynamic perception and interactive reasoning abilities.
Approach:	They propose a game-based evaluation framework to assess multimodal large language models’ visual reasoning in dynamic, continuous-space environments.
Outcome:	The proposed framework systematically assesses MLLMs’ visual reasoning in dynamic, continuous-space environments.

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Xumeng Liu, Wenya Guo, Ying Zhang, Xubo Liu, Yu Zhao, Shenglong Yu, Xiaojie Yuan

Breaking the Generator Barrier: Disentangled Representation for Generalizable AI-Text Detection (2026.acl-long)

Challenge:	Existing methods for visual question generation focus on leveraging the semantics of inputs to propose questions, ignoring the logical coherence between generated questions and images.
Approach:	They propose a logical verification method that checks logical structure between Q, images, answers and acquired outside knowledge by incorporating logical coherence between Q and Q twice in the whole procedure.
Outcome:	The proposed method can generate diverse and insightful knowledge-based visual questions on two common datasets.

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Xiao Pu, Zepeng Cheng, Lin Yuan, Yu Wu, Xiuli Bi

Representation Learning

Challenge:	a rapid proliferation of large language models (LLMs) generate text that increasingly resembles human writing . this makes it difficult to capture subtle cues that distinguish AI-generated content from human-written content .
Approach:	They propose a framework that disentangles AI-detection semantics from generator-aware artifacts by latent encoding and perturbation-based regularization.
Outcome:	The proposed framework disentangles AI-detection semantics from generator-aware artifacts on 20 representative LLMs across 7 categories.

ErAConD: Error Annotated Conversational Dialog Dataset for Grammatical Error Correction (2022.naacl-main)

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Xun Yuan, Derek Pham, Sam Davidson, Zhou Yu

Natural Language Interfaces

Challenge:	Currently available grammatical error correction datasets focus on written essays . a novel dataset is presented to improve the accuracy of existing educational chatbots .
Approach:	They propose a novel grammatical error correction dataset using essays and other long-form text written by language learners.
Outcome:	The proposed dataset improves the performance of a conversational chatbot in a human-machine conversational setting.

MoE-I2: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition (2024.findings-emnlp)

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Cheng Yang, Yang Sui, Jinqi Xiao, Lingyi Huang, Yu Gong, Yuanlin Duan, Wenqi Jia, Miao Yin, Yu Cheng, Bo Yuan

Understanding the Behaviors of Environment-aware Information Retrieval (2026.acl-long)

Challenge:	emergence of Mixture of Experts (MoE) LLMs has significantly advanced the development of language models.
Approach:	They propose a two-stage compression method tailored for Mixture of Experts to reduce the model size and decrease the computational cost.
Outcome:	The proposed method reduces model size and improves inference efficiency while maintaining performance in various zero-shot tasks.

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Ruifeng Yuan, Chaohao Yuan, David Dai, Yu Rong, Hong Cheng, Hou Pong Chan, Chenghao Xiao

Graph Attention Network with Memory Fusion for Aspect-level Sentiment Analysis (2020.aacl-main)

Challenge:	Recent retrieval-augmented generation approaches have demonstrated strong capability in handling complex queries.
Approach:	They propose a branching-based rollout technique that improves training stability . they find different retrievers exhibit distinct optimal query styles .
Outcome:	The proposed method improves training stability and improves retrieval-aware systems.

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Li Yuan, Jin Wang, Liang-Chih Yu, Xuejie Zhang

Knowledge-Infused Multi-Bit Watermarking for RAG Knowledge Bases (2026.findings-acl)

Challenge:	Recent studies ignored the syntactic relationship between the aspect and its corresponding context words, leading the model to focus on syntaktically unrelated words mistakenly.
Approach:	They propose to extend the graph convolutional network by assigning different weights to edges of connected words.
Outcome:	The proposed method can improve on five datasets showing that it learns and exploits multiword relations and draws different weights of words to improve performance.

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Ke Yang, Shuguang Yuan, Jing Yu, Chi Chen

Beyond Self-Report: Bridging the Intention-Behavior Gap in Critical Thinking Assessment via Interpretable Multi-Agent System (2026.acl-long)

Challenge:	Existing RAG watermarking methods are limited in their encoding capacity and potential degradation of performance or knowledge quality.
Approach:	They propose knowledge-infused and multi-bit watermarking (KMW) for RAG knowledge bases by benign knowledge completion and a tailored generative watermark algorithm.
Outcome:	The proposed method extracts watermarks from adversarial RAGs while remaining stealthy and secure.

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Zekun Li, Jifan Yu, Haoxuan Li, Ye He, Daniel Zhang-Li, Shangqing Tu, Joy Jia Yin Lim, Yikun Jiang, Jiaxin Yuan, Yu Zhang

TriPlay-RL: Tri-Role Self-Play Reinforcement Learning for LLM Safety Alignment (2026.acl-long)

Challenge:	Accurate assessment of critical thinking is limited by the Intention Behavior Gap in psychology . evaluators that measure self-reported competence are limited by multiagent architectures .
Approach:	They propose a framework that operationalizes cognitive assessment into an interpretable multi-agent workflow with Assessment Chain-of-Thought.
Outcome:	The proposed framework aligns better with human expert ratings than gold-standard inventories on large-scale simulations and human participants.

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Zhewen Tan, Wenhan Yu, Jianfeng Si, Tongxin Liu, Kaiqi Guan, Huiyan Jin, Jiawen Tao, Xiaokun Yuan, Xiangzheng Zhang, Duohe Ma, Tong Yang, Lin Sun

Robustness in NLP

Challenge:	Existing approaches to safety alignment of large language models rely on costly manual annotations or human review.
Approach:	They propose a closed-loop reinforcement learning framework called TriPlay-RL that enables iterative collaboration among three roles with near-zero manual annotation.
Outcome:	The proposed framework achieves 20%–50% improvement in adversarial effectiveness while preserving high output diversity while achieving 10%–30% gains in safety performance without degrading general reasoning capability.

Multi-View Incongruity Learning for Multimodal Sarcasm Detection (2025.coling-main)

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Diandian Guo, Cong Cao, Fangfang Yuan, Yanbing Liu, Guangjie Zeng, Xiaoyan Yu, Hao Peng, Philip S. Yu

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

Challenge:	Existing methods for multimodal sarcasm detection rely on spurious correlations, demonstrating poor generalizability beyond training environments.
Approach:	They propose a method that integrates multimodal incongruities via contrastive learning for multimodal sarcasm detection by using three views to drive multi-view learning.
Outcome:	The proposed method outperforms existing methods on benchmark datasets and shows that it is more generalizable than existing methods.

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

MoleculeQA: A Dataset to Evaluate Factual Accuracy in Molecular Comprehension (2024.findings-emnlp)

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

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Xingyu Lu, He Cao, Zijing Liu, Shengyuan Bai, Leqing Chen, Yuan Yao, Hai-Tao Zheng, Yu Li

Question Answering

Challenge:	Existing models generate erroneous information and evaluations fail to assess factual correctness of models.
Approach:	They propose to use MoleculeQA to evaluate molecular factual correctness in large language models by organizing molecules into a taxonomy and building QA pairs through human and LLM efforts.
Outcome:	The proposed model improves the factual correctness of generated information and enables the development of new models.

Auto-Stega: An Agent-Driven System for Lifelong Strategy Evolution in LLM-Based Text Steganography (2026.findings-acl)

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Jiuan Zhou, Yu Cheng, Yuan Xie, Zhaoxia Yin

Representation Learning

Challenge:	prevailing methods rely on hand-crafted or pre-specified strategies and struggle to balance efficiency, imperceptibility, and security, particularly at high embedding rates.
Approach:	They propose an agent-driven self-evolving framework that is the first to realize self-changing steganographic strategies by automatically discovering, composing, and adapting strategies at inference time.
Outcome:	The proposed framework achieves 42.2% perplexity and 1.6% anti-steganalysis performance over SOTA methods at high embedding rates.

Evaluating Evaluation Metrics – The Mirage of Hallucination Detection (2025.findings-emnlp)

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Atharva Kulkarni, Yuan Zhang, Joel Ruben Antony Moniz, Xiou Ge, Bo-Hsiang Tseng, Dhivya Piraviperumal, Swabha Swayamdipta, Hong Yu

You Can Have a Second Chance: Unbiased and Multi-bit Watermarking for Diffusion Language Models with Regret-based Remasking (2026.acl-long)

Challenge:	a large-scale empirical evaluation of hallucination detection metrics is conducted . hallucinosity is a significant obstacle to the reliability and widespread adoption of language models .
Approach:	They conduct large-scale empirical evaluation of hallucination detection metrics . they compare hallucinian language models, language models and decoding methods .
Outcome:	The results show that the evaluations of hallucination detection metrics fail to align with human judgments, they say . they also show that evaluations with LLM-based evaluation yield the best overall results .

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Ke Yang, Dongyang Liang, Jing Yu, Shuguang Yuan, Chi Chen

Symbol-LLM: Towards Foundational Symbol-centric Interface For Large Language Models (2024.acl-long)

Challenge:	Existing sequential LLMs cannot be directly applied to DLMs, as their generation order is arbitrary.
Approach:	They propose a stability-aware constraint that allows watermarking only in stable contexts and a bit-controlled, unbiased modulation to preserve the original DLM output distribution.
Outcome:	The proposed scheme achieves stable watermarking with minimal quality impact while maintaining high detection accuracy and multi-bit capacity.

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Fangzhi Xu, Zhiyong Wu, Qiushi Sun, Siyu Ren, Fei Yuan, Shuai Yuan, Qika Lin, Yu Qiao, Jun Liu

Sentence Level Human Translation Quality Estimation with Attention-based Neural Networks (2020.lrec-1)

Challenge:	Large Language Models (LLMs) have limitations when it comes to comprehending and expressing world knowledge that extends beyond the boundaries of natural language.
Approach:	They propose a model that integrates symbolic data into LLM training without loss of generality ability.
Outcome:	The proposed model performs better on symbol- and NL-centric tasks.

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Yu Yuan, Serge Sharoff

Machine Translation

Challenge:	Existing methods for assessing translation quality rely on manual features and external knowledge.
Approach:	They propose to use a neural model without feature engineering to detect which parts in sentence pairs are most relevant for assessing quality.
Outcome:	The proposed model outperforms feature-based methods on a large human annotated dataset.

MELOV: Multimodal Entity Linking with Optimized Visual Features in Latent Space (2024.findings-acl)

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Xuhui Sui, Ying Zhang, Yu Zhao, Kehui Song, Baohang Zhou, Xiaojie Yuan

Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models (2024.emnlp-main)

Challenge:	Existing approaches to multimodal entity linking focus on textual contexts but lack in social media vision modality.
Approach:	They propose a latent space vision feature optimization framework MELOV to address these challenges . they exploit variational autoencoder to mine shared information and generate text-based visual features .
Outcome:	The proposed framework is superior to existing methods on three benchmark datasets.

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Yu Yuan, Lili Zhao, Kai Zhang, Guangting Zheng, Qi Liu

On the Vulnerability of Text Sanitization (2025.naacl-long)

Challenge:	Large Language Models (LLMs) have shown remarkable capabilities in various tasks, but may rely on dataset biases as shortcuts for prediction.
Approach:	They propose to use a test suite to evaluate the impact of shortcuts on LLMs' performance.
Outcome:	The proposed test suite incorporates six shortcut types, five evaluation metrics, and four prompting strategies.

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Meng Tong, Kejiang Chen, Xiaojian Yuan, Jiayang Liu, Weiming Zhang, Nenghai Yu, Jie Zhang

Predicting Rewards Alongside Tokens: Non-disruptive Parameter Insertion for Efficient Inference Intervention in Large Language Model (2024.emnlp-main)

Challenge:	Existing reconstruction attacks on text sanitization are not able to accurately assess their effectiveness.
Approach:	They propose to use ASR to measure the effectiveness of reconstruction attacks to evaluate sanitization performance.
Outcome:	The proposed reconstruction attacks achieve a 46.4% improvement in ASR over the state-of-the-art baseline with a privacy budget of =4.0 on the SST-2 dataset.

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Chenhan Yuan, Fei Huang, Ru Peng, Keming Lu, Bowen Yu, Chang Zhou, Jingren Zhou

Overcoming Language Priors in Visual Question Answering via Distinguishing Superficially Similar Instances (2022.coling-1)

Challenge:	Existing approaches to fine tune LLMs produce unsafe responses and unreliable reasoning, but this solution introduces substantial time and space overhead due to the separate models required.
Approach:	They propose to insert extra parameters into transformer architecture to predict calibration signals along with original LLM output.
Outcome:	The proposed model reduces time and space costs while enabling seamless online deployment.

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Yike Wu, Yu Zhao, Shiwan Zhao, Ying Zhang, Xiaojie Yuan, Guoqing Zhao, Ning Jiang

Visual Data in NLP

Challenge:	Existing VQA models rely on the superficial correlation between question type and frequent answers to make predictions, without really understanding the input.
Approach:	They propose a training framework that explicitly encourages the VQA model to distinguish between superficially similar instances.
Outcome:	The proposed framework achieves state-of-the-art performance on VQA-CP v2 . it explicitly encourages the model to distinguish between the superficially similar instances .

ToHRE: A Top-Down Classification Strategy with Hierarchical Bag Representation for Distantly Supervised Relation Extraction (2020.coling-main)

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Erxin Yu, Wenjuan Han, Yuan Tian, Yi Chang

Information Extraction & Text Mining

Challenge:	Existing methods to find relational facts from texts lack hierarchical information of relations.
Approach:	They propose a hierarchical classification framework which extracts relation in a top-down manner.
Outcome:	The proposed method significantly outperforms state-of-the-art methods on NYT dataset . the proposed method generates large amounts of training data by aligning KBs with unlabeled corpora .

COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)

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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin

Language Models can Evaluate Themselves via Probability Discrepancy (2024.findings-acl)

Challenge:	Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation.
Approach:	They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets.
Outcome:	The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark.

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Tingyu Xia, Bowen Yu, Yuan Wu, Yi Chang, Chang Zhou

CENTAUR: Bridging the Impossible Trinity of Privacy, Efficiency, and Performance in Privacy-Preserving Transformer Inference (2025.acl-long)

Challenge:	Existing evaluation frameworks focus on superficial text differences and fail to align with human judgment.
Approach:	They propose a new method to evaluate the performance of Large Language Models (LLMs) by calculating probability discrepancies between original response generation and revised versions of LLMs.
Outcome:	The proposed method eliminates the need for training an additional evaluation model or relying on external proprietary models such as GPT-4 as a judger.

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Jinglong Luo, Guanzhong Chen, Yehong Zhang, Shiyu Liu, Hui Wang, Yue Yu, Xun Zhou, Yuan Qi, Zenglin Xu

Detecting AI-Generated Content on Social Media with Multi-modal Language Models (2026.acl-industry)

Challenge:	Existing privacy-preserving Transformer Inference frameworks suffer from high computational overhead and performance losses.
Approach:	They propose a framework that integrates random permutations and SMPC to address the "impossible trinity" CENTAUR resists diverse data reconstruction attacks and boosts inference speed by 5.030.4 times .
Outcome:	CENTAUR achieves an unprecedented balance between privacy, efficiency, and performance.

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Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu, Sumedha Singla, Brian Vanover, Huijun Qian, Zihao Wang, Fujun Liu, Aashu Singh, Jianyu Wang, Xuewen Zhang

Unsupervised Slot Schema Induction for Task-oriented Dialog (2022.naacl-main)

Challenge:	Existing methods for AI-generated content detection face poor generalization to newer models, reliance on single modalities, and lack of interpretable explanations.
Approach:	They propose a model that curates diverse social media data and trains a vision-language model for detection and explanation.
Outcome:	The proposed model achieves state-of-the-art detection performance on public benchmarks and observes positive downstream impacts on user engagement.

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Dian Yu, Mingqiu Wang, Yuan Cao, Izhak Shafran, Laurent Shafey, Hagen Soltau

Dunhuang-Bench: How Well Do MLLMs Understand Cultural Heritage? (2026.findings-acl)

Challenge:	Defining task-specific schemas is the first step of building a task-oriented dialog system.
Approach:	They propose an unsupervised approach for slot schema induction from unlabeled dialog corpora using in-domain language models and unsupervised parsing structures.
Outcome:	The proposed method shows significant performance improvement on multi-domain and SGD datasets.

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Junyi Yuan, Jian Zhang, Tianxiu Yu, Yanlin Zhou, Xiaobo Jin, Qiufeng Wang, Fangyu Wu

Knowledge-grounded Dialog State Tracking (2022.findings-emnlp)

Challenge:	Recent advances in Multimodal Large Language Models (MLLMs) have led to extensive evaluations on Chinese cultural benchmarks.
Approach:	They construct a large-scale benchmark comprising 486 images and 22,970 QA pairs to evaluate MLLMs' cultural understanding.
Outcome:	The proposed benchmark incorporates three task formats to evaluate MLLMs’ cultural understanding: Question Answering with Text Description, Multi-turn Dialogue, and Question Answers with Choices.

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Dian Yu, Mingqiu Wang, Yuan Cao, Laurent El Shafey, Izhak Shafran, Hagen Soltau

CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution (2026.acl-long)

Challenge:	Structured knowledge is encoded implicitly into model parameters for downstream tasks, making training inefficient.
Approach:	They propose to perform dialog state tracking grounded on knowledge encoded externally.
Outcome:	The proposed method outperforms baseline models in the few-shot learning setting.

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Xiangxi Zheng, Kuang He, Jiayi Hu, Ping Yu, Rui Yan, Yuan Yao, Peng Hou, Anxiang Zeng, Alex Jinpeng Wang

LM-Interview: An Easy-to-use Smart Interviewer System via Knowledge-guided Language Model Exploitation (2024.emnlp-demo)

Challenge:	Existing approaches to chart-to-code generation are constrained by data-centric limitations . authors present a new framework that redesigns both training and alignment data .
Approach:	They propose a data-centric framework that redesigns both training and alignment data for chart-to-code generation.
Outcome:	The proposed framework outperforms open-source baselines and is competitive with GPT-5.

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Hanming Li, Jifan Yu, Ruimiao Li, Zhanxin Hao, Yan Xuan, Jiaxi Yuan, Bin Xu, Juanzi Li, Zhiyuan Liu

CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks (2026.acl-long)

Challenge:	Semi-structured interviews are a crucial method of data acquisition in qualitative research.
Approach:	They propose a semi-structured interview system that automates interview preparation, analysis and control by interviewers.
Outcome:	Experimental results show that LM-Interview performs comparable to human interviewers . the system can be used to analyze semi-structured interviews without interviewers' involvement .

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Lingyue Fu, Hao Guan, Bolun Zhang, Haowei Yuan, Yaoming Zhu, Lin Qiu, ZongYu Wang, Xuezhi Cao, Xunliang Cai, Weiwen Liu, Weinan Zhang, Yong Yu

Challenge:	Existing large language models for software engineering rely on coarse-grained pass rates obscuring specific cognitive bottlenecks.
Approach:	They propose a repository-level benchmark that dissects coding capabilities through atomized tasks.
Outcome:	The proposed framework achieves a 78.55% validity yield, surpassing the 31.7% retention rate of SWE-bench-Verified.

POINTS-Reader: Distillation-Free Adaptation of Vision-Language Models for Document Conversion (2025.emnlp-main)

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Yuan Liu, Zhongyin Zhao, Le Tian, Haicheng Wang, Xubing Ye, Yangxiu You, Zilin Yu, Chuhan Wu, Zhou Xiao, Yang Yu, Jie Zhou

Conformal Event Prediction with Temporal Knowledge Graph (2026.findings-acl)

Challenge:	Existing approaches to training document conversion models with manual annotation are costly and time-consuming, and training student models by distilling outputs from teacher models can significantly limit their performance in real-world applications.
Approach:	They propose a fully automated framework for constructing high-quality document extraction datasets and models capable of handling diverse document formats and layouts.
Outcome:	The proposed model outperforms existing models and improves on annotated documents.

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Cheng Hu, Cong Cao, Fangfang Yuan, Diandian Guo, Pin Xu, Yu Liu, Yanbing Liu

Knowledge Representation

Challenge:	Current event prediction methods lack rigorous uncertainty quantification, which limits their reliability for decision-making.
Approach:	They propose a conformal prediction framework that applies conformal predictions to event prediction to address this challenge.
Outcome:	The proposed framework guarantees coverage while improving efficiency on three public datasets.

PRESTO: Progressive Pretraining Enhances Synthetic Chemistry Outcomes (2024.findings-emnlp)

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He Cao, Yanjun Shao, Zhiyuan Liu, Zijing Liu, Xiangru Tang, Yuan Yao, Yu Li

Efficient Test-Time Scaling via Temporal Reasoning Aggregation (2026.findings-acl)

Challenge:	Multimodal Large Language Models (MLLMs) have seen growing adoption across various scientific domains.
Approach:	They propose a framework that bridges the molecule-text modality gap by integrating a comprehensive benchmark of pretraining strategies and dataset configurations.
Outcome:	The proposed framework improves multimodal LLMs through cross-modal alignment and multi-graph understanding.

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Jiakun Li, Xingwei He, Kefan Li, Hongzheng Chai, Hongyue Yu, Yuan Yuan

Enrich, Aggregate, and Generate: Three-stage Biomedical Data-to-Text Generation Using Large Language Models in Low-resource Scenarios (2026.findings-acl)

Challenge:	Existing dynamic early-exit methods rely on single-step confidence signals . existing approaches are unreliable for detecting reasoning convergence in multi-step settings .
Approach:	They propose a training-free framework for efficient test-time scaling that determines when to terminate reasoning based on temporal aggregation of multi-step evidence rather than instantaneous signals.
Outcome:	Experiments show that TRACE reduces reasoning token usage by 25% on average while maintaining accuracy within 1–2% of full-length reasoning.

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Yupian Lin, Guangya Yu, Yuang Bian, Cheng Yuan, Hui Luo, Tong Ruan

Text Generation

Challenge:	Biomedical data-to-text generation is a branch of Natural Language Generation, aiming at generating textual natural language descriptions that can fluently and precisely describe the structured data.
Approach:	They propose an LLM framework that can be used to generate textual natural language descriptions using in-context learning.
Outcome:	The proposed framework provides good interpretability and superior performance on the BioLeaflets dataset.

MAXS: Meta-Adaptive Exploration with LLM Agents (2026.findings-acl)

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Jian Zhang, Zhiyuan Wang, Zhangqi Wang, Yu He, Haoran Luo, li Yuan, Lingling Zhang, Rui Mao, Qika Lin, Jun Liu

Cross-lingual Terminology Extraction for Translation Quality Estimation (L18-1)

Challenge:	Existing methods for inference are often myopic and have divergent reasoning paths . a meta-adaptive reasoning framework is proposed to improve the efficiency of LLM agents .
Approach:	They propose a meta-adaptive reasoning framework that integrates tool execution and reasoning planning.
Outcome:	The proposed framework outperforms existing methods in performance and inference efficiency.

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Yu Yuan, Yuze Gao, Yue Zhang, Serge Sharoff

Incorporating Object-Level Visual Context for Multimodal Fine-Grained Entity Typing (2023.findings-emnlp)

Challenge:	Using common statistical measures for termhood and unithood, we identify terms from monolingual texts and investigate the contribution of terminology to translation quality.
Approach:	They propose to use common statistical measures for termhood and unithood as features to train classifiers for identifying terms in cross-domain and cross-language settings.
Outcome:	The proposed method has shown some reliability in automatically identifying terms in human translations, but drawbacks in handling low frequency terms and term variations shall be dealt with in the future.

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Ying Zhang, Wenbo Fan, Kehui Song, Yu Zhao, Xuhui Sui, Xiaojie Yuan

Few-shot Intent Classification and Slot Filling with Retrieved Examples (2021.naacl-main)

Challenge:	Experimental results show that fine-grained entity typing is superior to text-based methods.
Approach:	They propose a task called fine-grained entity typing to classify entities . they propose combining textual and visual contexts to capture fine-granular semantic information .
Outcome:	The proposed approach achieves superior classification performance compared to previous text-based approaches.

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Dian Yu, Luheng He, Yuan Zhang, Xinya Du, Panupong Pasupat, Qi Li

SSR-A: Spatial- and Semantic-Aware Instructions and Curriculum Reinforcement for Advertisement Compliant Rectification (2026.acl-industry)

Challenge:	Existing methods for few-shot learning are based on labeled examples, but they are non-trivial . few-sshot learning is challenging due to the imbalance in the amount of data between the source and target domains.
Approach:	They propose retrieval-based methods for intent classification and slot filling tasks . they use a batch-softmax objective to learn similar contextualized representations for spans .
Outcome:	The proposed method outperforms previous systems on the CLINC and SNIPS benchmarks.

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Cao Te, Mengge Xue, Zhenyu Hu, Yuan Chen, Liqun Liu, Peng Shu, Huan Yu, Jie Jiang

Chatbot To Help Patients Understand Their Health (2025.findings-emnlp)

Challenge:	Existing methods to fix non-compliant images suffer from over-editing, destroying original intent and perceptual similarity.
Approach:	They propose a framework for the minimalist rectification of non-compliant image ads.
Outcome:	The proposed framework outperforms state-of-the-art baselines in both compliance and preservation of visual and commercial consistency.

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Won Seok Jang, Hieu Tran, Manav Shaileshkumar Mistry, Sai Kiran Gandluri, Yifan Zhang, Sharmin Sultana, Sunjae Kwon, Yuan Zhang, Zonghai Yao, Hong Yu

LlmFixer: Fix the Helpfulness of Defensive Large Language Models (2025.findings-emnlp)

Challenge:	NoteAid-Chatbot is a conversational AI designed to help patients better understand their health .
Approach:	They propose a new learning paradigm that leverages a multi-agent large language model and reinforcement learning framework without relying on costly human-generated training data.
Outcome:	The proposed framework surpasses non-expert human training methods.

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Zelong Yu, Xiaoming Zhang, Litian Zhang, Yu Yuan, Chaozhuo Li

QA-Driven Zero-shot Slot Filling with Weak Supervision Pretraining (2021.acl-short)

Challenge:	Several defense strategies have been introduced to defend against jailbreak attacks, but these strategies weakened the usefulness of large language models.
Approach:	They propose a framework that acts on large language models equipped with any defense strategy to recover their usefulness.
Outcome:	The proposed framework can be used on large language models to recover their usefulness without updating the parameters of a defensive large language model.

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Xinya Du, Luheng He, Qi Li, Dian Yu, Panupong Pasupat, Yuan Zhang

Learning Semantic Textual Similarity via Topic-informed Discrete Latent Variables (2022.emnlp-main)

Challenge:	Existing methods to predict slots and their values do not encode enough semantic information, limiting the models’ zero-shot capability.
Approach:	They propose a QA-driven slot filling model which extracts slot-filler spans from utterances with a span-based QA model.
Outcome:	The proposed model outperforms baselines by over 5% on the SNIPS benchmark.

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Erxin Yu, Lan Du, Yuan Jin, Zhepei Wei, Yi Chang

Dynamic Guided and Domain Applicable Safeguards for Enhanced Security in Large Language Models (2025.findings-naacl)

Challenge:	Recent discrete latent variable models have received a surge of interest in both NLP and CV . they are comparable to the continuous counterparts in representation learning, but are more interpretable in their predictions.
Approach:	They develop a topic-informed discrete latent variable model for semantic textual similarity . they inject the quantized representation into a transformer-based language model .
Outcome:	The proposed model outperforms strong baselines in semantic textual similarity tasks.

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Weidi Luo, He Cao, Zijing Liu, Yu Wang, Aidan Wong, Bin Feng, Yuan Yao, Yu Li

LogToP: Logic Tree-of-Program with Table Instruction-tuned LLMs for Controlled Logical Table-to-Text Generation (2026.findings-eacl)

Challenge:	Existing defense methods struggle with two key issues: inadequate defense capabilities and over-defensiveness.
Approach:	They propose a multi-agents-based framework that leverages accurate external information to provide an unbiased summary of user intentions and safety response guidance.
Outcome:	Experiments on popular jailbreak attacks and benign datasets show that the proposed framework can enhance LLM's robustness against jailbreaks without compromising its general functionality.

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Yupian Lin, Guangya Yu, Cheng Yuan, Huan Du, Hui Luo, Yuang Bian, Jingping Liu, Zhidong He, Wen Du, Tong Ruan

Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats (2026.acl-industry)

Challenge:	Existing LLMs are difficult to achieve satisfactory results in table-related tasks.
Approach:	They propose to develop a specialized logical table-to-text generation model that can be used for table-related tasks.
Outcome:	The proposed model achieves state-of-the-art on a Logic2Text dataset.

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Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin, Zhiyuan Yang, Yu Zi Wei, Xin Wang, Mingxuan Yuan, Xianzhi Yu, Zhenhua Dong

Green & Sustainable NLP

Challenge:	Low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency.
Approach:	They evaluate HiFloat (HiF8 and HiF4), a family of floating-point formats tailored for Ascend NPUs.
Outcome:	The proposed formats excel with high-variance data and are compatible with state-of-the-art quantization frameworks.

Towards Unified Representations of Knowledge Graph and Expert Rules for Machine Learning and Reasoning (2022.aacl-main)

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Zhepei Wei, Yue Wang, Jinnan Li, Zhining Liu, Erxin Yu, Yuan Tian, Xin Wang, Yi Chang

BERT-BC: A Unified Alignment and Interaction Model over Hierarchical BERT for Response Selection (2024.lrec-main)

Challenge:	Empirical study shows superiority of proposed method over time-tested knowledge-driven and data-driven methods.
Approach:	They propose a cognitive knowledge graph that unifies expert rules and relational facts as the substrate of machine learning and reasoning models.
Outcome:	Empirical results show the proposed method superior to time-tested methods . the proposed model can perform both learning and reasoning with labeled data .

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Zhenfei Yang, Beiming Yu, Yuan Cui, Shi Feng, Daling Wang, Yifei Zhang

LoopTool: Closing the Data–Training Loop for Robust LLM Tool Calls (2026.acl-long)

Challenge:	Recent performance boosting for dialogue response selection task achieved by Cross-Encoder based models is limited and the learned models have poor generalization capability in realistic scenarios.
Approach:	They propose a model that combines the representation-based Bi-Encoder and interaction-based Cross-Encoding to achieve better semantic representation.
Outcome:	The proposed model can achieve state-of-the-art performance on three benchmark datasets for multi-turn response selection.

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Kangning Zhang, Weiwen Liu, Wenxiang Jiao, Kounianhua Du, Yuan Lu, Weinan Zhang, Yong Yu

InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery (2025.coling-main)

Challenge:	Large Language Models (LLMs) are powerful tools for multi-step tasks, but static data pipelines hinder tool learning and cause noisy labels to persist.
Approach:	They propose a fully automated, model-aware data evolution framework that tightly integrates data synthesis and model training.
Outcome:	Experiments show that LoopTool-8B significantly surpasses its 32B data generator and achieves new state-of-the-art results on the BFCL-v3 and ACEBench benchmarks for its scale.

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He Cao, Zijing Liu, Xingyu Lu, Yuan Yao, Yu Li