Papers by Yi Liu

SILO-BENCH: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems (2026.acl-long)

Challenge:	Existing approaches to multimodal representation learning focus on directional alignment and embedding magnitudes (L2-norm) however, these methods often fail to account for the intrinsic role of L2-norm in the contrastive process.
Approach:	They propose a plug-and-play framework that optimizes L2-norm alignment and Directional consistency jointly.
Outcome:	The proposed framework achieves consistent and significant performance gains over established baselines across 95 tasks using UniIR and VLM2Vec-V2 frameworks.

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Yuzhe Zhang, Feiran Liu, Yi Shan, Xinyi Huang, Xin Yang, Yueqi Zhu, Xuxin Cheng, Cao Liu, Ke Zeng, Terry Jingchen Zhang, Wenyuan Jiang

Challenge:	Existing benchmarks conflate coordination ability with role-based priors.
Approach:	They propose a role-free benchmark for evaluating free-form collaboration under information silos.
Outcome:	The proposed benchmark systematically probes coordination capabilities under information silos using 54 configurations and 3 frontier LLMs.

MDSEval: A Meta-Evaluation Benchmark for Multimodal Dialogue Summarization (2025.findings-emnlp)

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Yinhong Liu, Jianfeng He, Hang Su, Ruixue Lian, Yi Nian, Jake W. Vincent, Srikanth Vishnubhotla, Robinson Piramuthu, Saab Mansour

Alignment-Enhanced Decoding: Defending Jailbreaks via Token-Level Adaptive Refining of Probability Distributions (2024.emnlp-main)

Challenge:	Multimodal Dialogue Summarization (MDS) is a critical task with wide-ranging applications.
Approach:	They propose a meta-evaluation benchmark for multimodal dialogue summarization based on image-sharing dialogues, corresponding summaries and human judgments .
Outcome:	The proposed framework is the first to identify and formalize key evaluation dimensions specific to MDS.

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Quan Liu, Zhenhong Zhou, Longzhu He, Yi Liu, Wei Zhang, Sen Su

ReCo: Reliable Causal Chain Reasoning via Structural Causal Recurrent Neural Networks (2022.emnlp-main)

Challenge:	Existing defenses against jailbreaks focus on perturbing or inspecting inputs, but ignore competing objectives, the underlying cause of alignment failures.
Approach:	They propose a novel defense that employs adaptive decoding to address the root causes of jailbreak issues.
Outcome:	The proposed defense improves safety alignment while maintaining helpfulness.

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Kai Xiong, Xiao Ding, Zhongyang Li, Li Du, Ting Liu, Bing Qin, Yi Zheng, Baoxing Huai

A Multi-sentiment-resource Enhanced Attention Network for Sentiment Classification (P18-2)

Challenge:	Causal chain reasoning models suffer from two main transitive problems: threshold effect and scene drift.
Approach:	They propose a framework that uses exogenous variables to represent causal pairs and estimates the threshold and scene contradictions using structural causal recurrent neural networks.
Outcome:	The proposed framework outperforms baselines on Chinese and English CCR datasets.

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Zeyang Lei, Yujiu Yang, Min Yang, Yi Liu

Sentiment Analysis

Challenge:	Existing sentiment classification approaches do not fully exploit sentiment linguistic knowledge.
Approach:	They propose a Multi-sentiment-resource Enhanced Attention Network to integrate sentiment linguistic knowledge into the deep neural network via attention mechanisms.
Outcome:	The proposed network captures sentiments from different representation sub-spaces, and is superior to strong competitors.

HATA: Trainable and Hardware-Efficient Hash-Aware Top-k Attention for Scalable Large Model Inference (2025.findings-acl)

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Ping Gong, Jiawei Yi, Shengnan Wang, Juncheng Zhang, Zewen Jin, Ouxiang Zhou, Ruibo Liu, Guanbin Xu, Youhui Bai, Bowen Ye, Kun Yuan, Tong Yang, Gong Zhang, Renhai Chen, Feng Wu, Cheng Li

LLM-VA: Resolving the Jailbreak-Overrefusal Trade-off via Vector Alignment (2026.acl-long)

Challenge:	Existing top-k attention methods struggle to strike a balance between efficiency and accuracy.
Approach:	They propose a top-k attention approach that integrates low-overhead techniques into the Top-k Attention process to achieve 7.2 speedup compared to vanilla full attention.
Outcome:	The proposed approach achieves 7.2 speedup compared to current top-k attention methods while maintaining model accuracy.

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Haonan Zhang, Dongxia Wang, Yi Liu, Kexin Chen, Wenhai Wang

ESGenius: Benchmarking LLMs on Environmental, Social, and Governance (ESG) and Sustainability Knowledge (2025.emnlp-main)

Challenge:	Existing vector steering methods adjust the magnitude of answer vectors, but this creates a fundamental trade-off—reducing jailbreak increases over-refusal.
Approach:	They propose a method which aligns va with vb through closed-form weight updates, making the model’s willingness to respond causally dependent on its safety assessment.
Outcome:	Experiments on 12 LLMs show that the proposed method achieves 11.45% higher F1 than the best baseline while preserving 95.92% utility.

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Chaoyue He, Xin Zhou, Yi Wu, Xinjia Yu, Yan Zhang, Lei Zhang, Di Wang, Shengfei Lyu, Hong Xu, Wang Xiaoqiao, Wei Liu, Chunyan Miao

ATLANTIS: Weak-to-Strong Learning via Importance Sampling (2025.acl-long)

Challenge:	ESGenius is a comprehensive benchmark for evaluating Large Language Models on ESG and sustainability knowledge.
Approach:	They introduce ESGenius, a benchmark for evaluating and enhancing ESG proficiency . they use a rigorous two-stage evaluation protocol and a repository of foundational frameworks .
Outcome:	ESGenius is a benchmark for evaluating and enhancing the proficiency of Large Language Models (LLMs) in ESG and sustainability-focused question answering.

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Yi Liu, Guoyin Wang, Shicheng Li, Feifan Song, Xu Sun

Challenge:	ATLANTIS is a new technique to improve the performance of large language models.
Approach:	They propose a new technique to bridge the gap between the distribution of current datasets and the real-world data distribution by using importance sampling.
Outcome:	The proposed technique can bring consistent and significant improvements to models’ performance and can be flexibly transferred among models with different structures.

Decentralized Arena: Towards Democratic and Scalable Automatic Evaluation of Language Models (2026.acl-long)

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Yanbin Yin, Kun Zhou, Zhen Wang, Xiangdong Zhang, Yifei Shao, Shibo Hao, Yi Gu, Jieyuan Liu, Somanshu Singla, Tianyang Liu, Eric P. Xing, Zhengzhong Liu, Haojian Jin, Zhiting Hu

Repairing Catastrophic-Neglect in Text-to-Image Diffusion Models via Attention-Guided Feature Enhancement (2024.findings-emnlp)

Challenge:	closed-ended question-based benchmarks struggle with saturation as newer models emerge . crowd-sourced leaderboards rely on costly and slow human judges .
Approach:	They propose a framework that leverages collective intelligence from all large language models to evaluate each other.
Outcome:	a new framework enables a democratic, pairwise evaluation of all large language models . it achieves 97% correlation with human judgements, while significantly reducing the cost.

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Zhiyuan Chang, Mingyang Li, Junjie Wang, Yi Liu, Qing Wang, Yang Liu

Task Oriented In-Domain Data Augmentation (2024.emnlp-main)

Challenge:	Text-to-Image Diffusion models generate high-quality images from textual descriptions, but they often produce images that do not fully align with the input prompts, resulting in semantic inconsistencies.
Approach:	They propose an automated repair approach to address catastrophic-neglect in T2I DMs.
Outcome:	The proposed model achieves 10.1%-16.3% higher Correct Rate in image generation compared to baselines.

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Xiao Liang, Xinyu Hu, Simiao Zuo, Yeyun Gong, Qiang Lou, Yi Liu, Shao-Lun Huang, Jian Jiao

Numerical Reasoning

Challenge:	Existing methods for large language models suffer from two major issues: in-domain data are scarce compared with general domain-agnostic data.
Approach:	They propose a task-oriented in-domain data augmentation framework that uses in- domain data selection and task-orientated synthetic passage generation to adapt LLMs to two domains: advertisement and math.
Outcome:	The proposed framework improves LLM performance by 8% in the advertisement domain and 7.5% in the math domain.

SimulSpeech: End-to-End Simultaneous Speech to Text Translation (2020.acl-main)

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Yi Ren, Jinglin Liu, Xu Tan, Chen Zhang, Tao Qin, Zhou Zhao, Tie-Yan Liu

Subtle Errors in Reasoning: Preference Learning via Error-injected Self-editing (2025.acl-long)

Challenge:	SimulSpeech is an end-to-end simultaneous speech to text translation system . conventional approaches to simultaneous speech translation divide the translation process into two stages .
Approach:	They develop an end-to-end simultaneous speech to text translation system which translates speech in source language to text in target language concurrently.
Outcome:	The proposed system achieves reasonable BLEU scores and lower delay compared to full-sentence translation model.

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Kaishuai Xu, Tiezheng Yu, Wenjun Hou, Yi Cheng, Chak Tou Leong, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li

From Superficial to Deep: Integrating External Knowledge for Follow-up Question Generation Using Knowledge Graph and LLM (2025.coling-main)

Challenge:	Existing studies to improve mathematical ability typically involve applying preference learning to step-wise solution pairs, but they overlook critical subtle errors.
Approach:	They propose a preference learning framework that injects predefined subtle errors into pivotal tokens to construct hard pairs for error mitigation.
Outcome:	Extensive experiments show that the proposed framework improves on Qwen2-7B-Instruct and MATH with 4.5K training samples.

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Jianyu Liu, Yi Huang, Sheng Bi, Junlan Feng, Guilin Qi

Knowledge Representation

Challenge:	Existing methods for generating follow-up questions are limited to shallow contextual questions that are uninspiring and have a large gap to the human level.
Approach:	They propose a three-stage external knowledge-enhanced follow-up question generation method which generates questions by identifying contextual topics, building a knowledge graph online, and finally combining these with a large language model to generate the final question.
Outcome:	The proposed method generates questions by identifying contextual topics, building a knowledge graph (KG) online, and finally combining these with a large language model to generate the final question.

How Do Answer Tokens Read Reasoning Traces? Self-Reading Patterns in Thinking LLMs for Quantitative Reasoning (2026.findings-acl)

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Haoyang Chen, Yi Liu, Jianzhi Shao, Tao Zhang, Chengfu Huo, Wei Hu

Exploring Mode Connectivity for Pre-trained Language Models (2022.emnlp-main)

Challenge:	Prior work on activation steering has focused on shaping reasoning traces, but it remains unclear how answer tokens actually read and integrate the reasoning to produce reliable outcomes.
Approach:	They propose a training-free steering method that uses self-reading quality scores to guide inference toward benign self-readiness and away from uncertain and disorganized reading.
Outcome:	The proposed method yields consistent accuracy gains in the reasoning traces generated by thinking LLMs.

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Yujia Qin, Cheng Qian, Jing Yi, Weize Chen, Yankai Lin, Xu Han, Zhiyuan Liu, Maosong Sun, Jie Zhou

Knowledge Inheritance for Pre-trained Language Models (2022.naacl-main)

Challenge:	Recent years have witnessed the prevalent application of pre-trained language models (PLMs) in NLP. From the perspective of parameter space, PLMs provide generic initialization, starting from which high-performance minima could be found.
Approach:	They investigate the geometric connections of different minima through the lens of mode connectivity, which measures whether two minima can be connected with a low-loss path.
Outcome:	The proposed model can be used to find low-loss paths between two minima, and to understand how their mode connectivity affects their task knowledge.

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Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Revisiting Over-Smoothness in Text to Speech (2022.acl-long)

Challenge:	Existing large-scale pre-trained language models are mainly trained from scratch individually, ignoring that many well-taught PLMs are available.
Approach:	They propose a pre-training framework called knowledge inheritance and propose auxiliary supervision to efficiently learn larger PLMs.
Outcome:	The proposed framework can be used to train large-scale language models with huge parameters and a large dataset can be adapted to domain adaptation and knowledge transfer.

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Yi Ren, Xu Tan, Tao Qin, Zhou Zhao, Tie-Yan Liu

ORGAN: Observation-Guided Radiology Report Generation via Tree Reasoning (2023.acl-long)

Challenge:	Non-autoregressive text to speech models ignore correlation in time and frequency domains, causing blurry results.
Approach:	They revisit the problem of over-smoothness in non-autoregressive text to speech models . they use methods that reduce complexity of data distributions and improve modeling methods .
Outcome:	The proposed models achieve better voice quality and faster inference speed than autoregressive models.

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Wenjun Hou, Kaishuai Xu, Yi Cheng, Wenjie Li, Jiang Liu

Weakly-supervised Text Classification Based on Keyword Graph (2021.emnlp-main)

Challenge:	Existing methods to generate radiology reports only rely on high-level plans, but they lack important information.
Approach:	They propose an Observation-guided radiology Report Generation framework which generates free-text descriptions for a set of radiographs.
Outcome:	The proposed framework outperforms state-of-the-art methods regarding text quality and clinical efficacy.

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Lu Zhang, Jiandong Ding, Yi Xu, Yingyao Liu, Shuigeng Zhou

Evaluating and Aligning Human Economic Risk Preferences in LLMs (2025.emnlp-main)

Challenge:	Existing methods for text classification ignore keyword correlation, thus ignoring it . existing methods treat keywords independently, thus not exploiting correlation between them .
Approach:	They propose a framework to explore keyword-keyword correlation on keyword graph by GNN . they use a self-supervised task to pretrain annotators and fine-tune them .
Outcome:	The proposed method outperforms existing methods on long- and short-text datasets.

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Jiaxin Liu, Yixuan Tang, Yi Yang, Kar Yan Tam

Demystifying Small Language Models for Edge Deployment (2025.acl-long)

Challenge:	Large Language Models (LLMs) are increasingly used in decision-making scenarios that involve risk assessment, yet their alignment with human economic rationality remains unclear.
Approach:	They propose an evaluation metric called Risk Disparity Score (RDS) and assess whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual’s persona.
Outcome:	The proposed evaluation metric assesses whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual’s persona.

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Zhenyan Lu, Xiang Li, Dongqi Cai, Rongjie Yi, Fangming Liu, Wei Liu, Jian Luan, Xiwen Zhang, Nicholas D. Lane, Mengwei Xu

Can Persona-Prompted LLMs Emulate Subgroup Values? An Empirical Analysis of Generalisability and Fairness in Cultural Alignment (2026.acl-long)

Challenge:	Small language models (SLMs) are a promising solution for resource-constrained devices such as smartphones and the Web of Things.
Approach:	They propose to use SLMs to build and optimize a set of small language models that are publicly accessible.
Outcome:	The proposed models outperform 7B models in general tasks, while their in-context learning capabilities remain limited and their efficiency has significant optimization potential.

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Bryan Chen Zhengyu Tan, Zhengyuan Liu, Xiaoyuan Yi, Jing Yao, Xing Xie, Nancy F. Chen, Roy Ka-Wei Lee

SpecEdit: A Spectral Approach for Multi-Round Knowledge Editing (2026.findings-acl)

Challenge:	Current alignment paradigms treat "human values" as a monolithic entity, ignoring the fact that many societies are a mosaic of diverse subgroups with distinct and sometimes conflicting values, preferences, and norms.
Approach:	They examine whether Large Language Models can emulate distinct cultural values of subgroups . they use a global value survey to examine the value landscape of a multicultural society .
Outcome:	The proposed model improves on unseen, out-of-distribution subgroups by 17.4% . the model widens the disparity between subgroup groups when measured by distance-aware metrics.

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Junxian Liu, Zikun Deng, Xin Wu, Dazhen Deng, Yi Cai

Explainability & Interpretability in NLP

Challenge:	Multi-round knowledge editing suffers from performance degradation as edits accumulate . intrinsic knowledge of model and historical edit memories are naively coupled during editing . SpecEdit improves model editing performance by reducing destructive coupling .
Approach:	They propose a spectral-based model editing module that integrates into existing editing methods without altering their original optimization procedures.
Outcome:	The proposed model improves performance on multiple LLMs and editing methods.

FPE2M2: Approaching Lossless and Efficient Quantization with Native Floating Point (2025.findings-acl)

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Ke Yi, Jianwei Zhang, Zhiying Xu, Xinlong Yang, Yang Zhou, Minmin Sun, Zengke Liu, Tong Zhang, Junyang Lin, Jingren Zhou

Collaborative Chain-of-Agents for Parametric-Retrieved Knowledge Synergy (2026.acl-long)

Challenge:	Auto-regressive decoding is a memory-bound job, meaning decoding performance is limited by the bandwidth rather than the computational capabilities of the GPU.
Approach:	They propose a framework that supports lossless weight-only quantization inference and validate it on Qwen and LLaMA Models.
Outcome:	The proposed framework achieves the highest efficiency with lossless accuracy on Qwen and LLaMA Models across various modalities.

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Yi Jiang, Sendong Zhao, Jianbo Li, Haochun Wang, Lizhe Zhang, Yan Liu, Bing Qin

Multi-Attribute Controlled Text Generation with Contrastive-Generator and External-Discriminator (2022.coling-1)

Challenge:	Existing RAG methods focus on external retrieval, while ignoring the rich content of the model.
Approach:	They propose a framework that enhances explicit synergy over parametric and retrieved knowledge by integrating external retrieval components into the input context of the LLMs.
Outcome:	The proposed framework enhances explicit synergy over parametric and retrieved knowledge.

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Guisheng Liu, Yi Li, Yanqing Guo, Xiangyang Luo, Bo Wang

Knowledge Graph-Guided Retrieval Augmented Generation (2025.naacl-long)

Challenge:	Existing studies on controlled text generation focus on single-attribute control, but in practical applications, they lack controllability.
Approach:	They propose a framework for multi-attribute controlled text generation that can effectively generate texts with more attributes.
Outcome:	The proposed framework achieves remarkable controllability while keeping the text fluent and diverse.

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Xiangrong Zhu, Yuexiang Xie, Yi Liu, Yaliang Li, Wei Hu

Chinese Lexical Substitution: Dataset and Method (2023.emnlp-main)

Challenge:	Existing studies on RAG focus on semantic retrieval of isolated relevant chunks, which ignore their intrinsic relationships.
Approach:	They propose a framework that utilizes knowledge graphs to provide fact-level relationships between chunks, improving the diversity and coherence of the retrieved results.
Outcome:	Extensive experiments on the HotpotQA dataset and its variants demonstrate the advantages of KG2RAG compared to existing RAG-based approaches in terms of response quality and retrieval quality.

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Jipeng Qiang, Kang Liu, Ying Li, Yun Li, Yi Zhu, Yun-Hao Yuan, Xiaocheng Hu, Xiaoye Ouyang

Low-Resource NLP

Challenge:	Existing benchmarks for lexical substitution (LS) are limited and limited in coverage . despite extensive research on Lexical Substitution in various languages, there is limited evidence for LS in Chinese.
Approach:	They propose to use human and machine collaboration to construct a Chinese LS dataset . they combine four unsupervised LS methods to generate candidate substitutes .
Outcome:	The proposed method outperforms existing benchmarks on the Chinese lexical substitution task.

LongTutor: Benchmarking Large Language Models for Long-term Personalized Tutoring (2026.acl-long)

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Ning Li, Zheng Zhang, Zhenya Huang, Rui Li, Yi Zhan, Yinbo Luo, Qi Liu, Enhong Chen

HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization (2025.emnlp-main)

Challenge:	Existing evaluations focus on isolated, short-term interactions, overlooking the inherently long-term nature of learning.
Approach:	They propose a benchmark for long-term personalized tutoring based on an annotated learning log . they propose an automated generator–verifier pipeline to enable benchmark expansion .
Outcome:	The proposed benchmarks evaluate LLMs across three progressive tasks: evidence acquisition, knowledge state diagnosis, and adaptive teaching action.

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Huaqin Zhao, Jiaxi Li, Yi Pan, Shizhe Liang, Xiaofeng Yang, Fei Dou, Tianming Liu, Jin Lu

Design Challenges for a Multi-Perspective Search Engine (2022.findings-naacl)

Challenge:	Large language models (LLMs) face memory challenges due to the high cost of backpropagation.
Approach:	They propose a zeroth-order (ZO) optimization that matches memory usage to inference . they propose scalable and memory-efficient zeroth order (ZE) optimizer that integrates annealed A-GNB gradients with diagonal Hessian estimation and layer-wise clipping as a second-order pre-conditioner.
Outcome:	The proposed algorithm outperforms state-of-the-art methods with an average speedup of 20 over MeZO on RoBERTa-large and OPT-1.3B.

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Sihao Chen, Siyi Liu, Xander Uyttendaele, Yi Zhang, William Bruno, Dan Roth

Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation (2022.emnlp-main)

Challenge:	a document retrieval system fails to deliver diverse and direct responses to controversial questions . classical document retrievals provide a ranked list of references to relevant but not necessarily trustworthy web documents .
Approach:	They propose a perspective-oriented document retrieval paradigm to address these challenges . they propose sponses with different perspectives within topically-related web documents .
Outcome:	The proposed system is based on a user survey and a prototype . it will be used to assess the utility and understanding of the system .

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Yang Yu, Fangzhao Wu, Chuhan Wu, Jingwei Yi, Qi Liu

Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling (D19-1)

Challenge:	Existing work fine tunes the PLM with the news recommendation task, which can cause a domain shift problem.
Approach:	They propose a self-supervised method to adapt general PLM to news domain with a contrastive matching task between news titles and news bodies.
Outcome:	The proposed method can improve both the effectiveness and efficiency of the large PLM-based news recommendation model while maintaining its performance.

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Jinfeng Rao, Linqing Liu, Yi Tay, Wei Yang, Peng Shi, Jimmy Lin

Sticking to the Mean: Detecting Sticky Tokens in Text Embedding Models (2025.acl-long)

Challenge:	Existing techniques for relevance and semantic matching cannot be easily adapted to the other.
Approach:	They propose a model that incorporates a hybrid encoder module, a relevance matching module and co-attention mechanisms that capture context-aware semantic relatedness.
Outcome:	The proposed model incorporates a hybrid encoder module, a relevance matching module and co-attention mechanisms that capture context-aware semantic relatedness.

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Kexin Chen, Dongxia Wang, Yi Liu, Haonan Zhang, Wenhai Wang

Representation Learning

Challenge:	Sticky tokens, when repeatedly inserted into sentences, pull sentence similarity toward a certain value, disrupting the normal distribution of embedding distances and degrading downstream performance.
Approach:	They propose a method to detect “sticky tokens” by sentence and token filtering and apply it to 40 checkpoints across 14 model families.
Outcome:	The proposed method detects 868 sticky tokens across 14 models and shows that their presence does not correlate with model size or vocabulary size.

Target-Sensitive Memory Networks for Aspect Sentiment Classification (P18-1)

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Shuai Wang, Sahisnu Mazumder, Bing Liu, Mianwei Zhou, Yi Chang

Sentiment Analysis

Challenge:	Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis.
Approach:	They propose to use memory networks to deal with ASC using aspect and sentence terms and use them to classify the sentiment polarity.
Outcome:	The proposed techniques can be implemented in a variety of contexts and their effectiveness is evaluated.

Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting (2021.acl-long)

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Yi Cheng, Siyao Li, Bang Liu, Ruihui Zhao, Sujian Li, Chenghua Lin, Yefeng Zheng

Mitigating Over-Generation for Unsupervised Keyphrase Extraction with Heterogeneous Centrality Detection (2023.emnlp-main)

Challenge:	Existing QG systems perform substantially worse in answering multi-hop questions than single-hop ones.
Approach:	They propose a framework that progressively increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain.
Outcome:	The proposed framework increases question difficulty through step-by-step rewriting under the guidance of an extracted reasoning chain.

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Mingyang Song, Pengyu Xu, Yi Feng, Huafeng Liu, Liping Jing

LIRE: listwise reward enhancement for preference alignment (2024.findings-acl)

Challenge:	Existing keyphrase extraction models incorrectly determine a keyphrase as a phrase but output other candidates as keyphrases because they contain the same word.
Approach:	They propose a new approach that detects both implicit and explicit centrality within a heterogeneous graph as the importance score of each candidate keyphrase.
Outcome:	The proposed approach outperforms state-of-the-art keyphrase extraction models on three benchmark datasets.

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Mingye Zhu, Yi Liu, Lei Zhang, Junbo Guo, Zhendong Mao

Challenge:	prevailing approaches to preference alignment focus on pairwise comparisons, with limited exploration into multi-response scenarios.
Approach:	They propose a listwise reward enhancement approach that integrates offline rewards of multiple responses into a streamlined listwise framework.
Outcome:	The proposed approach outperforms existing methods on dialogue and summarization tasks with good transferability to out-of-distribution data.

CTRAP: Embedding Collapse Trap to Safeguard Large Language Models from Harmful Fine-Tuning (2026.acl-long)

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Biao Yi, Tiansheng Huang, Baolei Zhang, Tong Li, Lihai Nie, Zheli Liu, Li Shen

Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network (P18-1)

Challenge:	Fine-tuning-as-a-service exposes models to harmful fine-tuneing attacks . however, inherent general adaptability of LLMs allows them to bypass selective unlearning by rapidly relearning or repurposing their general capabilities for harmful tasks.
Approach:	They propose a paradigm shift that inducing model collapse instead of selective removal by relearning or repurposing general capabilities for harmful tasks.
Outcome:	The proposed model collapse mechanism neutralizes the very general capabilities that attackers exploit, tackling the core issue unaddressed by selective unlearning.

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Xiangyang Zhou, Lu Li, Daxiang Dong, Yi Liu, Ying Chen, Wayne Xin Zhao, Dianhai Yu, Hua Wu

RLKGF: Reinforcement Learning from Knowledge Graph Feedback Without Human Annotations (2025.findings-acl)

Challenge:	Existing models for matching dialogue responses rely on semantic and functional dependencies . a recent study only uses the last utterance in context for matching a reply .
Approach:	They propose a model that matches a response with its multi-turn context using attention.
Outcome:	The proposed model outperforms the state-of-the-art models on two large-scale multi-turn response selection tasks.

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Lian Yan, Chen Tang, Yi Guan, Haotian Wang, Songyuan Wang, Haifeng Liu, Yang Yang, Jingchi Jiang

RL-Guider: Leveraging Historical Decisions and Feedback for Drug Editing with Large Language Models (2025.findings-acl)

Challenge:	Lack of human preference labels remains a significant bottleneck when applying RLHF to a downstream domain.
Approach:	They propose a method that leverages human priors encoded in Knowledge Graphs (KGs) to derive RL rewards in the absence of manual annotations.
Outcome:	Experiments on three public and one private medical dialogue datasets show that the proposed method outperforms the competitive RLAIF in improving LLM diagnostic accuracy.

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Xufeng Liu, Yixuan Ding, Jingxiang Qu, Yichi Zhang, Wenhan Gao, Yi Liu

RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning (2023.findings-emnlp)

Challenge:	RLs can be used to refine drugs by iterative conversations with domain experts . existing methods do not leverage past knowledge, but human experts develop intuition over time through historical experience .
Approach:	They propose a reinforcement-learning agent to provide suggestions to large language models . RL-Guider leverages the “world-level” knowledge of LLMs and historical feedback .
Outcome:	a new reinforcement-learning agent improves the performance of large language models . the proposed agent leverages the “world-level” knowledge of LLMs and historical feedback .

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Wenjun Hou, Yi Cheng, Kaishuai Xu, Wenjie Li, Jiang Liu

Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation (2024.acl-long)

Challenge:	Recent studies have focused on producing concise observations while neglecting the precise attributes that determine the severity of diseases.
Approach:	They propose a model that generates precise radiology reports via dynamic disease progression reasoning by combining historical and spatiotemporal information.
Outcome:	Experiments on two publicly available datasets show the proposed model can generate precise and accurate radiology reports with dynamic disease progression reasoning.

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Yi Liu, Xiangyu Liu, Xiangrong Zhu, Wei Hu

Learning from Sibling Mentions with Scalable Graph Inference in Fine-Grained Entity Typing (2022.acl-long)

Challenge:	Existing studies neglect attribute correlations formed by the intertwining of different attributes.
Approach:	They propose a multi-aspect controllable text generation method with disentangled counterfactual augmentation that alleviates imbalanced attribute correlations during training by disentanglement.
Outcome:	The proposed method outperforms state-of-the-art methods in imbalanced and balanced attribute correlation scenarios.

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Yi Chen, Jiayang Cheng, Haiyun Jiang, Lemao Liu, Haisong Zhang, Shuming Shi, Ruifeng Xu

Towards Database-Free Text-to-SQL Evaluation: A Graph-Based Metric for Functional Correctness (2025.coling-main)

Challenge:	Existing models struggle to handle hard mentions due to insufficient contexts, limiting their overall typing performance.
Approach:	They propose to exploit sibling mentions to enhance the mention representations by adding unseen test mentions as new nodes for inference.
Outcome:	The proposed model outperforms ten strong baseline models and outperformed strong baselines.

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Yi Zhan, Longjie Cui, Han Weng, Guifeng Wang, Yu Tian, Boyi Liu, Yingxiang Yang, Xiaoming Yin, Jiajun Xie, Yang Sun

SEAD: A Surrogate-free Label-only Membership Inference Attack against Pre-trained LLMs with Semantic-Aware Density (2026.findings-acl)

Challenge:	Existing metrics for evaluating functional correctness of SQL queries are prone to false positives due to inadequately prepared test databases.
Approach:	They propose a graph-based metric that uses a relational operator tree to extract rich semantic information from the logical execution plan of SQL queries and embed it into a diagram.
Outcome:	The proposed method eliminates the need for extensive test database preparation and performs graph matching on unseen SQL queries.

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Biao Yi, Jiahao Li, Yiming Li, Yu He, Baolei Zhang, Zheli Liu, Dacheng Tao

RTADev: Intention Aligned Multi-Agent Framework for Software Development (2025.findings-acl)

Challenge:	Existing membership inference attacks require access to complete logits, but such access is often unavailable in real-world deployments where only the generated text is exposed.
Approach:	They propose a surrogate-free label-only MIA approach that directly estimates token probabilities through Monte Carlo sampling of the target model.
Outcome:	The proposed approach outperforms existing label-only attacks and serves as a foundational density estimator in the label-exclusive setting.

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Jie Liu, Guohua Wang, Ronghui Yang, Jiajie Zeng, Mengchen Zhao, Yi Cai

PunchBench: Benchmarking MLLMs in Multimodal Punchline Comprehension (2025.acl-long)

Challenge:	Large Language Models (LLMs) are efficient assistants to humans in software development tasks, but they can cause errors during the development process.
Approach:	They propose an intention aligned multi-agent framework that ensures that all agents work based on a consensus.
Outcome:	The proposed framework reduces errors and improves the quality of generated software code.

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Kun Ouyang, Yuanxin Liu, Shicheng Li, Yi Liu, Hao Zhou, Fandong Meng, Jie Zhou, Xu Sun

A Study of Non-autoregressive Model for Sequence Generation (2020.acl-main)

Challenge:	Existing benchmarks on punchline comprehension suffer from language shortcuts that allow models to rely on text, lack of question diversity, and narrow focus on a specific domain of multimodal content.
Approach:	They propose a multimodal punchline comprehension benchmark to assess models' ability to comprehend punchlines.
Outcome:	The proposed model surpasses in-context learning and chain-of-thought in punchline comprehension.

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Yi Ren, Jinglin Liu, Xu Tan, Zhou Zhao, Sheng Zhao, Tie-Yan Liu

Multimodal Sarcasm Target Identification in Tweets (2022.acl-long)

Challenge:	Non-autoregressive (NAR) models generate all tokens in parallel, resulting in faster generation speed compared to autoregressive models.
Approach:	They propose to use knowledge distillation and source-target alignment to bridge the gap between NAR and autoregressive models in various tasks.
Outcome:	The proposed techniques can speed up NAR models in some tasks but not all . the proposed techniques reduce target token dependency while allowing for faster inference .

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Jiquan Wang, Lin Sun, Yi Liu, Meizhi Shao, Zengwei Zheng

MMAC: A Multilingual, Multimodal Alignment Framework for Cultural Grounding Evaluation (2026.acl-long)

Challenge:	Existing methods to detect sarcasm target with text lacking context are not sufficient and complete.
Approach:	They propose a multi-modal sarcasm target identification task that performs both textual and visual detection.
Outcome:	The proposed model can perform textual target labeling and visual target detection.

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Weihua Zheng, Zhengyuan Liu, Tanmoy Chakraborty, Weiwen Xu, Xiaoxue Gao, Bryan Chen Zhengyu Tan, Bowei Zou, Chang Liu, Yujia Hu, Xing Xie, Xiaoyuan Yi, Jing Yao, Chaojun Wang, Long Li, Rui Liu, Huiyao Liu, Koji Inoue, Ryuichi Sumida, Tatsuya Kawahara, Fan Xu, Lingyu Ye, Wei Tian, Dongjun Kim, Jimin Jung, Jaehyung Seo, Nadya Yuki Wangsajaya, Pham Minh Duc, Ojasva Saxena, Palash Nandi, Xiyan Tao, Wiwik Karlina, Tuan Luong, Keertana Arun Vasan, Roy Ka-Wei Lee, Nancy F. Chen

Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems (2021.naacl-demos)

Challenge:	Existing models lack cultural alignment across modalities and languages . a new framework to assess cultural awareness across linguistics and languages is needed .
Approach:	They propose a framework that integrates tri-modally aligned cultural benchmarks and a five-dimensional evaluation protocol to assess cross-country awareness disparities.
Outcome:	The proposed framework assesses cultural awareness disparities across modalities and languages . it is the first dataset aligned at the input level across text, image, and speech .

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Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung, Maryam Fazel-Zarandi, Raefer Gabriel, Shuyang Gao, Rahul Goel, Dilek Hakkani-Tur, Jan Jezabek, Abhay Jha, Jiun-Yu Kao, Prakash Krishnan, Peter Ku, Anuj Goyal, Chien-Wei Lin, Qing Liu, Arindam Mandal, Angeliki Metallinou, Vishal Naik, Yi Pan, Shachi Paul, Vittorio Perera, Abhishek Sethi, Minmin Shen, Nikko Strom, Eddie Wang

FastDiff 2: Revisiting and Incorporating GANs and Diffusion Models in High-Fidelity Speech Synthesis (2023.findings-acl)

Challenge:	Traditional goal-oriented dialogue systems require annotations which are hard to obtain for every new domain, limiting scalability.
Approach:	They propose a data-driven approach to building goal-oriented dialogue systems . they use a seed dialogue simulator to generate annotated conversations instead of collecting annotations .
Outcome:	The proposed system improves turn-level action signature prediction accuracy by 50% . the system is scalable, extensible and data efficient .

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Rongjie Huang, Yi Ren, Ziyue Jiang, Chenye Cui, Jinglin Liu, Zhou Zhao

What’s Missing in Screen-to-Action? Towards a UI-in-the-Loop Paradigm for Multimodal GUI Reasoning (2026.findings-acl)

Challenge:	Experimental results show that Generative adversarial networks sacrifice sample diversity for quality and speed, while diffusion models exhibit outperformed sample quality and diversity at a high computational cost.
Approach:	They propose to combine GANs and diffusion probabilistic models to achieve better sample quality and diversity.
Outcome:	The proposed models outperform GANs and diffusion models in speech synthesis . the proposed models enjoy an efficient 4-step sampling process and exhibit better sample diversity .

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Songze Li, Xiaoke Guo, Tianqi Liu, Biao Yi, Zhaoyan Gong, Zhiqiang Liu, Huajun Chen, Wen Zhang

VENUS: A VLLM-driven Video Content Discovery System for Real Application Scenarios (2025.emnlp-industry)

Challenge:	Existing GUI reasoning methods rely on direct screen-based decision-making, which lacks interpretability and overlooks a comprehensive understanding of UI elements, ultimately leading to task failure.
Approach:	They propose a GUI reasoning paradigm that treats the GUI reasoning task as a cyclic *Screen-UI elements-Action process.
Outcome:	The proposed paradigm achieves state-of-the-art UI understanding performance while yielding superior results in GUI reasoning tasks.

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Minyi Zhao, Yi Liu, Jianfeng Wen, Boshen Zhang, Hailang Chang, Zhiheng Ouyang, Jie Wang, Wensong He, Shuigeng Zhou

Visual Data in NLP

Challenge:	Video Content Discovery (VCD) is to identify specific videos defined by a pre-specified text policy.
Approach:	They propose a Vision-Language Large Model-driven video content discovery system called VENUS to solve these problems.
Outcome:	The proposed system generates high-quality, VCD-specific data for model training and extends it to support it better.

Knowledge Context Modeling with Pre-trained Language Models for Contrastive Knowledge Graph Completion (2024.findings-acl)

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Guangqian Yang, Yi Liu, Lei Zhang, Licheng Zhang, Hongtao Xie, Zhendong Mao

Fine-grained Knowledge Enhancement for Retrieval-Augmented Generation (2025.findings-acl)

Challenge:	Text-based knowledge graph completion methods neglect knowledge contexts in inferring process.
Approach:	They propose a framework which models the knowledge context as additional prompts with pre-trained language models for knowledge graph completion.
Outcome:	The proposed framework achieves state-of-the-art on FB15k-237, WN18RR and Wikidata5M datasets.

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Jingxuan Han, Zhendong Mao, Yi Liu, Yexuan Che, Zheren Fu, Quan Wang

M-ABSA: A Multilingual Dataset for Aspect-Based Sentiment Analysis (2025.emnlp-main)

Challenge:	Existing studies rely on semantic similarity to retrieve knowledge but ignore fine-grained information within documents.
Approach:	They propose a fine-grained knowledge enhancement method to fill knowledge gaps with retrieved external information by a Chain-of-Thought prompting procedure and a decoding enhancement strategy to constrain the document-based decoding process.
Outcome:	The proposed method can be applied in a plug-and-play manner to enhance its performance with no additional modules or training process.

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ChengYan Wu, Bolei Ma, Yihong Liu, Zheyu Zhang, Ningyuan Deng, Yanshu Li, Baolan Chen, Yi Zhang, Yun Xue, Barbara Plank

Multilinguality

Challenge:	Existing studies focus on English-centric aspects of sentiment analysis, limiting scope for multilingual evaluation and research.
Approach:	They propose to use a multilingual dataset to analyze aspects with associated sentiment elements in text.
Outcome:	The proposed dataset is the most extensive multilingual parallel dataset for ABSA to date.

Prompt-Guided Internal States for Hallucination Detection of Large Language Models (2025.acl-long)

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Fujie Zhang, Peiqi Yu, Biao Yi, Baolei Zhang, Tong Li, Zheli Liu

Mixture of Heterogeneous Grouped Experts for Language Modeling (2026.acl-industry)

Challenge:	Large Language Models (LLMs) generate incorrect or logically incorrect responses, which is known as LLM hallucinations.
Approach:	They propose a framework for supervised hallucination detection using in-domain data by prompting changes to the structure related to text truthfulness in LLMs’ internal states.
Outcome:	The proposed framework enhances the cross-domain generalization of existing hallucination detection methods.

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Zhicheng Ma, Xiang Liu, Zhaoxiang Liu, Ning Wang, Yi Shen, Kai Wang, Shuming Shi, Shiguo Lian

Compound AI Systems Optimization: A Survey of Methods, Challenges, and Future Directions (2025.emnlp-main)

Challenge:	Large Language Models (LLMs) based on Mixture-of-Experts (MoE) enforce uniform expert sizes, creating a rigidity that fails to align computational costs with varying token-level complexity.
Approach:	They propose a mixture of heterogeneous grouped experts (MoHGE) that allows for flexible, resource-aware expert combinations.
Outcome:	The proposed model matches the performance of existing Mixture-of-Experts architectures while maintaining balanced GPU utilization.

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Yu-Ang Lee, Guan-Ting Yi, Mei-Yi Liu, Jui-Chao Lu, Guan-Bo Yang, Yun-Nung Chen

Mehran Kazemi, Bahare Fatemi, Hritik Bansal, John Palowitch, Chrysovalantis Anastasiou, Sanket Vaibhav Mehta, Lalit K Jain, Virginia Aglietti, Disha Jindal, Peter Chen, Nishanth Dikkala, Gladys Tyen, Xin Liu, Uri Shalit, Silvia Chiappa, Kate Olszewska, Yi Tay, Vinh Q. Tran, Quoc V Le, Orhan Firat

Challenge:	Recent advances in large language models (LLMs) and AI systems have led to a paradigm shift in the design and optimization of complex workflows.
Approach:	They propose a systematic review of recent progress in optimizing compound AI systems . they formalize the notion of compound AI system optimization and classify existing methods along several key dimensions .
Outcome:	The proposed methods outperform existing methods in the field of compound AI and highlight open research challenges and future directions.

BIG-Bench Extra Hard (2025.acl-long)

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CADReview: Automatically Reviewing CAD Programs with Error Detection and Correction (2025.acl-long)

Challenge:	Current benchmarks for large language model reasoning focus on math and coding abilities, leaving a gap in evaluating broader reasoning proficiencies.
Approach:	They propose a benchmark to evaluate general reasoning in large language models . they use BIG-Bench and its harder version BIG-Benefit Hard to assess general reasoning .
Outcome:	The new benchmark pushes the boundaries of LLM reasoning evaluation.

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Jiali Chen, Xusen Hei, HongFei Liu, Yuancheng Wei, Zikun Deng, Jiayuan Xie, Yi Cai, Li Qing

MCPG: A Flexible Multi-Level Controllable Framework for Unsupervised Paraphrase Generation (2022.findings-emnlp)

Challenge:	Computer-aided design (CAD) is crucial in prototyping 3D objects through geometric instructions.
Approach:	They propose a CAD review task to automatically detect and correct potential errors . they propose CAD program repairer framework to provide helpful feedback on error correction .
Outcome:	The proposed framework outperforms existing MLLMs in detecting errors and providing feedback on error correction.

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Yi Chen, Haiyun Jiang, Lemao Liu, Rui Wang, Shuming Shi, Ruifeng Xu

PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)

Challenge:	Existing studies on controllable unsupervised paraphrase generation are expensive and require supervised training on large parallel corpora.
Approach:	They propose a method for controllable unsupervised paraphrase generation that is flexible to adapt to specific domains without extra training.
Outcome:	The proposed method outperforms state-of-the-art unsupervised baselines by a margin.

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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei

Cross-Lingual Dependency Parsing by POS-Guided Word Reordering (2020.findings-emnlp)

Challenge:	Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning.
Approach:	They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios.
Outcome:	The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics.

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Lu Liu, Yi Zhou, Jianhan Xu, Xiaoqing Zheng, Kai-Wei Chang, Xuanjing Huang

Syntactic Text Processing

Challenge:	Existing approaches to cross-lingual dependency parsing rely on large corpus size and cost.
Approach:	They propose a cross-lingual dependency parsing approach based on word reordering . they propose to train a model that transfers knowledge learned in one or multiple languages to target languages .
Outcome:	The proposed approach outperforms the baseline approach in Hindi and Latin by 15.3% and 6.7%.

Learning How and What to Memorize: Cognition-Inspired Two-Stage Optimization for Evolving Memory (2026.acl-long)

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Derong Xu, Shuochen Liu, Pengfei Luo, Pengyue Jia, Yingyi Zhang, Yi Wen, Yimin Deng, Wenlin Zhang, Enhong Chen, Xiangyu Zhao, Tong Xu

Challenge:	Existing memory systems rely on static, hand-crafted update rules for personalization, but sparse outcome rewards provide weak supervision, resulting in unstable long-horizon optimization.
Approach:	They propose a memory guideline optimization framework that learns how memory should be organized and what information to update.
Outcome:	The proposed framework learns how memory should be organized and what information to update.

Open Domain Question Answering with Conflicting Contexts (2025.findings-naacl)

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Siyi Liu, Qiang Ning, Kishaloy Halder, Zheng Qi, Wei Xiao, Phu Mon Htut, Yi Zhang, Neha Anna John, Bonan Min, Yassine Benajiba, Dan Roth

Play Guessing Game with LLM: Indirect Jailbreak Attack with Implicit Clues (2024.findings-acl)

Challenge:	Open domain question answering systems often rely on information retrieved from large collections of text to answer questions.
Approach:	They evaluate and benchmark three powerful Large Language Models with a dataset . they find that 25% of unambiguous open domain questions can lead to conflicting contexts .
Outcome:	The proposed model can't be used to answer questions with conflicting contexts . it can be fine tuned to provide richer information into the model's training .

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Zhiyuan Chang, Mingyang Li, Yi Liu, Junjie Wang, Qing Wang, Yang Liu

Segment-Level and Category-Oriented Network for Knowledge-Based Referring Expression Comprehension (2023.findings-acl)

Challenge:	Existing jailbreak attacks primarily utilize scenario camouflage techniques, however their explicit mention of malicious intent will be easily recognized and defended by LLMs.
Approach:	They propose an indirect jailbreak attack approach, Puzzler, which can bypass LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query.
Outcome:	The proposed approach can bypass the LLM’s defensive strategies and obtain malicious response by implicitly providing LLMs with some clues about the original malicious query.

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Yuqi Bu, Xin Wu, Liuwu Li, Yi Cai, Qiong Liu, Qingbao Huang

Reinforcement Learning on Pre-Training Data (2026.acl-long)

Challenge:	Existing methods employ sentence-level retrieval and fusion methods, which may lead to similarity bias and interference from irrelevant information in unstructured knowledge sentences.
Approach:	They propose a segment-level and category-oriented network to solve similarity bias problem by segmenting and prompting knowledge retrieval methods and a category-based grounding method.
Outcome:	The proposed model eliminates similarity bias and improves the overall performance of the KB-REC task.

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

Can LLMs Hear the Dogwhistle? (2026.findings-acl)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Yifan Liu, Yi Lin, Xinwei Guo, Ziwei Wang, Jiaxin Zhang, Guanhua Chen, Haiyan Wu, Xiangyu Zhao, Xin Yao, Xuetao Wei

MDCR: A Dataset for Multi-Document Conditional Reasoning (2024.findings-emnlp)

Challenge:	Existing safety benchmarks focus on explicitly harmful content, but ignore context-dependent expressions such as dogwhistles.
Approach:	They propose a benchmark for evaluating LLM safety under dogwhistle-driven prompts . their findings expose a blind spot in current safety evaluation practices .
Outcome:	The proposed benchmark compared safety performance with toxic terms using dogwhistle-driven prompts.

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Peter Baile Chen, Yi Zhang, Chunwei Liu, Sejal Gupta, Yoon Kim, Mike Cafarella

The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) (2024.findings-acl)

Challenge:	ConditionalQA is limited to questions on single documents, neglecting harder cases that may require cross-document reasoning and optimization.
Approach:	They propose to use a dataset to evaluate models' ability to answer eligibility questions on single documents.
Outcome:	The proposed dataset can reflect real-world challenges and serve as a test bed for complex conditional reasoning that requires optimization.

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Shenglai Zeng, Jiankun Zhang, Pengfei He, Yiding Liu, Yue Xing, Han Xu, Jie Ren, Yi Chang, Shuaiqiang Wang, Dawei Yin, Jiliang Tang

EvoBench: Towards Real-world LLM-Generated Text Detection Benchmarking for Evolving Large Language Models (2025.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model generation with proprietary and private data, where data privacy is . a privacy issue that is currently under-explored, is posed by RAG.
Approach:	They propose to use retrieval-augmented generation (RAG) to facilitate language model generation with proprietary and private data where data privacy is a pivotal concern.
Outcome:	The proposed attack methods demonstrate that RAG can mitigate the old risks, i.e., leakage of the LLMs’ training data.

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Xiao Yu, Yi Yu, Dongrui Liu, Kejiang Chen, Weiming Zhang, Nenghai Yu, Jing Shao

Improving Multi-turn Emotional Support Dialogue Generation with Lookahead Strategy Planning (2022.emnlp-main)

Challenge:	Existing methods to detect LLM-generated texts rely on static benchmarks that neglect the evolving nature of LLMs.
Approach:	They propose a benchmark to evaluate the generalization of LLM-generated text detection methods.
Outcome:	The proposed benchmark measures generalization of 14 detection methods across LLMs.

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Yi Cheng, Wenge Liu, Wenjie Li, Jiashuo Wang, Ruihui Zhao, Bang Liu, Xiaodan Liang, Yefeng Zheng

Graph-Reward-SQL: Execution-Free Reinforcement Learning for Text-to-SQL via Graph Matching and Stepwise Reward (2025.findings-emnlp)

Challenge:	Existing research on building ES conversation systems only considered single-turn interactions with users, which is over-simplified and has limited support for multi-turn systems.
Approach:	They propose a multi-turn ES conversation system that uses lookahead heuristics to estimate future user feedback after using particular strategies.
Outcome:	The proposed system significantly outperforms baselines in both dialogue generation and strategy planning.

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Han Weng, Puzhen Wu, Cui Longjie, Yi Zhan, Boyi Liu, Yuanfeng Song, Dun Zeng, Yingxiang Yang, Qianru Zhang, Dong Huang, Xiaoming Yin, Yang Sun, Xing Chen

Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order (2020.acl-main)

Challenge:	Existing methods to enhance performance of large language models (LLMs) on Text-to-SQL tasks rely on execution-based or LLM-based reward models.
Approach:	They propose a reward model framework for RL-based Text-to-SQL that employs the GMNScore outcome reward model.
Outcome:	The proposed reward model outperforms existing reward models on standard benchmarks including Spider and BIRD.

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Yi Liao, Xin Jiang, Qun Liu

Communication Efficient Federated Learning for Multilingual Neural Machine Translation with Adapter (2023.findings-acl)

Challenge:	Large-scale pretrained language models such as masked language model (MLM) have brought significant improvements to many NLU and NLG tasks.
Approach:	They propose a probabilistic masking scheme for the masked language model and a model with a uniform prior distribution on the masking ratio.
Outcome:	The proposed model outperforms BERT on a bunch of downstream NLG tasks.

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Yi Liu, Xiaohan Bi, Lei Li, Sishuo Chen, Wenkai Yang, Xu Sun

Multilinguality

Challenge:	Existing frameworks for federated multilingual neural machine translation (Fed-MNMT) are limited in language resources.
Approach:	They propose a framework that keeps PLMs frozen and only transfers lightweight adapter modules between clients.
Outcome:	The proposed framework reduces communication cost by over 98% while achieving similar or even better performance compared to baselines.

Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation (2023.emnlp-main)

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Jiaang Li, Quan Wang, Yi Liu, Licheng Zhang, Zhendong Mao

Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language Models (2024.findings-emnlp)

Challenge:	Existing approaches to learning on Knowledge Graphs (KGs) are not critical for learning on KGs.
Approach:	They propose an alternative approach to represent entities by composing entity-corresponding codewords matched from predefined small-scale codebooks.
Outcome:	The proposed approach achieves similar results to existing methods.

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Wenhao Shi, Zhiqiang Hu, Yi Bin, Junhua Liu, Yang Yang, See-Kiong Ng, Lidong Bing, Roy Lee

Visual-Linguistic Dependency Encoding for Image-Text Retrieval (2024.lrec-main)

Challenge:	Existing image instruction fine-tuning datasets do not fully exploit visual information to enhance multimodal reasoning capabilities of Large language models (LLMs).
Approach:	They propose a LLaVA-based model fine-tuned with MathV360K to bridge this gap by collecting 40K high-quality images with question-answer pairs from 24 existing datasets and synthesizing 320K new pairs.
Outcome:	The proposed model improves the multimodal reasoning capabilities of LLaVA-1.5 and demonstrates enhanced generalizability on the MMMU benchmark.

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Wenxin Guo, Lei Zhang, Kun Zhang, Yi Liu, Zhendong Mao

Controllable LLM Reasoning via Sparse Autoencoder-Based Steering (2026.acl-long)

Challenge:	Existing approaches to image-text retrieval ignore semantic discrepancies caused by syntactic structure in natural language expressions and relationships among visual entities.
Approach:	They propose a visual-linguistic dependency encoder framework which explicitly models the dependency information among textual words and interaction patterns between image regions.
Outcome:	The proposed framework outperforms existing methods on a vision-linguistic compositional structure reasoning dataset.

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Yi Fang, Wenjie Wang, Mingfeng Xue, Boyi Deng, Fengli Xu, Dayiheng Liu, Fuli Feng

ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)

Challenge:	Existing methods struggle to control fine-grained reasoning strategies due to conceptual entanglement in LRMs’ hidden states.
Approach:	They propose to decompose strategy-entangled hidden states into a disentangled feature space by using Sparse Autoencoders to identify the few strategy-specific features from the vast pool of SAE features.
Outcome:	The proposed method outperforms existing methods by 15% in control effectiveness.

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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang

An Empirical Study on Multiple Information Sources for Zero-Shot Fine-Grained Entity Typing (2021.emnlp-main)

Challenge:	Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE).
Approach:	They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE.
Outcome:	The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency.

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Yi Chen, Haiyun Jiang, Lemao Liu, Shuming Shi, Chuang Fan, Min Yang, Ruifeng Xu

The Generation Gap: Exploring Age Bias in the Value Systems of Large Language Models (2024.emnlp-main)

Challenge:	Auxiliary information from multiple sources has been demonstrated to be effective in zero-shot fine-grained entity typing (ZFET) however, there is no comprehensive understanding of how to make better use of the existing information sources and how they affect the performance of ZFET.
Approach:	They propose a multi-source fusion model targeting auxiliary information from multiple sources to improve zero-shot fine-grained entity typing (ZFET)
Outcome:	The proposed model achieves 11.42% and 22.84% gains over state-of-the-art baselines on BBN and Wiki respectively with regard to macro F1 scores.

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Siyang Liu, Trisha Maturi, Bowen Yi, Siqi Shen, Rada Mihalcea

Feedback to Reasoning: LLM-Assisted Molecular Optimization with Domain Feedback and Historical Reasoning (2026.findings-acl)

Challenge:	Using the World Value Survey, we find a general inclination of LLM values towards younger demographics, especially when compared to the US population.
Approach:	They use data from the World Value Survey to examine the alignment of LLM values with specific age groups.
Outcome:	The proposed model can be used to predict the value of a large language model and to assess its performance on 13 categories.

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Wenhan Gao, Xiran Fan, Chin-Chia Michael Yeh, Jiarui Sun, Yuzhong Chen, Menghai Pan, Mahashweta Das, Yi Liu

A Comprehensive Study of Jailbreak Attack versus Defense for Large Language Models (2024.findings-acl)

Challenge:	Existing methods for molecular optimization do not leverage domain feedback and historical knowledge with reasoning traces and chemical insights.
Approach:	They propose a conversational molecular optimization pipeline that enables LLMs to accumulate and retrieve past actions, rationales, and feedback.
Outcome:	The proposed framework transforms LLMs from passive text generators into agentic experts that learn both actions and reasoning from experience.

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Zihao Xu, Yi Liu, Gelei Deng, Yuekang Li, Stjepan Picek

INDOORWORLD : Integrating Physical Task Solving and Social Simulation in A Heterogeneous Multi-Agent Environment (2025.findings-emnlp)

Challenge:	Large Language Models (LLMs) have demonstrated capabilities for generating content that could be deemed harmful.
Approach:	They conduct a comprehensive analysis of existing studies on jailbreaking LLMs and their defense techniques.
Outcome:	The proposed techniques underperform existing white-box attacks and include special tokens significantly affects the likelihood of successful attacks.

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Dekun Wu, Frederik Brudy, Bang Liu, Yi Wang

Challenge:	Existing virtual environments for LLM agent research focus on task solving or social simulation . existing environments for virtual environments lack physical grounding of social behaviors .
Approach:	They propose a virtual environment that tightly integrates physical and social dynamics . IndoorWorld is a heterogeneous multi-agent environment that integrates social and physical dynamics based on a simulation of physical environments .
Outcome:	The proposed environment integrates physical and social dynamics into a heterogeneous multi-agent environment.

COVID-19 Literature Knowledge Graph Construction and Drug Repurposing Report Generation (2021.naacl-demos)

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Qingyun Wang, Manling Li, Xuan Wang, Nikolaus Parulian, Guangxing Han, Jiawei Ma, Jingxuan Tu, Ying Lin, Ranran Haoran Zhang, Weili Liu, Aabhas Chauhan, Yingjun Guan, Bangzheng Li, Ruisong Li, Xiangchen Song, Yi Fung, Heng Ji, Jiawei Han, Shih-Fu Chang, James Pustejovsky, Jasmine Rah, David Liem, Ahmed ELsayed, Martha Palmer, Clare Voss, Cynthia Schneider, Boyan Onyshkevych

CADMate: Generating CAD Assembly Plan with Geometric Chain-of-Thought and Spatial Physical Rewards (2026.acl-long)

Challenge:	a new framework to digest relevant biomedical knowledge is needed to combat COVID-19 . quantity of research results is a bottleneck, and false information promoted in publications .
Approach:	a team of researchers has developed a framework to extract multimedia knowledge elements from scientific literature to combat COVID-19.
Outcome:	a new framework extracts fine-grained multimedia knowledge elements from scientific literature . it provides detailed contextual sentences, subfigures, and knowledge subgraphs as evidence . the framework is based on a case study of drug repurposing .

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Jiali Chen, DingBa Fu, Xusen Hei, Yuhang Liu, Yiyang Chen, Jiayuan Xie, Wenqi Fan, Yi Cai

TempCompass: Do Video LLMs Really Understand Videos? (2024.findings-acl)

Challenge:	Computer-aided design (CAD) is crucial in prototyping complex 3D objects . designers manually define assembly sequences for individual CAD parts .
Approach:	They propose a framework for computer-aided design that predicts actions for CAD parts . they use a reference design image and disassembled parts to generate 6-DoF transformations .
Outcome:	The proposed framework outperforms existing MLLMs in the design of CAD assemblies.

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Yuanxin Liu, Shicheng Li, Yi Liu, Yuxiang Wang, Shuhuai Ren, Lei Li, Sishuo Chen, Xu Sun, Lu Hou

Visual Data in NLP

Challenge:	Existing benchmarks on video large language models lack a comprehensive feedback on temporal perception ability . current models cannot distinguish between different temporal aspects and are limited in task formats .
Approach:	They propose a benchmark to evaluate temporal perception ability of video large language models . they construct conflicting videos that share the same static content but differ in a specific temporal aspect .
Outcome:	The proposed benchmarks show that video large language models exhibit poor temporal perception ability.

ParaLS: Lexical Substitution via Pretrained Paraphraser (2023.acl-long)

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Jipeng Qiang, Kang Liu, Yun Li, Yunhao Yuan, Yi Zhu

Eval-RAR: Evaluation-Driven Retrieval-Augmented Reasoning via Reinforcement Learning (2026.findings-acl)

Challenge:	Lexical substitution (LS) is an extremely powerful technology that can be used as a backbone of various NLP applications such as writing assistance.
Approach:	They propose two simple decoding strategies that focus on the variations of the target word during decoding to generate substitutes from a paraphraser.
Outcome:	The proposed methods outperform state-of-the-art LS methods based on pre-trained language models on three benchmarks.

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Heng Yu, Rui Li, Qi Liu, Wenjun Feng, Junfeng Kang, Yi Zhan

ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)

Challenge:	Existing methods for retrieval-augmented generation fail to provide explicit supervision for internal reasoning process.
Approach:	They propose an Evaluation-driven Retrieval-Augmented Reasoning framework that uses reinforcement learning and a fine-grained evaluation reward to optimize the process.
Outcome:	Eval-RAR outperforms existing methods on QA benchmarks on seven single-hop and multi-hop tasks.

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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo

Prefix-diffusion: A Lightweight Diffusion Model for Diverse Image Captioning (2024.lrec-main)

Challenge:	Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task.
Approach:	They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5.
Outcome:	The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers.

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Guisheng Liu, Yi Li, Zhengcong Fei, Haiyan Fu, Xiangyang Luo, Yanqing Guo

RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict (2024.lrec-main)

Challenge:	Existing image captioning models require large trainable parameters to bridge visual and textual representations.
Approach:	They propose a lightweight image captioning network in combination with continuous diffusion that injects prefix image embeddings into denoising process of diffusion model.
Outcome:	The proposed method generates diverse captions with relatively less parameters while maintaining fluency and relevance compared with other models.

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Yirong Zeng, Xiao Ding, Yi Zhao, Xiangyu Li, Jie Zhang, Chao Yao, Ting Liu, Bing Qin

DeepRTL2: A Versatile Model for RTL-Related Tasks (2025.findings-acl)

Challenge:	Existing methods to verify factuality of claims do not provide sufficient evidence for explainable fact-checking systems.
Approach:	They propose a method to automatically retrieve and summarize evidence from the Web and a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022.
Outcome:	The proposed method can retrieve and summarize evidence from the Web and generate explanations in 16 languages.

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Yi Liu, Hongji Zhang, Yunhao Zhou, Zhengyuan Shi, Changran Xu, Qiang Xu

Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation (2024.lrec-main)

Challenge:	Integration of large language models into electronic design automation has been a key driver in eDA.
Approach:	They propose a family of large language models that unifies generation- and embedding-based tasks related to RTL.
Outcome:	The proposed model achieves state-of-the-art performance across all evaluated tasks.

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Ge Gao, Jongin Kim, Sejin Paik, Ekaterina Novozhilova, Yi Liu, Sarah T. Bonna, Margrit Betke, Derry Tanti Wijaya

RelCLIP: Adapting Language-Image Pretraining for Visual Relationship Detection via Relational Contrastive Learning (2022.emnlp-main)

Challenge:	Existing methods for classifying discrete emotions from news headlines have been limited to using headlines.
Approach:	They propose to use people’s free-text explanations to classify emotions elicited by news headlines to generate emotion explanations from headlines.
Outcome:	The proposed method improves on methods that only use headlines and train a pretrained model for explanation generation.

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Yi Zhu, Zhaoqing Zhu, Bingqian Lin, Xiaodan Liang, Feng Zhao, Jianzhuang Liu

FaStFact: Faster, Stronger Long-Form Factuality Evaluations in LLMs (2025.findings-emnlp)

Challenge:	Existing visual relationship detection models only use numeric ids of relation labels for training, but ignore semantic correlation between labels.
Approach:	They propose a visual Relationship prediction framework that transfers natural language knowledge from Contrastive Language-Image Pre-training models to enhance the relationship prediction.
Outcome:	The proposed framework improves visual relationship prediction by matching semantic correlations with relation triplets.

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Yingjia Wan, Haochen Tan, Xiao Zhu, Xinyu Zhou, Zhiwei Li, Qingsong Lv, Changxuan Sun, Jiaqi Zeng, Yi Xu, Jianqiao Lu, Yinhong Liu, Zhijiang Guo

DeepGen: Diverse Search Ad Generation and Real-Time Customization (2022.emnlp-demos)

Challenge:	Prior evaluation pipelines fail to evaluate factuality of long-form LLMs due to inefficiency and costly human assessment.
Approach:	They propose a fast and strong evaluation pipeline that can evaluate factuality of long-form LLMs . they propose 'faStFact' to reduce cost of web searching and inference calling .
Outcome:	The proposed evaluation pipeline achieves highest alignment with human evaluation and efficiency among existing baselines.

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Konstantin Golobokov, Junyi Chai, Victor Ye Dong, Mandy Gu, Bingyu Chi, Jie Cao, Yulan Yan, Yi Liu

DAC: A Dynamic Attention-aware Approach for Task-Agnostic Prompt Compression (2025.acl-long)

Challenge:	Existing systems that generate ads manually are not effective in generating ad copy and generating millions of ads for large businesses.
Approach:	They propose a system that generates fluent ads from advertiser’s web pages in an abstractive fashion and solves practical issues such as factuality and inference speed.
Outcome:	The proposed system generates fluent ads from advertiser’s web pages in an abstractive fashion and solves practical issues such as factuality and inference speed.

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Yi Zhao, Zuchao Li, Hai Zhao, Baoyuan Qi, Liu Guoming

Knowledge-Guided Cross-Topic Visual Question Generation (2024.lrec-main)

Challenge:	Existing methods rely on information entropy as the metric to compress lexical units, but ignore attention-critical tokens and information . recent advent of In-Context Learning (ICL), Chain-of-Thought (CoT), and Retrieval Augmented Generation (RAG) technologies has significantly invigorated the landscape of applications based on Large Language Models (LLMs).
Approach:	They propose a dynamic attention-aware approach to task-agnostic prompt compression . they integrate entropy and attention information to achieve fine-grained prompt compression.
Outcome:	Experiments show that the proposed approach improves across tasks and LLMs.

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Hongfei Liu, Guohua Wang, Jiayuan Xie, Jiali Chen, Wenhao Fang, Yi Cai

Visual Data in NLP

Challenge:	Existing methods for visual question generation use answers or question types as constraints to generate questions.
Approach:	They propose a knowledge-guided cross-topic visual question generation task to generate unseen topics in cross-section scenarios.
Outcome:	The proposed model outperforms baselines and can generate unseen topic-related questions in cross-topic scenarios.

Uncertainty-Aware Iterative Preference Optimization for Enhanced LLM Reasoning (2025.acl-long)

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Lei Li, Hehuan Liu, Yaxin Zhou, ZhaoYang Gui, Xudong Weng, Yi Yuan, Zheng Wei, Zang Li

QuoteR: A Benchmark of Quote Recommendation for Writing (2022.acl-long)

Challenge:	Existing methods for enhancing the performance of large language models require expensive manual annotations.
Approach:	They propose an offline direct preference optimization method that collects preference pairs through iterative sampling and execution feedback to improve model confidence.
Outcome:	The proposed method improves performance on three reasoning tasks and shows a 3.6% improvement over the standard method.

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Fanchao Qi, Yanhui Yang, Jing Yi, Zhili Cheng, Zhiyuan Liu, Maosong Sun

Challenge:	Existing methods to recommend quotes are evaluated on unpublished datasets .
Approach:	They propose to build a dataset that is open and contains three parts including English, standard Chinese and classical Chinese.
Outcome:	The proposed model outperforms existing methods on all three parts of QuoteR.

PRA-RAG: Provably Robust Aggregation in Retrieval-Augmented Generation against Retrieval Corruption (2026.findings-acl)

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Xue Tan, Yi Zheng, Chang Huo, Yunruo Zhang, Yu Liu, Hao Luan, Zhuyang Yu, Jun Dai, Xiaoyan Sun, Ping Chen

Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation (2025.emnlp-main)

Challenge:	Existing defense mechanisms lack theoretical robustness guarantees and perform unreliably when the LLM has limited knowledge of the retrieved content.
Approach:	They propose a provably robust retrieval aggregation algorithm designed to defend against poisoning attacks on retrieved texts.
Outcome:	Experiments show that PRA-RAG reduces the attack success rate to as low as 1% while maintaining an accuracy of 71%, significantly outperforming representative state-of-the-art (SOTA) methods.

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Jiankun Zhang, Shenglai Zeng, Jie Ren, Tianqi Zheng, Hui Liu, Xianfeng Tang, Hui Liu, Yi Chang

PropaInsight: Toward Deeper Understanding of Propaganda in Terms of Techniques, Appeals, and Intent (2025.coling-main)

Challenge:	Large Multimodal Models (LMMs) can process text, images, and audio, but they introduce privacy vulnerabilities.
Approach:	They propose a compositional structured prompt attack to exploit MRAG privacy vulnerabilities . they show that LMMs can generate outputs resembling retrieved content .
Outcome:	The proposed approach generates outputs resembling retrieved content and exposes sensitive information.

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Jiateng Liu, Lin Ai, Zizhou Liu, Payam Karisani, Zheng Hui, Yi Fung, Preslav Nakov, Julia Hirschberg, Heng Ji

Sentiment Analysis

Challenge:	Existing research on propaganda detection does not capture the motives behind the content or its broader impact.
Approach:	They propose a framework that dissects propaganda into techniques, arousal appeals, and underlying intent.
Outcome:	The proposed framework improves performance in a wide range of scenarios and can be used to identify and categorize propaganda techniques.

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

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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

SAFETY-J: Evaluating Safety with Critique (2024.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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Yixiu Liu, Yuxiang Zheng, Shijie Xia, Jiajun Li, Yi Tu, Chaoling Song, Pengfei Liu

Challenge:	Current methods focus on binary safety classifications and lack detailed critique, limiting their utility for model improvement and user trust.
Approach:	They propose a bilingual generative safety evaluator for English and Chinese with critique-based judgment that utilizes a robust training dataset and augmented query-response pairs to assess safety across various scenarios comprehensively.
Outcome:	The proposed model improves safety evaluations by assessing the quality of critiques with minimal human intervention.

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

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

Negating Negatives: Alignment with Human Negative Samples via Distributional Dispreference Optimization (2024.findings-emnlp)

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

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Shitong Duan, Xiaoyuan Yi, Peng Zhang, Yan Liu, Zheng Liu, Tun Lu, Xing Xie, Ning Gu

Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation (2021.emnlp-main)

Challenge:	Existing methods to steer LLMs towards human preference suffer from noisy positive-negative training pairs.
Approach:	They propose a distributional preference optimization method which maximizes discrepancy between dispreferred responses and generated non-negative ones.
Outcome:	The proposed method achieves comparable generation quality and surpasses the latest strong baselines in producing less harmful and more informative responses with better training stability and faster convergence.

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Jingwei Yi, Fangzhao Wu, Chuhan Wu, Ruixuan Liu, Guangzhong Sun, Xing Xie

Improve Decoding Factuality by Token-wise Cross Layer Entropy of Large Language Models (2025.findings-naacl)

Challenge:	Existing news recommendation methods rely on centralized storage of user click behavior data, which may lead to privacy concerns and hazards.
Approach:	They propose a federated learning framework for privacy-preserving news recommendation . they propose aggregation of news representations and user model by a client .
Outcome:	The proposed framework reduces computation and communication cost on clients while keeping promising model performance.

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Jialiang Wu, Yi Shen, Sijia Liu, Yi Tang, Sen Song, Xiaoyi Wang, Longjun Cai

Causal-Debias: Unifying Debiasing in Pretrained Language Models and Fine-tuning via Causal Invariant Learning (2023.acl-long)

Challenge:	Large language models (LLMs) often struggle with the issue of generating inaccurate or fabricated content even when they possess correct knowledge.
Approach:	They propose a decoding method that mitigates hallucinations without extra training . they propose entropy eNhanced decoding that leverages inner probability changes .
Outcome:	The proposed method improves the truthfulness and informativeness of generation while maintaining robust QA accuracy.

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Fan Zhou, Yuzhou Mao, Liu Yu, Yi Yang, Ting Zhong

Chain-Talker: Chain Understanding and Rendering for Empathetic Conversational Speech Synthesis (2025.findings-acl)

Challenge:	Existing methods to remove unwanted stereotypical associations from pretrained language models (PLMs) are often focused on removing unwanted stereotypes from PLMs.
Approach:	They propose a framework to remove unwanted stereotypical associations in pretrained language models . they propose bias-relevant factors are causal, while labelrelevant factors causal .
Outcome:	The proposed framework reduces stereotypical associations after PLMs are fine-tuned . the proposed framework mitigates bias from a causal invariant perspective .

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Yifan Hu, Rui Liu, Yi Ren, Xiang Yin, Haizhou Li

ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation (2024.findings-emnlp)

Challenge:	Current generative CSS models face interpretability limitations due to insufficient emotional perception and redundant discrete speech coding.
Approach:	They propose a framework that aligns synthesized speech with the emotional context of user-agent interactions to achieve empathy.
Outcome:	The proposed framework produces more expressive speech than existing methods on three datasets.

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Wenjun Hou, Yi Cheng, Kaishuai Xu, Yan Hu, Wenjie Li, Jiang Liu

Mitigating Biases in Language Models via Bias Unlearning (2025.emnlp-main)

Challenge:	Existing approaches to radiology report generation lack inter-report consistency, exhibiting biases towards common patterns and susceptibility to lesion variants.
Approach:	They propose a method which improves the inter-report consistency of radiology report generation by extracting lesions from input images and examining their characteristics.
Outcome:	The proposed system captures similarities in semantically equivalent lesions and can be used to generate reports for two semantically identical cases.

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Dianqing Liu, Yi Liu, Guoqing Jin, Zhendong Mao

Pretrain-KGE: Learning Knowledge Representation from Pretrained Language Models (2020.findings-emnlp)

Challenge:	Recent debiasing approaches target different demographic groups, harming fairness and discrimination.
Approach:	They propose a model debiasing framework which targets stereotypes by unlearning stereotype forgetting and anti-stereotype retention.
Outcome:	The proposed framework outperforms existing methods in mitigating bias while retaining language modeling capabilities.

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Zhiyuan Zhang, Xiaoqian Liu, Yi Zhang, Qi Su, Xu Sun, Bin He

X-Boundary: Establishing Exact Safety Boundary to Shield LLMs from Jailbreak Attacks without Compromising Usability (2025.findings-emnlp)

Challenge:	Existing knowledge graph embedding models suffer from limited knowledge representation due to sparse and noisy dataset annotations.
Approach:	They propose to use pretrained language models to enhance knowledge representation by leveraging world knowledge from pretrained models.
Outcome:	Extensive experiments show that the proposed framework can improve results over existing models.

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Xiaoya Lu, Dongrui Liu, Yi Yu, Luxin Xu, Jing Shao

A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation (2022.acl-long)

Challenge:	Existing methods for enhancing LLM security compromise usability, study finds . boundary-safe representations close to harmful representations are disrupted, resulting in usability decline .
Approach:	They propose a method to push harmful representations away from boundary-safe representations and obtain an exact distinction boundary.
Outcome:	The proposed method reduces over-refusal rate and maintains general capability . it pushes harmful representations away from boundary-safe representations, thereby reducing usability.

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Tianyu Liu, Yizhe Zhang, Chris Brockett, Yi Mao, Zhifang Sui, Weizhu Chen, Bill Dolan

TISE: A Tripartite In-context Selection Method for Event Argument Extraction (2024.naacl-long)

Challenge:	Existing work on pre-trained generative models often fails to detect non-existent or incorrect content . Existing studies have attempted to detect hallucinations based on oracle references .
Approach:	They propose a token-level, reference-free hallucination detection task based on Wikipedia annotations to detect non-existent or incorrect content.
Outcome:	The proposed task is token-level, reference-free hallucination detection task and dataset . authors argue that the proposed task can be used in real-time to detect hallucines .

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Yanhe Fu, Yanan Cao, Qingyue Wang, Yi Liu

TARN-VIST: Topic Aware Reinforcement Network for Visual Storytelling (2024.lrec-main)

Challenge:	Recent studies show that LLMs can finish inference by providing several examples.
Approach:	They propose a method which integrates three requirements when selecting an in-context example and integrates them into a set of determinantal point processes to enhance the reasoning capabilities of LLMs.
Outcome:	The proposed method can achieve superior performance with fewer examples and outperform some supervised methods.

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Weiran Chen, Xin Li, Jiaqi Su, Guiqian Zhu, Ying Li, Yi Ji, Chunping Liu

Libra-VLA: Achieving Learning Equilibrium via Asynchronous Coarse-to-Fine Dual-System (2026.acl-long)

Challenge:	Existing methods for visual storytelling ignore latent topic information.
Approach:	They propose a topic-aware reinforcement network for VIsual StoryTelling that takes topic information into account to generate a coherent story.
Outcome:	The proposed method outperforms most of the competing models across multiple evaluation metrics.

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Yifei Wei, Linqing Zhong, Yi Liu, Yuxiang Lu, Xindong He, Maoqing Yao, Guanghui Ren

Challenge:	Vision-Language-Action models ground high-level semantic instructions into executable physical actions.
Approach:	They propose a Coarse-to-Fine Dual-System VLA architecture that decouples learning complexity into a coarse-to fine hierarchy while leveraging structural modularity to implement an asynchronous execution strategy.
Outcome:	The proposed architecture decouples learning complexity into a coarse-to-fine hierarchy while leveraging structural modularity to implement an asynchronous execution strategy.

Your Semantic-Independent Watermark is Fragile: A Semantic Perturbation Attack against EaaS Watermark (2025.findings-emnlp)

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Zekun Fei, Biao Yi, Jianing Geng, He Ruiqi, Lihai Nie, Zheli Liu

MC-indexing: Effective Long Document Retrieval via Multi-view Content-aware Indexing (2024.findings-emnlp)

Challenge:	Embedding-as-a-Service (EaaS) is a successful business pattern but faces significant challenges related to various forms of copyright infringement.
Approach:	They propose a semantic-independent watermarking scheme that exploits semantic perturbation tests to bypass verification.
Outcome:	The proposed watermarking schemes possess semantic-independent characteristics and exploit semantic perturbation tests to bypass verification.

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Kuicai Dong, Derrick Goh Xin Deik, Yi Lee, Hao Zhang, Xiangyang Li, Cong Zhang, Yong Liu

SynPrompt: Syntax-aware Enhanced Prompt Engineering for Aspect-based Sentiment Analysis (2024.lrec-main)

Challenge:	Existing methods for document question answering do not consider content structures, resulting chunks exclude vital information or include irrelevant content.
Approach:	They propose a method that segments document into content chunks and represents each content chunk in raw-text, keywords, and summary views.
Outcome:	The proposed method significantly improves recall of long document question answering datasets compared to state-of-the-art chunking schemes.

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Wen Yin, Cencen Liu, Yi Xu, Ahmad Raza Wahla, Huang Yiting, Dezhang Zheng

CLIO: Role-interactive Multi-event Head Attention Network for Document-level Event Extraction (2022.coling-1)

Challenge:	Existing methods of prompt-tuning for Aspect-based Sentiment Analysis (ABSA) are crude and simple.
Approach:	They propose a Syntax-aware Enhanced Prompt method which mines syntactic information related to aspect words from the syntaktic dependency tree.
Outcome:	The proposed method exploits the syntactic knowledge embedded in PLMs and achieves favorable results on three benchmark datasets.

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Yubing Ren, Yanan Cao, Fang Fang, Ping Guo, Zheng Lin, Wei Ma, Yi Liu

ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model (2025.emnlp-main)

Challenge:	Existing methods for document-level event extraction struggle due to two intrinsic challenges: nested arguments and multiple events.
Approach:	They propose a role-interactive multi-event head attention network to solve two challenges . they map different events to multiple subspaces and then determine whether the current event exists .
Outcome:	The proposed model improves on two widely used DEE datasets on the Internet.

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Zhongyi Zhou, Yichen Zhu, Minjie Zhu, Junjie Wen, Ning Liu, Zhiyuan Xu, Weibin Meng, Yaxin Peng, Chaomin Shen, Feifei Feng, Yi Xu

SeDev: Structured Semantic Exploration for LLM-Driven Code Generation (2026.acl-long)

Challenge:	Recent advances in vision-language-action models prioritize robotic action mastery . however, models trained on visual-text pairs struggle to interpret multimodal data .
Approach:	They propose a framework that integrates multimodal data after initial control mastery and a Mixture-of-Experts architecture to minimize task interference.
Outcome:	The proposed framework surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks and achieves six times higher performance on visual question-answering datasets.

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Ronghui Yang, Jie Liu, Jiajie Zeng, Jiexin Wang, Jiuchuan Jiang, Bo An, Yi Cai, Mengchen Zhao

Programming Languages in NLP

Challenge:	Large Language Models (LLMs) have shown remarkable capabilities in automating code generation, but they suffer from insufficient exploration of the vast solution space.
Approach:	They propose a large-scale LLM-driven code generation framework that efficiently finds high-quality solutions in only a few iterations.
Outcome:	The proposed framework outperforms baselines while maintaining reasonable time and computational costs.

Sculpting the Vector Space: Towards Efficient Multi-Vector Visual Document Retrieval via Prune-then-Merge Framework (2026.findings-acl)

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Yibo Yan, Mingdong Ou, Yi Cao, Xin Zou, Jiahao Huo, Shuliang Liu, James Kwok, Xuming Hu

Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Human-LLM Dialogue (2026.findings-acl)

Challenge:	Visual Document Retrieval (VDR) is of importance in multimodal retrieval applications.
Approach:	They propose a two-stage pruning and merging frameworks that combine pruning and merge techniques to achieve higher compression rates.
Outcome:	The proposed framework outperforms existing methods on 29 visual document retrieval datasets.

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Jonathan Ivey, Shivani Kumar, Jiayu Liu, Hua Shen, Sushrita Rakshit, Rohan Raju, Haotian Zhang, Aparna Ananthasubramaniam, Junghwan Kim, Bowen Yi, Dustin Wright, Abraham Israeli, Anders Giovanni Møller, Lechen Zhang, David Jurgens

Attention Basin: Why Contextual Position Matters in Large Language Models (2026.acl-long)

Challenge:	Recent work has sought to use large language models to simulate human-human and human-LLM interactions.
Approach:	They use a large-scale dataset to generate a paired LLM-LLM and human-LLm dialogues from the WildChat dataset and quantify how well they align with their human counterparts.
Outcome:	The proposed models perform similarly in simulating English, Chinese, and Russian dialogues.

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Zihao Yi, Zhenqing Ling, Delong Zeng, Haohao Luo, Zhe Xu, Wei Liu, Jian Luan, Wanxia Cao, Ying Shen

Domain-Specific Data Generation Framework for RAG Adaptation (2026.findings-acl)

Challenge:	Large Language Models (LLMs) are sensitive to the contextual position of information in input.
Approach:	They introduce Attention-Driven Reranking (AttnRank) which estimates a model’s intrinsic positional attention preferences using a small calibration set and reorders retrieved documents or few-shot examples to align the most salient content with these high-attention positions.
Outcome:	Experiments on multi-hop QA and few-shot in-context learning tasks show that AttnRank achieves substantial improvements across 10 large language models of varying architectures and scales, without modifying model parameters or training procedures.

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Chris Xing Tian, Weihao Xie, Zhen Chen, Hui Liu, Zhengyuan Yi, Haoliang Li, Shiqi Wang, Siwei Ma

Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction (2021.naacl-main)

Challenge:	Retrieval-Augmented Generation (RAG) combines the language understanding and reasoning capabilities of large language models (LLMs) with external retrieval to produce domain-grounded responses.
Approach:	They propose a scalable and modular data-centric framework for generating domain-grounded question–answer–context triples tailored to diverse RAG adaptation strategies.
Outcome:	The proposed framework generates domain-grounded question–answer–context triples for multiple RAG adaptation strategies.

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Zhenghao Liu, Xiaoyuan Yi, Maosong Sun, Liner Yang, Tat-Seng Chua

Syntactic Text Processing

Challenge:	Existing GEC models produce spurious corrections or fail to detect lots of errors.
Approach:	They propose a neural network for GEC quality estimation with multiple hypotheses . VERNet establishes interactions among hypothese based on reasoning graph .
Outcome:	The proposed model achieves state-of-the-art grammatical error detection performance and best quality estimation results on four GEC datasets.

SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training (2024.acl-long)

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Nan He, Weichen Xiong, Hanwen Liu, Yi Liao, Lei Ding, Kai Zhang, Guohua Tang, Xiao Han, Yang Wei

AlphaEdit+: Model Editing in the Presence of Conflicting and Inconsistent Knowledge (2026.findings-acl)

Challenge:	Current methods focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication.
Approach:	They propose a method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness.
Outcome:	The proposed method significantly improves training efficiency on deduplicated datasets and improves downstream accuracy by 1.77%.

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Qing Liu, Jianhao Zhang, Ou Wu, Michael Ng, Yi Du

Challenge:	Existing methods for knowledge editing struggle with knowledge conflicts and inconsistencies.
Approach:	They propose a new method for knowledge editing that relaxes null-space constraints and introduces a weighting scheme to mitigate conflicts between new and historical knowledge.
Outcome:	The proposed method outperforms existing methods on challenging datasets and outperformed existing methods.

Towards Dynamic Theory of Mind: Evaluating LLM Adaptation to Temporal Evolution of Human States (2025.acl-long)

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Yang Xiao, Jiashuo Wang, Qiancheng Xu, Changhe Song, Chunpu Xu, Yi Cheng, Wenjie Li, Pengfei Liu

Linguistics & Cognitive NLP

Challenge:	Existing benchmarks assess basic Theory of Mind abilities but neglect temporal evolution of mental states in real-world social contexts.
Approach:	They propose a benchmark specifically designed to evaluate Large Language Models' ability to understand and track the temporal progression of mental states across interconnected scenarios.
Outcome:	The proposed benchmarks underperform humans by 44.7% and show that they can model the dynamic nature of human mental states better than existing models.

SPPD: Self-training with Process Preference Learning Using Dynamic Value Margin (2025.findings-emnlp)

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Hao Yi, Qingyang Li, Yulan Hu, Fuzheng Zhang, Di Zhang, Yong Liu

Numerical Reasoning

Challenge:	Existing approaches to improve numerical and logical reasoning of Large Language Models are limited . existing approaches rely on prompt engineering and pretrained knowledge to ensure correctness .
Approach:	They propose to train LLMs with process-based reasoning using a dynamic value margin . they use the Bellman optimality equation to derive a value margin for step-level preference optimization .
Outcome:	The proposed method is equivalent to on-policy policy gradient methods under constrained reward functions.

DAST: Difficulty-Adaptive Slow-Thinking for Large Reasoning Models (2025.emnlp-industry)

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Yi Shen, Jian Zhang, Jieyun Huang, Shuming Shi, Wenjing Zhang, Jiangze Yan, Ning Wang, Kai Wang, Zhaoxiang Liu, Shiguo Lian

Supervised Optimism Correction: Be Confident When LLMs Are Sure (2025.findings-acl)

Challenge:	Recent advances in slow-thinking reasoning models have shown exceptional performance in complex reasoning tasks.
Approach:	They propose a framework that enables models to automatically adjust Chain-of-Thought (CoT) length based on problem difficulty.
Outcome:	The proposed framework penalizes inefficiency on simple problems while incentivizing deep reasoning for complex ones.

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Junjie Zhang, Rushuai Yang, Shunyu Liu, Ting-En Lin, Fei Huang, Yi Chen, Yongbin Li, Dacheng Tao

E-ViC: Reasoning Beyond Text via Embodied Visual Chain for Spatial Intelligence (2026.acl-long)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated remarkable success across diverse tasks such as instruction following, code generation, and medical diagnosis.
Approach:	They propose a supervised fine-tuning-based auxiliary loss for Q-value estimations during supervised refinement.
Outcome:	The proposed method outperforms beam search on GSM8K, MATH, and GAOKAO on reasoning benchmarks.

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Junbo Qi, Yi Zhang, Hanchu Ni, Che Liu, Zhimin Yao, Ruilin Yang, Xiancong Ren, Liangjian Wen, Wei Ge, Yuya Ieiri, Osamu Yoshie, Yong Dai, Xiaozhu Ju

PARA: Parameter-Efficient Fine-tuning with Prompt-Aware Representation Adjustment (2024.emnlp-industry)

Challenge:	Existing Vision-Language Models (VLMs) lack spatial reasoning, despite text-based CoTs . e-ViC reframes spatial intelligence as a verifiable, tool-using capability, argues a new study.
Approach:	They propose a framework that moves reasoning beyond text into the visual domain . they ground reasoning in pixel-level interactions to enable human-like "look-and-confirm" strategies .
Outcome:	The proposed framework outperforms existing Vision-Language Models with an average gain of 10.1%.

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Zequan Liu, Yi Zhao, Ming Tan, Wei Zhu, Aaron Tian

Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study (2023.emnlp-main)

Challenge:	Existing methods for parameter-efficient fine-tuning excel in the context of single-backbone multi-tenant applications.
Approach:	They propose to integrate a lightweight vector generator within each Transformer layer to improve prompt-aware representation adjustment.
Outcome:	The proposed method surpasses current benchmarks in terms of performance despite having a similar number of adjustable parameters.

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Boxin Wang, Wei Ping, Peng Xu, Lawrence McAfee, Zihan Liu, Mohammad Shoeybi, Yi Dong, Oleksii Kuchaiev, Bo Li, Chaowei Xiao, Anima Anandkumar, Bryan Catanzaro

Walk in Others’ Shoes with a Single Glance: Human-Centric Visual Grounding with Top-View Perspective Transformation (2025.acl-long)

Challenge:	a recent study shows that retrieval-augmented LMs can improve text generation quality and accuracy.
Approach:	They propose a model that reproduces RETRO parameters while retrieving a text corpus . they find RETRO outperforms GPT on text generation with less repetition .
Outcome:	The proposed model outperforms standard retrieval-augmented GPT and retrieval augmented GTP on text generation and accuracy tasks.

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Yuqi Bu, Xin Wu, Zirui Zhao, Yi Cai, David Hsu, Qiong Liu

Cognitive Alpha Mining via LLM-Driven Code-Based Evolution (2026.acl-long)

Challenge:	Existing VLMs are insensitive to information differences induced by slight perspective changes.
Approach:	They propose a visual perspective-taking task that requires robots to interpret human-centric instructions and identify corresponding objects from robot perspectives.
Outcome:	The proposed method improves performance by up to 18% and generalizes effectively to robotic and dynamic scenarios.

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Fengyuan Liu, Yi Huang, Sichun Luo, Yuqi Wang, Yazheng Yang, Xinye Li, Zefa Hu, Junlan Feng, Qi Liu

Learning to Align Multi-Faceted Evaluation: A Unified and Robust Framework (2025.findings-acl)

Challenge:	Existing approaches to finding effective predictive signals from financial data are limited by their complexity and low signal-to-noise ratio.
Approach:	They propose a framework that combines code-level alpha representation with LLM-driven reasoning and evolutionary search.
Outcome:	The proposed framework combines code-level alpha representation with LLM-driven reasoning and evolutionary search.

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Kaishuai Xu, Tiezheng Yu, Yi Cheng, Wenjun Hou, Liangyou Li, Xin Jiang, Lifeng Shang, Qun Liu, Wenjie Li

CMIG: Conceptual Metaphor Theory-Inspired Framework for Metaphorical Image Generation (2026.findings-acl)

Challenge:	Existing methods for fine-tuning open-source LLMs are limited to text-based analysis under predefined general criteria.
Approach:	They propose a framework that fine-tunes LLMs to replicate the evaluation explanations and judgments of proprietary models.
Outcome:	The proposed evaluation framework outperforms existing fine-tuned evaluation methods in effectiveness and robustness.

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Qingbao Huang, Cheng Yang, Jiawei Yao, Zhiyue Liu, Yi Cai, Xingmao Zhang

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

Challenge:	Existing text-to-image systems often produce visually plausible but semantically literal outputs.
Approach:	They propose a structured prompting framework inspired by Conceptual Metaphor Theory . they propose to identify source–target mappings, filter projectable source attributes and select a visual realization strategy in a reproducible reasoning workflow.
Outcome:	The proposed framework improves semantic alignment and controllability on metaphor prompts.

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

Improving Embedding-based Unsupervised Keyphrase Extraction by Incorporating Structural Information (2023.findings-acl)

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

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Mingyang Song, Huafeng Liu, Yi Feng, Liping Jing

Beyond Surface Similarity: Detecting Subtle Semantic Shifts in Financial Narratives (2024.findings-naacl)

Challenge:	Existing unsupervised keyphrase extraction models ignore the indicative role of the highlights in certain locations, leading to wrong keyphrases extraction.
Approach:	They propose a Highlight-Guided Unsupervised Keyphrase Extraction model that models phrase-document relevance via the highlights of documents and calculates cross-phrase relevance between all candidate phrases.
Outcome:	The proposed model outperforms the state-of-the-art unsupervised keyphrase extraction models on three benchmarks.

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Jiaxin Liu, Yi Yang, Kar Yan Tam

Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models (2024.findings-naacl)

Challenge:	Existing pretrained embeddings and LLM embeddables fail to discern subtle financial narrative shifts, resulting in a lack of insight for investors and regulators.
Approach:	They propose a financial domain-specific NLP task to measure nuanced semantic similarity between pairs of financial narratives.
Outcome:	The proposed method outperforms existing methods trained on classic STS tasks and generic LLM embeddings on a human-annotated dataset.

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Wei He, Shichun Liu, Jun Zhao, Yiwen Ding, Yi Lu, Zhiheng Xi, Tao Gui, Qi Zhang, Xuanjing Huang

A Study of Syntactic Multi-Modality in Non-Autoregressive Machine Translation (2022.naacl-main)

Challenge:	Existing methods that rely on limited demos and out-of-demonstration (OOD) queries fail when faced with out- of-demotion queries.
Approach:	They propose a query-aware prompting method that elicits the inherent generalizability of large language models by query-based demo generation.
Outcome:	The proposed method outperforms state-of-the-art methods in the OOD setting and two public math benchmarks.

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Kexun Zhang, Rui Wang, Xu Tan, Junliang Guo, Yi Ren, Tao Qin, Tie-Yan Liu

Multilinguality

Challenge:	Non-autoregressive translation models suffer from the multi-modality problem when a source sentence corresponds to multiple correct translations.
Approach:	They propose to decompose the syntactic multi-modality problem into short- and long-range models and evaluate them on synthesized and real datasets.
Outcome:	The proposed loss functions can handle short- and long-range syntactic multi-modalities better than existing models.

Visual-Textual Alignment for Graph Inference in Visual Dialog (2020.coling-main)

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Tianling Jiang, Yi Ji, Chunping Liu, Hailin Shao

TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models (2021.acl-long)

Challenge:	Existing approaches to visual dialog do not understand semantic dependencies between visual and textual contents.
Approach:	They propose a Visual-Textual Alignment for Graph Inference network that makes up the lack of structural inference in visual dialog.
Outcome:	The proposed model outperforms existing models on a VisDial dataset.

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Jie He, Bo Peng, Yi Liao, Qun Liu, Deyi Xiong

CLAPSpeech: Learning Prosody from Text Context with Contrastive Language-Audio Pre-Training (2023.acl-long)

Challenge:	Using pretrained language models, we propose an error-annotated dataset for text generation . we use carefully selected prompt words to guide GPT-2 to generate candidate sentences .
Approach:	They propose an error-annotated dataset with multiple benchmark tasks for text generation from pretrained language models.
Outcome:	The proposed dataset covers 24 types of errors according to common sense and linguistics.

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Zhenhui Ye, Rongjie Huang, Yi Ren, Ziyue Jiang, Jinglin Liu, Jinzheng He, Xiang Yin, Zhou Zhao

EAVE: Efficient Product Attribute Value Extraction via Lightweight Sparse-layer Interaction (2024.findings-emnlp)

Challenge:	Existing methods for expressive text-to-speech only implicitly learn prosody with masked token reconstruction tasks.
Approach:	They propose a cross-modal contrastive pre-training framework that learns from prosody variance of the same text token under different contexts.
Outcome:	The proposed framework can learn from prosody variance of a text token under different contexts.

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Li Yang, Qifan Wang, Jianfeng Chi, Jiahao Liu, Jingang Wang, Fuli Feng, Zenglin Xu, Yi Fang, Lifu Huang, Dongfang Liu

Towards Better Entity Linking with Multi-View Enhanced Distillation (2023.acl-long)

Challenge:	Existing methods to extract product attribute value require multiple extractions to obtain all corresponding values.
Approach:	They propose an Efficient product Attribute Value Extraction approach using lightweight sparse-layer interaction.
Outcome:	The proposed method achieves significant efficiency gains with neutral or marginal loss in performance when the context is long and number of attributes is large.

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Yi Liu, Yuan Tian, Jianxun Lian, Xinlong Wang, Yanan Cao, Fang Fang, Wen Zhang, Haizhen Huang, Weiwei Deng, Qi Zhang

A Critical Analysis of Document Out-of-Distribution Detection (2023.findings-emnlp)

Challenge:	Entity linking is a fundamental task in Natural Language Processing (NLP), connecting mentions within unstructured contexts to their corresponding entities in a Knowledge Base (KB).
Approach:	They propose a dual-encoder framework that can efficiently match mentions to two-encoding frameworks by a global-view.
Outcome:	The proposed framework achieves state-of-the-art on several entity linking benchmarks.

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Jiuxiang Gu, Yifei Ming, Yi Zhou, Jason Kuen, Vlad Morariu, Handong Zhao, Ruiyi Zhang, Nikolaos Barmpalios, Anqi Liu, Yixuan Li, Tong Sun, Ani Nenkova

GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning (2026.findings-acl)

Challenge:	Existing document understanding models focus on single-modal inputs such as images or texts.
Approach:	They propose to use a spatial-aware adapter to adapt transformer-based language models to document domain to exploit multi-modal information.
Outcome:	The proposed model significantly improves the OOD detection performance compared to using a standard language model and to competitive baselines.

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Yi Chen, Yuying Ge, Rui Wang, Yixiao Ge, Junhao Cheng, Ying Shan, Xihui Liu

Learning the Beauty in Songs: Neural Singing Voice Beautifier (2022.acl-long)

Challenge:	Recent reinforcement learning approaches have advanced reasoning in Large Language Models (LLMs), yet their adaptation to multimodal LLMs remains underexplored.
Approach:	They propose a reinforcement learning framework that eliminates KL penalties and rewards consistency . they propose GRPO-CARE, which outperforms standard GR PO, with a base reward for accuracy and an adaptive bonus for consistency.
Outcome:	The proposed framework outperforms standard GRPO on the most difficult evaluation level and reasoning consistency test benchmarks.

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Jinglin Liu, Chengxi Li, Yi Ren, Zhiying Zhu, Zhou Zhao

Speech & Audio in NLP

Challenge:	Existing techniques for pitch correction are limited to intonation but ignore the overall aesthetic quality.
Approach:	They propose a novel time-warping approach for pitch correction to synchronize the amateur recording with the template pitch curve.
Outcome:	The proposed model improves intonation and vocal tone while keeping content and vocal timbre.

FlipGuard: Defending Preference Alignment against Update Regression with Constrained Optimization (2024.emnlp-main)

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Mingye Zhu, Yi Liu, Quan Wang, Junbo Guo, Zhendong Mao

Challenge:	Recent advances in preference alignment have significantly improved Large Language Models' ability to generate texts that align with human preferences and values.
Approach:	They propose a constrained optimization approach to detect and mitigate update regression with focal attention.
Outcome:	The proposed approach detects and mitigates update regression with focal attention while maintaining excellent overall performance.

Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020.acl-main)

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Xu Han, Yi Dai, Tianyu Gao, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou

Challenge:	Existing methods to learn incessantly emerging novel relations are overfitting the few memorized examples of old relations, causing confusion among existing relations.
Approach:	They introduce episodic memory activation and reconsolidation (EMAR) to continual relation learning.
Outcome:	The proposed method outperforms state-of-the-art models in catastrophic forgetting old relations.

Learning Query Adaptive Anchor Representation for Inductive Relation Prediction (2023.findings-acl)

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Zhiwen Xie, Yi Zhang, Jin Liu, Guangyou Zhou, Jimmy Huang

TablePilot: Recommending Human-Preferred Tabular Data Analysis with Large Language Models (2025.acl-industry)

Challenge:	Existing methods to infer the missing links between entities are limited to the transductive setting . Query Adaptive Anchor Representation (QAAR) model is based on entity-independent features .
Approach:	They propose a query adaptive anchor representation model which extracts one opening subgraph and performs reasoning by one time for all candidate triples.
Outcome:	The proposed model outperforms state-of-the-art models in relation prediction task.

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Deyin Yi, Yihao Liu, Lang Cao, Mengyu Zhou, Haoyu Dong, Shi Han, Dongmei Zhang

Perturbation-driven Dual Auxiliary Contrastive Learning for Collaborative Filtering Recommendation (2025.coling-main)

Challenge:	Tabular data analysis is crucial in many scenarios, yet its complexity and density can make it challenging to determine the most appropriate analysis operations for a new table.
Approach:	They propose a tabular data analysis framework that recommends query-code-result triplets for new tables . they propose Rec-Align, a method to further improve recommendation quality .
Outcome:	The proposed framework achieves 77.0% top-5 recommendation recall on a dataset designed for tabular data analysis recommendation.

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Caihong Mu, Keyang Zhang, Jialiang Zhou, Yi Liu

OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)

Challenge:	Existing contrastive learning-based methods struggle with data sparsity in real-world recommendations . Graph collaborative filtering incorporates contrastive training as an auxiliary task to improve performance .
Approach:	They propose a perturbation-driven dual auxiliary contrastive learning task for collaborative filtering . structure perturbation and weight perturbation are used to construct two graphs .
Outcome:	The proposed model outperforms benchmark models on multiple public datasets.

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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou

AV-TranSpeech: Audio-Visual Robust Speech-to-Speech Translation (2023.acl-long)

Challenge:	a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say .
Approach:	They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible .
Outcome:	The proposed framework achieves state-of-the-art performance among open-source projects.

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Rongjie Huang, Huadai Liu, Xize Cheng, Yi Ren, Linjun Li, Zhenhui Ye, Jinzheng He, Lichao Zhang, Jinglin Liu, Xiang Yin, Zhou Zhao

Do Code Semantics Help? A Comprehensive Study on Execution Trace-Based Information for Code Large Language Models (2025.findings-emnlp)

Challenge:	Existing models for speech-to-speech translation suffer from distinct degradation in noisy environments and fail to translate visual speech.
Approach:	They propose a text-based audio-visual speech-to-speech translation model that integrates visual information with audio-only data to improve system robustness.
Outcome:	The proposed model outperforms models trained on audio-only corpus in two languages . it also improves with low-resource audio-visual data, compared with baselines .

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Jian Jornbowrl Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Yi Li

An Empirical Study of LLM Reasoning Ability Under Strict Output Length Constraint (2025.emnlp-main)

Challenge:	Code Large Language Models have limited ability to reason about runtime behavior and understand functionality . authors present a generic framework to support integrating semantic information to code task-relevant prompts .
Approach:	a study examines the role of trace-based semantic information in boosting supervised fine-tuning and post-phase inference of Code LLMs.
Outcome:	a new framework integrates semantic information to code task-relevant prompts . the proposed framework shows that trace-based semantic information boosts reasoning ability .

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Yi Sun, Han Wang, Jiaqiang Li, Jiacheng Liu, Xiangyu Li, Hao Wen, Yizhen Yuan, Huiwen Zheng, Yan Liang, Yuanchun Li, Yunxin Liu

Low-Resource NLP

Challenge:	Large Language Models (LLMs) are a powerful tool for test-time scaling, but they are often used under time constraints.
Approach:	They propose to use LLMs to make models think before answering questions . they also use self-correction and best-of-N decoding to encourage deeper thinking .
Outcome:	The proposed models are able to achieve higher inference accuracy with extra inference computation under time constraints.

Avoiding Knowledge Edit Skipping in Multi-hop Question Answering with Guided Decomposition (2025.findings-emnlp)

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Yi Liu, Xiangrong Zhu, Xiangyu Liu, Wei Wei, Wei Hu

Different Tunes Played with Equal Skill: Exploring a Unified Optimization Subspace for Parameter-Efficient Tuning (2022.findings-emnlp)

Challenge:	Existing methods for knowledge editing fail to work in multi-hop question answering due to 'edit skipping' edit skipping occurs due to the mismatch between the granularity of LLMs in problem-solving and the facts in the edited memory.
Approach:	They propose a retrieval-augmented generation-based method that edits knowledge without modifying parameters without retraining LLMs.
Outcome:	The proposed method outperforms state-of-the-art methods for KE in multi-hop question answering.

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Jing Yi, Weize Chen, Yujia Qin, Yankai Lin, Ning Ding, Xu Han, Zhiyuan Liu, Maosong Sun, Jie Zhou

Nullspace Disentanglement for Red Teaming Language Models (2025.emnlp-main)

Challenge:	Existing delta tuning algorithms freeze most of the parameters and only optimize minimal adaptive parameters.
Approach:	They propose to decompose DETs into a unified optimization subspace and conduct optimization within the subspace.
Outcome:	The proposed DETs achieve comparable performance to the original DET and can be transferred to another DET with non-trivial performance.

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Yi Han, Yuanxing Liu, Weinan Zhang, Ting Liu

Feasible is Not Enough: Cost-Aware Optimal Tool-Chain Planning on Multi-Solution Tool Graphs (2026.findings-acl)

Challenge:	Existing work mainly leverages crowd workers to construct test cases.
Approach:	They propose a black-box approach that exploits the unique properties of the nullspace to disentangle and regulate the crucial success information within test cases.
Outcome:	The proposed approach outperforms baseline methods regarding the attack success rate and excels in aspects of diversity and fluency.

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Liangliang Liu, Yanming Li, Yigang Liu, Jialong Han, Rujia Shen, Yi Guan, Yi Lin, Jingchi Jiang

Equal Truth: Rumor Detection with Invariant Group Fairness (2025.findings-emnlp)

Challenge:	Existing tools and benchmarks often form tool learning (TL) as a single-solution setting . exploring large-scale TG is computationally expensive, especially under constrained context budgets.
Approach:	They propose a framework for learning optimal TL policies over large tool graphs . they train a reinforcement learning agent to acquire transferable expansion skills .
Outcome:	The proposed framework improves task success and solution optimality by 46.21% and 66.34% on multiSoTLBench.

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Junyi Chen, Mengjia Wu, Qian Liu, Jing Sun, Ying Ding, Yi Zhang

AlphaQT-Bench: Diagnosing the Gap between Financial Code Generation and Quantitative Reasoning in LLMs (2026.findings-acl)

Challenge:	Existing rumor detection methods rarely consider fairness issues inherent in the model . this can lead to biased predictions across stakeholder groups, undermining their detection effectiveness .
Approach:	They propose a framework to address fairness issues inherent in rumor detection models . they perform unsupervised partitioning to dynamically identify potential unfair data patterns . then, they apply invariant learning to these partitions to extract fair and informative feature representations .
Outcome:	The proposed method outperforms strong baselines regarding detection and fairness performance . it also shows robust performance on out-of-distribution samples .

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Sichun Luo, Yi Huang, Shichang Meng, Fengyuan Liu, Mukai Li, Qinghua Yao, Zefa Hu, Junlan Feng, Qi Liu

Programming Languages in NLP

Challenge:	Existing benchmarks rely on outcome-driven metrics such as profitability and look-ahead bias.
Approach:	They propose a diagnostic benchmark for instruction-grounded financial code generation under strict semantic and temporal constraints.
Outcome:	The proposed benchmarks show that the models fail under causal, structural, or functional constraints.

Exploring and Distilling Multi-Dimensional Clues for Interpretable Social Bot Detection (2026.acl-long)

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Yi Han, Haiqi Lu, Lizi Liao, Shuhan Zhou, Yuanxing Liu, Weinan Zhang, Ting Liu

RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation (2023.emnlp-main)

Challenge:	Existing research on social bot detection results directly without corresponding supportive explanations, making it difficult to assess the extent to which such predictions are trustworthy.
Approach:	They propose a four-dimensional clue framework that uses outcome-reward reinforcement learning to train inspectors to generate faithful, grounded clues from user information, semantic features, interactive situation, and behavioral pattern.
Outcome:	The proposed framework outperforms baselines in detection performance and significantly improves the performance of large language models.

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Fengji Zhang, Bei Chen, Yue Zhang, Jacky Keung, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, Weizhu Chen

Chain-of-Question: A Progressive Question Decomposition Approach for Complex Knowledge Base Question Answering (2024.findings-acl)

Challenge:	RepoCoder is a repository-level code completion framework that utilizes the useful information scattered in files.
Approach:	They propose a repository-level code completion framework called RepoCoder . it integrates a similarity-based retriever and a pre-trained code language model . they propose 'repoBench' benchmark to validate the framework's effectiveness .
Outcome:	The proposed framework outperforms the vanilla retrieval-augmented code completion approach in the real-world.

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Peng Yixing, Quan Wang, Licheng Zhang, Yi Liu, Zhendong Mao

TombRaider: Entering the Vault of History to Jailbreak Large Language Models (2025.emnlp-main)

Challenge:	Existing methods to answer complex questions rely on decomposition of complex questions into sub-questions . Existing approaches to decompose complex questions are limited by the original question .
Approach:	They propose a question decomposition approach to decompose semantically clear questions . they use the decomposed sub-questions to select relevant patterns as auxiliary information .
Outcome:	The proposed method achieves state-of-the-art performance on multiple datasets.

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Junchen Ding, Jiahao Zhang, Yi Liu, Ziqi Ding, Gelei Deng, Yuekang Li

BadActs: A Universal Backdoor Defense in the Activation Space (2024.findings-acl)

Challenge:	Existing jailbreak techniques focus on prompt manipulation or intent obfuscation to bypass safety filters.
Approach:	They propose a jailbreak technique that exploits the ability to store, retrieve, and use historical knowledge of Large Language Models (LLMs) they use an inspector agent to extract historical information and an attacker agent to generate adversarial prompts, enabling effective bypassing of safety filters.
Outcome:	The proposed jailbreak technique outperforms state-of-the-art jailbreak techniques on six popular models and maintains over 55.4% ASR against defence mechanisms.

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Biao Yi, Sishuo Chen, Yiming Li, Tong Li, Baolei Zhang, Zheli Liu

Parsing Tweets into Universal Dependencies (N18-1)

Challenge:	Backdoor attacks pose an increasingly severe security threat to Deep Neural Networks . existing methods focused on the word space are ineffective against feature-space triggers - a recent study has shown .
Approach:	They propose a backdoor defense that purifies backdoor samples in the activation space . they aim to eliminate backdoor triggers while preserving the integrity of clean data .
Outcome:	The proposed method achieves state-of-the-art against backdoor attacks on clean data.

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Yijia Liu, Yi Zhu, Wanxiang Che, Bing Qin, Nathan Schneider, Noah A. Smith

Syntactic Text Processing

Challenge:	a new tweet treebank for English is designed to analyze tweets with universal dependencies (UD).
Approach:	They extend the universal dependencies guidelines to include special constructions in tweets that affect tokenization, part-of-speech tagging, and labeled dependencies.
Outcome:	The proposed method outperforms state-of-the-art parsers on other treebanks in accuracy and speed.

OneRec-Think: In-Text Reasoning for Generative Recommendation (2026.acl-long)

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Zhanyu Liu, Shiyao Wang, Xingmei Wang, Rongzhou Zhang, Jiaxin Deng, Honghui Bao, Jinghao Zhang, Wuchao Li, PengFei Zheng, Xiangyu Wu, Yifei Hu, Qigen Hu, Xinchen Luo, Lejian Ren, Zhang Zixing, Qianqian Wang, Kuo Cai, Yunfan Wu, Hongtao Cheng, Zexuan Cheng, Lu Ren, Huanjie Wang, Yi Su, Ruiming Tang, Kun Gai, Guorui Zhou

Linguistic Cues for LLM-based Implicit Discourse Relation Classification (2026.findings-eacl)

Challenge:	Existing generative models lack the capacity for explicit and controllable reasoning, a key advantage of LLMs.
Approach:	They propose a framework that integrates dialogue, reasoning, and personalized recommendation.
Outcome:	Experiments across public benchmarks show state-of-the-art performance.

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Yi Fan, Michael Strube, Wei Liu

DPWriter: Reinforcement Learning with Diverse Planning Branching for Creative Writing (2026.acl-long)

Challenge:	Large language models (LLMs) have been successful in many NLP tasks, but they struggle to capture subtle lexical relations between arguments.
Approach:	They propose a strategy that enriches arguments with explicit lexical-level semantic cues before fine-tuning.
Outcome:	The proposed approach improves F1 scores in cross-domain scenarios by more than 10 points compared to baselines.

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Qian Cao, Yahui Liu, Wei Bi, Yi Zhao, Ruihua Song, Xiting Wang, Ruiming Tang, Guorui Zhou, Han Li

Challenge:	Existing methods for enhancing large language models (LLMs) lack explicit mechanisms for guiding diverse exploration and instead prioritize efficiency and performance over diversity.
Approach:	They propose a reinforcement learning-based framework that decomposes the generation process into explicitly planned intermediate steps and introduces divergence at the planning phase based on diversity variation.
Outcome:	The proposed method significantly outperforms existing baselines on creative writing benchmarks on a semi-structured long chain-of-thought (CoT) it introduces divergence at the planning phase based on diversity variation, alongside a group-aware diversity reward to encourage distinct trajectories.

R-LoRA: Randomized Multi-Head LoRA for Efficient Multi-task Learning (2025.findings-emnlp)

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Jinda Liu, Yi Chang, Yuan Wu

TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents (2026.findings-acl)

Challenge:	Low-Rank Adaptation (LoRA) improves performance in multi-task learning by diversifying the head matrices through Multi-Head Dropout and Multi-head Random Initialization.
Approach:	They propose a low-rank adaptive approach to fine-tune large language models by approximating weight updates through low-ranked matrices.
Outcome:	The proposed approach improves performance in multi-task learning while reducing memory usage and training time.

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Kai Li, Xuanqing Yu, Ziyi Ni, Yi Zeng, Yao Xu, Zheqing Zhang, Xin Li, Jitao Sang, Xiaogang Duan, Xuelei Wang, Chengbao Liu, Jie Tan

Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs (2024.findings-emnlp)

Challenge:	Existing memory frameworks provide limited support for temporally structured information across hierarchical levels, leading to fragmented memories and unstable long-horizon personalization.
Approach:	They propose a temporal–hierarchical memory framework that organizes conversations through a Temporal Memory Tree.
Outcome:	The proposed framework outperforms baselines while reducing the recalled memory length by 52.20%.

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Wenhua Cheng, Weiwei Zhang, Haihao Shen, Yiyang Cai, Xin He, Lv Kaokao, Yi Liu

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

Challenge:	Large Language Models (LLMs) have demonstrated exceptional proficiency in language-related tasks, but their deployment poses significant memory and storage requirements.
Approach:	They propose a method that optimizes rounding values and weight clipping within 200 steps.
Outcome:	The proposed method achieves exceptional results across 2 to 4 bits while maintaining low tuning costs and avoiding additional inference overhead.

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

SEARCH-R: Structured Entity-Aware Retrieval with Chain-of-Reasoning Navigator for Multi-hop Question Answering (2026.findings-acl)

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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FU Yuqing, Yimin Deng, Wanyu Wang, Yuhao Wang, Yejing Wang, Hongshi Liu, Yiqi Wang, Xiao Han, Maolin Wang, Guoshuai Zhao, Yi Chang, Xiangyu Zhao

TOREE: Evaluating Topic Relevance of Student Essays for Chinese Primary and Middle School Education (2024.findings-acl)

Challenge:	Existing approaches to multi-hop question answering lack effective control over reasoning paths, leading to astray results.
Approach:	They propose a framework for multi-hop question answering that trains an end-to-end reasoning path navigator to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model.
Outcome:	The proposed framework trains an end-to-end reasoning path navigator . it is able to provide a powerful sub-question decomposer by fine-tuning the Llama3.1-8B model .

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Xinlin Zhuang, Hongyi Wu, Xinshu Shen, Peimin Yu, Gaowei Yi, Xinhao Chen, Tu Hu, Yang Chen, Yupei Ren, Yadong Zhang, Youqi Song, Binxuan Liu, Man Lan

Synapse: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation (2026.findings-acl)

Challenge:	Existing research on Automatic Essay Scoring (AES) for Chinese essays has overlooked topic relevance and lacks detailed feedback.
Approach:	They propose to use TOREE to assess topic relevance in Chinese primary and middle school students’ essays to improve automatic and human evaluations.
Outcome:	The proposed method significantly improves both automatic and human evaluations across four diverse LLMs.

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Hanqi Jiang, Junhao Chen, Yi Pan, Ling Chen, Weihang You, Yifan Zhou, Ruidong Zhang, Yohannes Abate, Tianming Liu

LLaTiSA: Towards Difficulty-Stratified Time Series Reasoning from Visual Perception to Semantics (2026.findings-acl)

Challenge:	Large Language Models excel at generalized reasoning, but lack the ability to accumulate experiences and maintain narrative coherence over long horizons.
Approach:	They propose a unified memory architecture that transcends static vector similarity.
Outcome:	The proposed model outperforms state-of-the-art methods in temporal and multihop reasoning tasks.

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Yueyang Ding, HaoPeng Zhang, Rui Dai, Yi Wang, Tianyu Zong, Kaikui Liu, Xiangxiang Chu

CLGC: A Corpus for Chinese Literary Grace Evaluation (2022.lrec-1)

Challenge:	Current research hinders the development of unified Time Series Reasoning Models (TSRMs) time series data are a fundamental modality for capturing the temporal dynamics of complex systems.
Approach:	They propose a time series reasoning model that integrates visualized patterns with precision-calibrated numerical tables to enhance the temporal perception of Vision-Language Models.
Outcome:	The proposed model outperforms existing models and exhibits robust out-of-distribution generalization across diverse tasks and real-world scenarios.

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Yi Li, Dong Yu, Pengyuan Liu

Text Classification

Challenge:	Literature grace is a key element of the style and quality of articles in China.
Approach:	They propose to annotate a Chinese literary grace corpus with 10,000 texts and 1.85 million tokens and build a literary grace evaluation task to assess the literary grace level.
Outcome:	The proposed model achieves 79.71% on the weighted average F1-score.

MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems (2026.acl-long)

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Shuhang Chen, Hangjie Yuan, Yunqiu Xu, Pengwei Liu, Tao Feng, Jun Cen, Zeying Huang, Yi Yang

RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection (2025.acl-long)

Challenge:	Existing multimodal large language models (MLLMs) exhibit significant limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs.
Approach:	They propose a benchmark that provides a fine-grained evaluation of MLLMs’ perception and reasoning capabilities.
Outcome:	The proposed benchmark shows that existing MLLMs exhibit limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs.

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Wenjun Hou, Yi Cheng, Kaishuai Xu, Heng Li, Yan Hu, Wenjie Li, Jiang Liu

CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models (2023.findings-emnlp)

Challenge:	Existing approaches to enhance radiology report generation overlook the knowledge already embedded within the models, leading to redundant information integration.
Approach:	They propose a framework for enhancing radiology report generation with supplementary knowledge injection that leverages both internal and external knowledge.
Outcome:	Extensive experiments on MIMIC-CXR, CheXpert-Plus, and IU X-ray show that the proposed model outperforms state-of-the-art LLMs in both language quality and clinical accuracy.

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Cheng Qian, Chi Han, Yi Fung, Yujia Qin, Zhiyuan Liu, Heng Ji