Papers by Li Jin
Modeling Hierarchical Syntax Structure with Triplet Position for Source Code Summarization (2022.acl-long)
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| Challenge: | Existing approaches to describe the syntax structure of code are lacking in retaining the semantic structure of source code. |
| Approach: | They propose to use a triplet position to model hierarchical syntax structure of code by introducing a graph neural network and Transformer to preserve the structural and sequential information of code. |
| Outcome: | The proposed model preserves the structural and sequential information of code and a pointer-generator network that pays attention to both the structure and sequential tokens of code for a better summary generation. |
CODERL+: Improving Code Generation via Reinforcement with Execution Semantics Alignment (2026.acl-long)
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Xue Jiang, Yihong Dong, Mengyang Liu, Deng Hongyi, Tian Wang, Yongding Tao, Zhi Jin, Wenpin Jiao, Ge Li
| Challenge: | Large Language Models excel at code generation by learning from vast code corpora, but a fundamental semantic gap remains between training on textual patterns and the goal of functional correctness . reinforcement learning with verifiable rewards (RLVR) approaches are inefficient for establishing a well-aligned connection between the textual representation of code and its execution semantics. |
| Approach: | They propose a novel approach that integrates execution semantics alignment into the RLVR training pipeline for code generation. |
| Outcome: | The proposed model outperforms baseline training and RLVR and shows strong applicability across RL and LLMs. |
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning (2022.acl-short)
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Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length. |
| Approach: | They propose to use a length-aware Convolutional Neural Network to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy. |
| Outcome: | The proposed model improves performance under both offline and online learning strategies. |
SiLP: Enhancing Non-Dominant Language Capabilities with a Selective Bidirectional Language Projection Framework (2026.acl-long)
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| Challenge: | Existing methods to improve performance of large language models rely on additional training objectives or language-specific parameters. |
| Approach: | They propose a bidirectional language projection framework that enables efficient multilingual alignment and language shift using the intrinsic parameters. |
| Outcome: | The proposed framework improves performance of non-dominant languages and improves internal representations. |
OpenSR: Open-Modality Speech Recognition via Maintaining Multi-Modality Alignment (2023.acl-long)
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| Challenge: | Speech Recognition often gets stuck in the lack of new domain utterances when training a model of new-domain speech. |
| Approach: | They propose a training system Open-modality Speech Recognition that enables zero-shot modality transfer . they use multi-modal alignment in phoneme space to maintain multi-modality alignment . |
| Outcome: | The proposed system achieves zero-shot modality transfer compared to existing methods . it achieves state-of-the-art performance on audio-visual speech recognition and lip-reading with 2.7% and 25.0%, respectively. |
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
| 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. |
Two Pathways to Truthfulness: On the Intrinsic Encoding of LLM Hallucinations (2026.acl-long)
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Wen Luo, Guangyue Peng, Wei Li, Shaohang Wei, Feifan Song, Liang Wang, Nan Yang, Xingxing Zhang, Jing Jin, Furu Wei, Houfeng Wang
| Challenge: | Previous work shows that large language models generate hallucinations, yet the origins and mechanisms of these signals remain unclear. |
| Approach: | They propose to validate and disentangle two different pathways for truthfulness cues . they also propose to use the same mechanism to derive self-contained evidence from the generated answer . |
| Outcome: | The proposed applications improve hallucination detection performance by integrating two different inputs. |
IntentCoding: Amplifying User Intent in Code Generation (2026.findings-acl)
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| Challenge: | IntentCoding captures the influence of user intent by masking out the intent, and integrates seamlessly with existing decoding procedures. |
| Approach: | They propose a decoding strategy that captures the influence of user intent by masking out the intent and applies a multi-strength ensemble mechanism to amplify the effect of user intention during generation. |
| Outcome: | The proposed model significantly improves both constraint satisfaction and functional correctness compared to greedy decoding approaches. |
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs (2021.acl-long)
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| Challenge: | Temporal Knowledge Graphs (TKGs) are used in many different areas of research. |
| Approach: | They propose to use a beam search policy to induce multiple clues from historical facts . they propose to adopt a graph convolution network based sequence method to deduce answers from clues . |
| Outcome: | The proposed model can predict future facts in two stages, Clue Searching and Temporal Reasoning. |
Semantic-conditioned Dual Adaptation for Cross-domain Query-based Visual Segmentation (2023.findings-acl)
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| Challenge: | Existing approaches to visual segmentation from language queries require expensive labeling and degradation when deployed to an unseen domain. |
| Approach: | They propose a task to adapt a visual segmentation model from a labeled domain to an unseen domain. |
| Outcome: | The proposed framework achieves precise feature- and relation-invariant across domains via universal semantic structure. |
Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities (2025.findings-acl)
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Wenyue Hua, Kaijie Zhu, Lingyao Li, Lizhou Fan, Mingyu Jin, Shuhang Lin, Haochen Xue, Zelong Li, Jindong Wang, Yongfeng Zhang
| Challenge: | Using large language models, large language model models can be used to evaluate reasoning abilities in context-rich scenarios. |
| Approach: | They construct datasets for both propositional logic and abductive logic reasoning with four difficulty levels across 12 distinct domains based on Wikipedia categorization and those with purely abstract variables. |
| Outcome: | The proposed model can be used to benchmark LLMs in real-world scenarios, but not in context-rich scenarios. |
TrInk: Ink Generation with Transformer Network (2025.emnlp-main)
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Zezhong Jin, Shubhang Desai, Xu Chen, Biyi Fang, Zhuoyi Huang, Zhe Li, Chong-Xin Gan, Xiao Tu, Man-Wai Mak, Yan Lu, Shujie Liu
| Challenge: | Existing methods for handwriting generation capture global dependencies and can generate high-quality handwritten samples. |
| Approach: | They propose a Transformer-based model for ink generation, TrInk, which captures global dependencies. |
| Outcome: | The proposed model reduces character error rate and word error rate by 35.56% on the IAM-OnDB dataset compared to previous models. |
Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents (2026.acl-long)
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null Ymyang, Jiang Zhong, Li Jin, Xiao Sun, Jingwang Huang, null Gaojinpeng, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Qin Lei, Kaiwen Wei
| Challenge: | Existing benchmarks focus on simple image-text interactions, overlooking complex visual formats like charts. |
| Approach: | They propose a semi-automatic framework for generating evaluation samples through multi-modal keypoint extraction, knowledge graph construction, and qa pair synthesis. |
| Outcome: | The proposed framework generates 4,738 question-answering pairs across 8 domains from real-world documents. |
Temporal Knowledge Graph Reasoning Based on N-tuple Modeling (2023.findings-emnlp)
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| Challenge: | Existing Temporal Knowledge Graphs (TKGs) only contain their core entities and form them as quadruples. |
| Approach: | They propose to describe a temporal fact more accurately as an n-tuple . they propose to use a neural network to learn evolutional representations of entities . |
| Outcome: | The proposed model oversimplifies and causes information loss on two datasets. |
Instruction-following Evaluation through Verbalizer Manipulation (2024.findings-naacl)
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| Challenge: | Existing benchmarks focus on common instructions that align well with what the model learned during training, but proficiency in responding to these instructions does not necessarily imply strong ability in instruction following. |
| Approach: | They propose a new instruction-following evaluation protocol called verbalizer manipulation that instructs the model to verbalize the task label with words aligning with model priors to different extents. |
| Outcome: | The proposed protocol can be integrated with any classification benchmark to examine the model’s reliance on priors and its ability to override them to accurately follow the instructions. |
End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding (2022.acl-long)
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Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, Shiliang Pu, Fei Wu
| Challenge: | Existing methods for grounding video frames with dense annotations require enormous amount of human effort. |
| Approach: | They propose to ground natural language in video frames with only one frame labeled . they propose an end-to-end model that eliminates interference of irrelevant frames . |
| Outcome: | The proposed model can ground natural language in all video frames with only one frame labeled . the proposed model eliminates interference of irrelevant frames based on branch search and cropping techniques . |
Feature-Adaptive and Data-Scalable In-Context Learning (2024.acl-long)
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| Challenge: | In-context learning (ICL) is a popular way to stimulate LLM capabilities for downstream tasks due to context length constraints. |
| Approach: | They propose a feature-adaptive and data-scalable in-context learning framework which leverages task-adaptives to promote inference on the downstream task. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on 10 datasets under different data settings and LLM scale. |
CodeArena: Evaluating and Aligning CodeLLMs on Human Preference (2025.emnlp-main)
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Jian Yang, Jiaxi Yang, Wei Zhang, Jin Ke, Yibo Miao, Lei Zhang, Liqun Yang, Zeyu Cui, Yichang Zhang, Zhoujun Li, Binyuan Hui, Junyang Lin
| Challenge: | Code large language models (codeLLMs) focus on synthesizing the correct code snippet, ignoring the alignment with human preferences. |
| Approach: | They propose a benchmark code-based on 40 categories and 44 programming languages to emulate real-world coding tasks. |
| Outcome: | The proposed benchmarks show that open-source code LLMs perform better than open-sourced ones. |
Topology-of-Question-Decomposition: Enhancing Large Language Models with Information Retrieval for Knowledge-Intensive Tasks (2025.coling-main)
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| Challenge: | Large language models (LLMs) are constrained to chaining immediate reasoning steps and relying solely on parametric knowledge. |
| Approach: | They propose a framework that activates retrieval only when necessary to improve answer accuracy. |
| Outcome: | Experiments show that the proposed framework improves performance in knowledge-intensive tasks. |
Tug-of-War between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models (2024.lrec-main)
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| Challenge: | Existing knowledge conflicts in RALMs can ensnare them in a tug-of-war between knowledge and evidence, limiting their practical applicability. |
| Approach: | They propose a method called Conflict-Disentangle Contrastive Decoding (CD2) to better calibrate the model’s confidence. |
| Outcome: | The proposed method can resolve knowledge conflicts in large language models with the help of conflict-disentangle contrast decoding (CD2) . |
CODE-MVP: Learning to Represent Source Code from Multiple Views with Contrastive Pre-Training (2022.findings-naacl)
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| Challenge: | Recent studies have focused on code representation learning, which aims to represent the semantics of source code into distributed vectors. |
| Approach: | They propose to integrate different views with the natural-language description of source code into a unified framework with Multi-View contrastive Pre-training. |
| Outcome: | The proposed model outperforms state-of-the-art models on three downstream tasks over five datasets. |
Rule-Aware Reinforcement Learning for Knowledge Graph Reasoning (2021.findings-acl)
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| Challenge: | Existing methods to reason missing facts on Knowledge Graphs face with serious incompleteness due to their black-box nature. |
| Approach: | They propose a multi-hop reasoning method that injects high quality symbolic rules into the model's reasoning process and employs partially random beam search. |
| Outcome: | The proposed method outperforms existing multi-hop reasoning methods in terms of Hit@1 and MRR. |
BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover’s Distance (2020.emnlp-main)
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| Challenge: | Pre-trained language models have been proposed and applied to many NLP tasks, yielding state-of-the-art performance, but high storage and computational costs obstruct them to be effectively deployed on resource-constrained devices and real-time applications. |
| Approach: | They propose a BERT distillation method which allows each intermediate student layer to learn from any intermediate teacher layers. |
| Outcome: | The proposed method can learn from different teacher layers adaptively for different NLP tasks. |
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)
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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang
| Challenge: | Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs). |
| Approach: | They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories. |
| Outcome: | The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks. |
Agent-to-Agent Theory of Mind: Testing Interlocutor Awareness among Large Language Models (2025.emnlp-main)
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| Challenge: | Prior work has focused on situational awareness, which refers to a model's ability to recognize its operating phase and constraints, but it has neglected the complementary capacity to identify and adapt to the identity and characteristics of a dialogue partner. |
| Approach: | They formalize interlocutor awareness and evaluate its emergence in contemporary LLMs. |
| Outcome: | The proposed model reliably identify same-family peers and certain prominent model families, such as GPT and Claude. |
Data Whisperer: Efficient Data Selection for Task-Specific LLM Fine-Tuning via Few-Shot In-Context Learning (2025.acl-long)
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Shaobo Wang, Xiangqi Jin, Ziming Wang, Jize Wang, Jiajun Zhang, Kaixin Li, Zichen Wen, Zhong Li, Conghui He, Xuming Hu, Linfeng Zhang
| Challenge: | Using fine-tuning on task-specific data is essential for large language models to be effective in specialized tasks. |
| Approach: | They propose a method that leverages few-shot in-context learning with the model to be fine-tuned. |
| Outcome: | The proposed method outperforms existing methods with a 3.1-point improvement and a 7.4 speedup on the Llama-3-8B-Instruct model using just 10% of the dataset. |
TAVT: Towards Transferable Audio-Visual Text Generation (2023.acl-long)
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| Challenge: | Existing transfer learning techniques focus on uni-modal analysis and lack consideration of multi-modal content and cross-modal relation. |
| Approach: | They propose a transferable audio-visual text generation framework that incorporates two components: Audio-Visual Meta-Mapper and Dual Counterfactual Contrastive Learning. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods across multiple domains and modal settings. |
COPEN: Probing Conceptual Knowledge in Pre-trained Language Models (2022.emnlp-main)
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| Challenge: | Existing knowledge probing studies focus on evaluating factual knowledge of pre-trained language models (PLMs) but ignore conceptual knowledge. |
| Approach: | They evaluate conceptual knowledge of pre-trained language models by annotating 24k data instances covering 393 concepts. |
| Outcome: | The proposed tasks evaluate pre-trained language models' conceptual knowledge of entities, learn conceptual properties, and conceptualize entities in contexts. |
Zero-shot Generalization in Dialog State Tracking through Generative Question Answering (2021.eacl-main)
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| Challenge: | Existing methods for Dialog State Tracking do not generalize well to new domains and unseen slots. |
| Approach: | They propose an ontology-free framework that queries for unseen constraints and slots in multi-domain task-oriented dialogs using a conditional language model pre-trained on substantive English sentences. |
| Outcome: | The proposed framework improves goal accuracy in zero-shot domain adaptation settings by up to 9% over the previous state-of-the-art on the MultiWOZ 2.1 dataset. |
Cutting Off the Head Ends the Conflict: A Mechanism for Interpreting and Mitigating Knowledge Conflicts in Language Models (2024.findings-acl)
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Zhuoran Jin, Pengfei Cao, Hongbang Yuan, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao
| Challenge: | Existing methods to expand internal memory boundaries of language models by providing external context can often conflict, leading to knowledge conflicts. |
| Approach: | They propose a method that prunes conflicting attention heads without updating model parameters. |
| Outcome: | The proposed method can flexibly control eight LMs to use internal memory or external context without updating model parameters. |
GER-LLM: Efficient and Effective Geospatial Entity Resolution with Large Language Model (2025.emnlp-main)
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| Challenge: | Existing methods for integrating spatial data from diverse sources are limited by their reliance on large amounts of training data and their inability to incorporate commonsense knowledge. |
| Approach: | They propose a framework that integrates large language models into the GER pipeline. |
| Outcome: | The proposed framework improves on real-world geospatial datasets and shows that it is more efficient than state-of-the-art methods. |
Weakly-Supervised Spoken Video Grounding via Semantic Interaction Learning (2023.acl-long)
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| Challenge: | Recent work on spoken video grounding challenges extracting semantic information from speech . previous studies focused on textual queries, but recent work focuses on spoken queries . |
| Approach: | They propose a framework for weakly-supervised spoken video grounding to represent cross-modal semantics without expensive temporal annotations. |
| Outcome: | The proposed framework is more efficient than existing methods. |
Focus on Your Question! Interpreting and Mitigating Toxic CoT Problems in Commonsense Reasoning (2024.acl-long)
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| Challenge: | Large language models exhibit high-level commonsense reasoning abilities, especially with enhancement methods like Chain-of-Thought (CoT). |
| Approach: | They propose a chain-of-thought-like method to elicit models' potential abilities to generate rationales and answers that are based on attribution tracing and causal tracers to probe the internal working mechanism of the LLM. |
| Outcome: | The proposed method eliminates Toxic CoT problems and improves the model’s overall commonsense reasoning performance by 5.5%. |
Distributed LLM Serving on Consumer-Grade GPUs by Reconciling Computation and Communication (2025.findings-emnlp)
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Lewei Jin, Kui Zhang, Yongqi Chen, null Zhuoyifan, Renjie Li, Yi Gao, Bowei Yang, Zhengong Cai, Wei Dong
| Challenge: | Large language models are reshaping internet services, and serving them is costly. |
| Approach: | They propose an efficient distributed LLM serving system that splits prefill and decode requests into smaller chunks . |
| Outcome: | The proposed system reduces TTFT, TPOT, and latency compared to the state-of-the-art system. |
Depth Aware Hierarchical Replay Continual Learning for Knowledge Based Question Answering (2024.lrec-main)
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| Challenge: | Continual learning models adapt well to the latest data but lose ability to remember past data due to changes in the data source. |
| Approach: | They propose a hierarchical replay framework that allows models to keep a small memory of previous learned data that uses replay. |
| Outcome: | The proposed model outperforms previous continual learning methods in mitigating catastrophic forgetting. |
Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction (2026.acl-long)
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| Challenge: | Current distractor generation methods produce shared distractors for all students, ignoring individual variations in reasoning, which limits their diagnostic effectiveness. |
| Approach: | They propose a method which tailors distractors to each student’s specific cognitive flaws, inferred from their past question-answering (QA) history. |
| Outcome: | The proposed framework outperforms existing methods in generating plausible distractors and adapts to group-level settings. |
LLM-Driven Knowledge Injection Advances Zero-Shot and Cross-Target Stance Detection (2024.naacl-short)
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| Challenge: | Existing methods for stance detection focus on background information and not on the accompanying input texts. |
| Approach: | They propose to prompt Large Language Models to explicitly extract the relationship between paired text and unseen target as contextual knowledge and inject it into a generation model BART to exploit the rich contexts and semantics. |
| Outcome: | The proposed model is able to detect stance labels in zero-shot and cross-target scenarios. |
HELENE: Hessian Layer-wise Clipping and Gradient Annealing for Accelerating Fine-tuning LLM with Zeroth-order Optimization (2025.emnlp-main)
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| 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. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow Paradigm (2024.findings-acl)
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Yuanzhen Xie, Xinzhou Jin, Tao Xie, Matrixmxlin Matrixmxlin, Liang Chen, Chenyun Yu, Cheng Lei, Chengxiang Zhuo, Bo Hu, Zang Li
| Challenge: | In-context learning of large-language models has achieved remarkable success in the field of natural language processing . however, the single-step chain-of-thought prompting approach faces challenges such as attention diffusion and inadequate performance in complex tasks like text-to-SQL. |
| Approach: | They propose a workflow paradigm method to enhance the attention and problem-solving scope of large-language models through decomposition. |
| Outcome: | The proposed method outperforms existing methods on three datasets and improves the upper limit of LLM-based approaches. |
MccSTN: Multi-Scale Contrast and Fine-Grained Feature Fusion Networks for Subject-driven Style Transfer (2024.lrec-main)
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| Challenge: | Stylistic style transfer is an important part of the image processing field . due to the low semantic similarity between the original image and the style image, many fine-grained style features are discarded. |
| Approach: | They propose a new style representation and transfer framework that can be adapted to existing image style transfers. |
| Outcome: | The proposed framework can be adapted to existing image style transfers. |
CRAB: Cross-environment Agent Benchmark for Multimodal Language Model Agents (2025.findings-acl)
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Tianqi Xu, Linyao Chen, Dai-Jie Wu, Yanjun Chen, Zecheng Zhang, Xiang Yao, Zhiqiang Xie, Yongchao Chen, Shilong Liu, Bochen Qian, Anjie Yang, Zhaoxuan Jin, Jianbo Deng, Philip Torr, Bernard Ghanem, Guohao Li
| Challenge: | Existing benchmarks for MLM agents in interactive environments are limited by their focus on a single environment, lack of detailed and generalized evaluation methods, and the complexity of constructing tasks and evaluators. |
| Approach: | They propose a cross-environment agent benchmark framework that integrates graph-based evaluation and task generation methods. |
| Outcome: | The proposed framework supports multiple devices and can be easily extended to any environment with a Python interface. |
Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention (C18-1)
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| Challenge: | Existing topic models ignore that one discusses diverse topics when dynamically interacting with different people. |
| Approach: | They propose an Interaction-Aware Topic Model (IATM) for microblog conversations by integrating network embedding and user attention. |
| Outcome: | The proposed model is based on three real-world microblog datasets. |
A Knowledge Plug-and-Play Test Bed for Open-domain Dialogue Generation (2024.lrec-main)
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| Challenge: | Knowledge-based open-domain dialogue generation aims to build chit-chat systems that talk to humans using mined support knowledge. |
| Approach: | They propose a benchmark for evaluating multi-source dialogue knowledge selection and response generation using Wikipedia's wizard of Wikipedia. |
| Outcome: | The proposed benchmark is called multi-source Wizard of Wikipedia (Ms.WoW) it contains clean support knowledge, grounded at the utterance level and partitioned into multiple knowledge sources. |
RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation (2025.acl-long)
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| Challenge: | Existing methods rely on separate retrievers to fetch top-k text chunks for generating evidence, and they lack joint optimization. |
| Approach: | They propose a framework that integrates retrieval and generation into a single, auto-regressive process, enabling LLMs to directly generate fine-grained evidence from the corpus with constrained decoding. |
| Outcome: | Extensive experiments on five open-domain QA datasets demonstrate the proposed framework’s superior performance across both in-domain and out-of-domain tasks. |
FinMaster: A Holistic Benchmark for Full-Pipeline Financial Management with Large Language Models (2026.findings-acl)
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Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Yujing Zhang, Yilin Xiao, Ruiyu Wang, Bo Li, Xiao Huang, Danny Dongning Sun, Xinrun Wang
| Challenge: | Existing benchmarks lack domain-specific data, realistic workflow-level task design, and standardized workflow- level evaluation. |
| Approach: | a new benchmark evaluates large language models on financial management workflows . the global financial services market is projected to grow to $37 trillion by 2027 . |
| Outcome: | a new benchmark for large language models on financial management workflows reveals critical capability gaps . accuracy drops from 90% on basic tasks to 40% on complex scenarios requiring multi-step reasoning . the global financial services market reached $25.8 trillion in 2022 and is projected to grow to $37 trillion by 2027 . |
SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis (2025.findings-naacl)
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Hengxing Cai, Xiaochen Cai, Junhan Chang, Sihang Li, Lin Yao, Wang Changxin, Zhifeng Gao, Hongshuai Wang, Li Yongge, Mujie Lin, Shuwen Yang, Jiankun Wang, Mingjun Xu, Jin Huang, Xi Fang, Jiaxi Zhuang, Yuqi Yin, Yaqi Li, Changhong Chen, Zheng Cheng, Zifeng Zhao, Linfeng Zhang, Guolin Ke
| Challenge: | Existing benchmarks fail to adequately evaluate the proficiency of Large Language Models (LLMs) Existing standards do not cover the skills needed to evaluate LLMs in scientific literature analysis. |
| Approach: | They propose a benchmark to evaluate the proficiency of large language models in scientific literature analysis. |
| Outcome: | SciAssess evaluates 11 LLMs on multiple tasks across scientific fields. |
Self-Edit: Fault-Aware Code Editor for Code Generation (2023.acl-long)
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| Challenge: | Existing Large language models (LLMs) have low pass rates and accuracy on competitive programming tasks. |
| Approach: | They propose a generate-and-edit approach that uses execution results of generated code from LLMs to improve code quality on competitive programming tasks. |
| Outcome: | The proposed method improves pass@1 by 89% on APPS-dev, 31% on apps-test, and 48% on HumanEval over nine popular code generation LLMs with parameter sizes ranging from 110M to 175B. |
OpenEval: Benchmarking Chinese LLMs across Capability, Alignment and Safety (2024.acl-demos)
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Chuang Liu, Linhao Yu, Jiaxuan Li, Renren Jin, Yufei Huang, Ling Shi, Junhui Zhang, Xinmeng Ji, Tingting Cui, Liutao Liutao, Jinwang Song, Hongying Zan, Sun Li, Deyi Xiong
| Challenge: | a rapid development of Chinese large language models poses big challenges for efficient LLM evaluation. |
| Approach: | They propose an evaluation testbed that benchmarks Chinese LLMs across capability, alignment and safety. |
| Outcome: | The evaluation platform OpenEval benchmarks Chinese LLMs across capability, alignment and safety. |
CodeAgent: Enhancing Code Generation with Tool-Integrated Agent Systems for Real-World Repo-level Coding Challenges (2024.acl-long)
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| Challenge: | Large Language Models excel in simple tasks such as generating standalone code units, but real-world software development often involves complex code repositories with complex dependencies and extensive documentation. |
| Approach: | They propose a novel LLM-based agent framework that employs external tools for effective repo-level code generation. |
| Outcome: | The proposed framework outperforms commercial products like Github Copilot in the humanEval benchmark and shows that it is adaptable and efficient across multiple code generation tasks. |
PACE: Improving Prompt with Actor-Critic Editing for Large Language Model (2024.findings-acl)
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| Challenge: | Prompt with Actor-Critic Editing (PACE) for LLMs improves performance of different human-written prompts, resulting in significant performance discrepancies. |
| Approach: | They propose to use LLMs as actors and critics to enable automatic prompt editing by taking feedback from both actors performing prompt and criticizing response into account. |
| Outcome: | The proposed model improves the performance of human-written prompts by 98% and compares to high-quality human-writing prompts. |
Automatic Generation of Model and Data Cards: A Step Towards Responsible AI (2024.naacl-long)
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| Challenge: | Existing models and datasets are incomplete and lack consistent documentation. |
| Approach: | They propose an automated generation approach using Large Language Models (LLMs) their paper establishes a comprehensive dataset and develops 'CardGen' pipeline . |
| Outcome: | The proposed approach exhibits enhanced completeness, objectivity, and faithfulness in generated model and data cards, a significant step in responsible AI documentation practices ensuring better accountability and traceability. |
FinRAGBench-V: A Benchmark for Multimodal RAG with Visual Citation in the Financial Domain (2025.emnlp-main)
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| Challenge: | Existing RAG research focuses on textual data, overlooking rich visual content in financial documents. |
| Approach: | They propose a visual RAG benchmark tailored for finance that integrates multimodal data and provides visual citation to ensure traceability. |
| Outcome: | The proposed visual RAG benchmark integrates multimodal data and provides visual citation to ensure traceability. |
A Topic Augmented Text Generation Model: Joint Learning of Semantics and Structural Features (D19-1)
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| Challenge: | Existing methods for text generation are limited in supervised setting and designed for specific applications. |
| Approach: | They propose a text generation model that learns semantics and structural features simultaneously . their model leverages a topic-based model to enhance the recognition of text semantics . |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of text perplexity and topic coherence. |
Taming LLMs with Gradient Grouping (2025.acl-long)
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| Challenge: | a new study presents scaling with gradient grouping (SGG) the adaptive learning rate scaling approach is based on per-parameter statistics, which incurs memory overhead. |
| Approach: | They propose an optimizer wrapper that improves adaptive learning rate estimation by dynamic grouping and group-specific scaling. |
| Outcome: | The proposed algorithm improves learning rate estimation on diverse models with different model sizes and batch sizes. |
Evaluating Readability and Faithfulness of Concept-based Explanations (2024.emnlp-main)
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| Challenge: | Existing methods for evaluating concepts from different perspectives lack a unified formalization. |
| Approach: | They propose a formal definition of concepts generalizing to diverse concept-based explanations’ settings and apply it to other types of explanations or tasks. |
| Outcome: | Extensive experimental analysis was carried out to determine the evaluation measures for explanation evaluation measures. |
Low-Rank HOCA: Efficient High-Order Cross-Modal Attention for Video Captioning (D19-1)
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| Challenge: | Existing studies on video captioning focus on the association relationships between multiple modalities. |
| Approach: | They propose a video captioning model with high-order cross-modal attention (HOCA) they propose low-rank HOCA which adopts tensor decomposition to reduce the space requirement . |
| Outcome: | The proposed model captures cross-modal interaction of different modalities and reduces space requirement. |
Joint Semantic and Strategy Matching for Persuasive Dialogue (2023.findings-emnlp)
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Chuhao Jin, Yutao Zhu, Lingzhen Kong, Shijie Li, Xiao Zhang, Ruihua Song, Xu Chen, Huan Chen, Yuchong Sun, Yu Chen, Jun Xu
| Challenge: | Persuasive dialogue models rely on utterance semantic matching and a key aspect has been ignored . compared with utterrance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuation. |
| Approach: | They propose to model conversation semantics and strategies to match them using a BERT-like module and an auto-regressive predictor. |
| Outcome: | The proposed model improves state-of-the-art by 5% on a small and 37% on 'large' datasets. |
HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning (2022.findings-emnlp)
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Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Temporal Knowledge Graphs (TKGs) store facts as triples in the form of subject, relation, object, timestamps. |
| Approach: | They propose a Temporal Knowledge Graph (TKG) model that extends each triple with a timestamp to describe dynamic facts. |
| Outcome: | The proposed model improves on six benchmark datasets with up to 5.6% performance improvement compared to the state-of-the-art models. |
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing? (2022.findings-acl)
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| Challenge: | Extensive experiments on multi-lingual datasets show that our method significantly outperforms multiple baselines and can robustly handle negative transfer. |
| Approach: | They propose to transfer semantic knowledge from rich-resourced languages to low-resource languages by using multilingual transfer learning. |
| Outcome: | The proposed model outperforms baselines and can handle negative transfer. |
Generative Music Models’ Alignment with Professional and Amateur Users’ Expectations (2025.findings-acl)
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| Challenge: | Recent years have witnessed rapid advances in text-to-music generation using large language models. |
| Approach: | They propose a task to align AI-generated music with human expressions . they use a dataset of over 1.5 million songs to analyze their content . |
| Outcome: | The proposed framework outperforms baseline models and facilitates end-to-end generation of songs audio. |
A Parallelized Framework for Simulating Large-Scale LLM Agents with Realistic Environments and Interactions (2025.acl-industry)
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| Challenge: | Existing work on large language models lacks a realistic environment and parallelized framework to support complex interactions between agents and environments. |
| Approach: | They propose a framework that integrates realistic societal environments and parallelized interactions to support simulations of large-scale agents. |
| Outcome: | The proposed framework can support simulations of 30,000 agents faster than the wall-clock time with 24 NVIDIA A800 GPUs and the performance increases linearly with the increase of LLM computational resources. |
Guide the Many-to-One Assignment: Open Information Extraction via IoU-aware Optimal Transport (2023.acl-long)
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Kaiwen Wei, Yiran Yang, Li Jin, Xian Sun, Zequn Zhang, Jingyuan Zhang, Xiao Li, Linhao Zhang, Jintao Liu, Guo Zhi
| Challenge: | Open Information Extraction (OIE) aims to extract structured information from text without the limitations of close ontology. |
| Approach: | They propose a method to assign ground truth labels to parallelly generated tuple proposals . they leverage intersection-over-union (IoU) as assignment quality measurement . |
| Outcome: | The proposed method outperforms the state-of-the-art models on three benchmarks. |
NOVA: An Iterative Planning Framework for Enhancing Scientific Innovation with Large Language Models (2025.findings-acl)
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Xiang Hu, Hongyu Fu, Jinge Wang, Yifeng Wang, Zhikun Li, Renjun Xu, Yu Lu, Yaochu Jin, Lili Pan, Zhenzhong Lan
| Challenge: | Existing approaches to generate research ideas rely on retrieval or prompt engineering to generate ideas. |
| Approach: | They propose a method that uses iterative planning and search to boost creative potential of LLMs by integrating external knowledge with broader and deeper insights. |
| Outcome: | The proposed method outperforms the current state-of-the-art in generating 2.5 times more top-rated ideas based on 170 seed papers in a Swiss Tournament evaluation. |
Polynomial Expansion Rank Adaptation: Enhancing Low-Rank Fine-Tuning with High-Order Interactions (2026.findings-acl)
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| Challenge: | Low-rank adaptation (LoRA) is a widely used strategy for efficient fine-tuning of large language models, but its strictly linear structure limits expressive capacity. |
| Approach: | They propose a method that introduces structured polynomial expansion directly into the low-rank factor space. |
| Outcome: | The proposed method outperforms state-of-the-art methods across diverse benchmarks. |
Musical Score Understanding Benchmark: Evaluating Large Language Models’ Comprehension of Complete Musical Scores (2026.acl-long)
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Congren Dai, Yue Yang, Krinos Li, Huichi Zhou, Shijie Liang, Zhang Bo, Enyang Liu, Ge Jin, Hongran An, Haosen Zhang, Peiyuan Jing, KinHei Lee, Zhenxuan Zhang, Xiaobing Li, Maosong Sun
| Challenge: | Existing benchmarks for musical score understanding are narrow in scope, focusing on isolated fragments, short excerpts, or multiple-choice formulations, rather than supporting holistic reasoning over entire scores. |
| Approach: | They propose a benchmark for score-level musical understanding across textual and visual modalities. |
| Outcome: | The musical score understanding benchmark contains 1,800 question-answer pairs from works by Bach, Beethoven, Chopin, Debussy, and others. |
Video-LLaVA: Learning United Visual Representation by Alignment Before Projection (2024.emnlp-main)
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| Challenge: | Existing approaches to visual-language understanding lack unified tokenization for images and videos . lack of unified visual representations makes it difficult to learn multi-modal interactions from poor projection layers. |
| Approach: | They propose to unify visual representation into the language feature space to advance the foundational LLM towards a unified LVLM. |
| Outcome: | The proposed model outperforms Video-ChatGPT on image benchmarks and on 9 image benchmark benchmarks. |
Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injection (2024.naacl-long)
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Jun Yan, Vikas Yadav, Shiyang Li, Lichang Chen, Zheng Tang, Hai Wang, Vijay Srinivasan, Xiang Ren, Hongxia Jin
| Challenge: | Instruction-tuned Large Language Models (LLMs) can modulate responses based on human instructions, but they can be maliciously steered to impact society in subtle but persistent ways. |
| Approach: | They propose a backdoor attack setting that allows an attacker to inject a virtual prompt into an LLM to steer it without any explicit injection at its input. |
| Outcome: | The proposed method is able to poison the model's instruction tuning data and show that it is highly effective in steering the model. |
Toward Global AI Inclusivity: A Large-Scale Multilingual Terminology Dataset (GIST) (2025.findings-acl)
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Jiarui Liu, Iman Ouzzani, Wenkai Li, Lechen Zhang, Tianyue Ou, Houda Bouamor, Zhijing Jin, Mona T. Diab
| Challenge: | Despite advances in machine translation, domain-specific terminology translation remains challenging. |
| Approach: | They propose a large-scale multilingual AI terminology dataset that combines LLMs for extraction with human expertise for translation. |
| Outcome: | The proposed framework combines human translation expertise with LLMs to improve translation accuracy and improve BLEU and COMET scores. |
RSA-Bench: Benchmarking Audio Large Models in Real-World Acoustic Scenarios (2026.findings-acl)
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Yibo Zhang, Kaiwen Luo, Liang Lin, Shilinlu Yan, Jin Wang, Yaoqi Guo, Yitian Chen, Yalan Qin, Zhenhong Zhou, Kun Wang, Li Sun
| Challenge: | Existing evaluations rely on synthetic Gaussian noise or simplistic single-source interference, failing to capture the intricate, multi-layered acoustic dynamics that characterize authentic physical environments. |
| Approach: | They propose a robustness benchmark to stress-test Audio Large Models (ALLMs) using high-fidelity auditory scene simulations. |
| Outcome: | The proposed model performs well on a wide range of tasks, including automatic speech recognition, speech translation, and audio-based reasoning. |
LINKED: Eliciting, Filtering and Integrating Knowledge in Large Language Model for Commonsense Reasoning (2024.findings-emnlp)
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Jiachun Li, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Kang Liu, Xiaojian Jiang, Jiexin Xu, Jun Zhao
| Challenge: | Large language models (LLMs) often exhibit poor performance on knowledge-intensive tasks, such as commonsense reasoning. |
| Approach: | They propose a method to elicit, filter and integrate knowledge in large language models (LINKED) they propose 'reward model' to filter out noisy knowledge and 'take marginal consistent reasoning module' |
| Outcome: | The proposed method outperforms SOTA baselines on two commonsense reasoning tasks. |
On-policy Reinforcement Fine-tuning with Offline reward for Multi-step Embodied Planning (2026.acl-long)
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| Challenge: | Embodied planning requires agents to make coherent multi-step decisions based on dynamic visual observations and verbal goals. |
| Approach: | They propose an On-policy Reinforcement fine-tuning framework with offline rewards for Embodied Task Planning that preserves generalization benefits of RFT while addressing costly interaction and sparse rewards. |
| Outcome: | The proposed framework outperforms closed-source and online-RL methods on EmbodiedBench, a recent benchmark for interactive embodied tasks. |
LongLeader: A Comprehensive Leaderboard for Large Language Models in Long-context Scenarios (2025.naacl-long)
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Pei Chen, Hongye Jin, Cheng-Che Lee, Rulin Shao, Jingfeng Yang, Mingyu Zhao, Zhaoyu Zhang, Qin Lu, Kaiwen Men, Ning Xie, Huasheng Li, Bing Yin, Han Li, Lingyun Wang
| Challenge: | LongLeader aims to assess different LLMs' long-context comprehension abilities . long-constext comprehension is a key bottleneck for many use cases . |
| Approach: | They propose a leaderboard to assess different LLMs' long-context comprehension abilities . they offer open-source access to the benchmarks and maintain a dedicated website . |
| Outcome: | The proposed model assesses different LLMs on selected benchmarks and provides open-source access to the benchmarks. |
Privacy Risks of Intermediate Representations: Attribute Inference in Distributed LLM Inference (2026.findings-acl)
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| Challenge: | Distributed LLMs avoid raw inputs by transmitting intermediate hidden states, a practice widely assumed to preserve privacy. |
| Approach: | They propose a distributed inference framework that transmits intermediate hidden states to avoid sending raw inputs by exposing sensitive user attributes. |
| Outcome: | The proposed approach achieves Top-1 accuracy of 0.997 on CMS, 0.980 on Skytrax, and 0.986 on ECHR. |
Hierarchical Document Refinement for Long-context Retrieval-augmented Generation (2025.acl-long)
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Jiajie Jin, Xiaoxi Li, Guanting Dong, Yuyao Zhang, Yutao Zhu, Yongkang Wu, Zhonghua Li, Ye Qi, Zhicheng Dou
| Challenge: | Real-world RAG applications often encounter long-context input scenarios where redundant information and noise results in higher inference costs and reduced performance. |
| Approach: | They propose an efficient plug-and-play refiner that leverages the structural characteristics of long documents. |
| Outcome: | Experiments on seven QA datasets show that LongRefiner achieves competitive performance in various scenarios while using 10x fewer computational costs and latency compared to baseline. |
SAPGraph: Structure-aware Extractive Summarization for Scientific Papers with Heterogeneous Graph (2022.aacl-main)
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Siya Qi, Lei Li, Yiyang Li, Jin Jiang, Dingxin Hu, Yuze Li, Yingqi Zhu, Yanquan Zhou, Marina Litvak, Natalia Vanetik
| Challenge: | Abstractive and extractive methods are used to condense long text into concise summaries while retaining essential information. |
| Approach: | They propose to use paper structure to extract paper summaries from long text . they provide a large-scale dataset of COVID-19-related papers . |
| Outcome: | The proposed framework generates more comprehensive and valuable summaries compared to previous work on COVID-19-related papers. |
SumCSE: Summary as a transformation for Contrastive Learning (2024.findings-naacl)
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| Challenge: | Sentence embedding models are typically trained using contrastive learning (CL) using human annotations directly or by repurposing other annotated datasets. |
| Approach: | They propose to use generative language models to generate CL data using annotated data. |
| Outcome: | The proposed method outperforms the previous best unsupervised method by 1.8 points and SimCSE, a strong supervised baseline by 0.3 points on the semantic text similarity (STS) benchmark. |
VideoCuRL: Video Curriculum Reinforcement Learning with Orthogonal Difficulty Decomposition (2026.acl-long)
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| Challenge: | Reinforcement Learning (RL) is crucial for Video-LLMs with complex spatiotemporal reasoning. |
| Approach: | They propose a framework that decomposes difficulty into two axes in video understanding . they employ efficient, training-free proxies to map data onto a 2D curriculum grid . |
| Outcome: | The proposed framework surpasses strong RL baselines on reasoning and perception tasks. |
Towards Robust Universal Information Extraction: Dataset, Evaluation, and Solution (2025.acl-long)
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| Challenge: | Existing robust benchmark datasets generate only a limited range of perturbations for a single Information Extraction (UIE) task, which fails to evaluate the robustness of UIE models effectively. |
| Approach: | They propose a new benchmark dataset that utilizes Large Language Models to generate more diverse and realistic perturbations across different IE tasks. |
| Outcome: | The proposed model performs better with only 15% of the data and is more robust with other models. |
Empirical Study on Data Attributes Insufficiency of Evaluation Benchmarks for LLMs (2025.coling-main)
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| Challenge: | Existing benchmarks for evaluating large language models neglect key qualitative data attributes that can significantly impact the final rankings of LLMs. |
| Approach: | They propose a framework with three modules designed to assess diversity, redundancy, and difficulty. |
| Outcome: | The proposed framework systematically incorporates diversity, redundancy, and difficulty attributes and shows that they influence the ranking of LLMs. |
KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction (2024.acl-long)
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Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Lixiang Lixiang, Zhilei Hu, Long Bai, Wei Li, Yidan Liu, Pan Yang, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | None. None.. None! |
| Approach: | None. None.. None! |
| Outcome: | None. None. No. : |
SiMFy: A Simple Yet Effective Approach for Temporal Knowledge Graph Reasoning (2023.findings-emnlp)
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| Challenge: | Existing models for temporal knowledge graph reasoning suffer from low training efficiency and insufficient generalization ability. |
| Approach: | They propose a temporal knowledge graph reasoning approach that uses multilayer perceptron to model the structural dependencies of events and adopts a fixed-frequency strategy to incorporate historical frequency during inference. |
| Outcome: | The proposed model achieves state-of-the-art performance with faster convergence speed and better generalization ability. |
ReFT: Reasoning with Reinforced Fine-Tuning (2024.acl-long)
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| Challenge: | Existing approaches to improve the generalization of large language models are using Supervised Fine-Tuning (SFT) this approach does not show sufficient generalization ability because it only relies on the given CoT data. |
| Approach: | They propose to use Chain-of-Thought annotations to train Large Language Models using supervised fine-tuning to improve generalization. |
| Outcome: | The proposed approach outperforms SFT on GSM8K, MathQA, and SVAMP datasets and shows a superior generalization ability. |
Trajectory2Task: Training Robust Tool-Calling Agents with Synthesized Yet Verifiable Data for Complex User Intents (2026.acl-long)
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Ziyi Wang, Yuxuan Lu, Yimeng Zhang, Pei Chen, Ziwei Dong, Jing Huang, Jiri Gesi, Xianfeng Tang, Chen Luo, Qun Liu, Yisi Sang, Hanqing Lu, Manling Li, Jin Lai, Dakuo Wang
| Challenge: | Tool-calling agents are increasingly deployed in real-world customer-facing workflows . but most studies on tool-callers focus on idealized settings with general, fixed, and well-specified tasks. |
| Approach: | They propose a tool-calling agent-based data pipeline that converts trajectories into user-facing tasks with controlled intent adaptations. |
| Outcome: | The proposed pipeline can be used to study tool use under three scenarios. |
Neuronal Insights into LLM Attacks: Targeted Neuron Tuning for Precise and Robust Vulnerability Patching (2026.findings-acl)
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| Challenge: | Existing gradient-based attribution methods are inapplicable to adversarial attacks . et al.: Targeted neuron tuning improves model robustness against jailbreak attacks despite the model's vulnerability to jailbreak. |
| Approach: | They propose a gradient-based method to identify key neurons sensitive to adversarial behaviors in open-ended generation tasks. |
| Outcome: | The proposed method detects key neurons sensitive to adversarial behaviors in open-ended tasks. |
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition (2021.findings-emnlp)
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Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu
| Challenge: | Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence. |
| Approach: | They propose a fast correction model that takes multiple ASR candidates as input for better correction accuracy. |
| Outcome: | The proposed model can reduce the word error rate (WER) with multiple candidates by 3.2% and 2.6%. |
GraphKV: Breaking the Static Selection Paradigm with Graph-Based KV Cache Eviction (2025.emnlp-main)
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| Challenge: | Efficient Key-Value (KV) cache management is essential for processing long text sequences in large language models (LLMs). |
| Approach: | They propose a graph-based framework that redefines token selection for KV cache compression. |
| Outcome: | The proposed framework can be used in existing KV cache eviction methods such as SnapKV and PyramidKV in a plug-and-play manner. |
XY-Tokenizer: Mitigating the Semantic-Acoustic Conflict in Low-Bitrate Speech Codecs (2026.acl-long)
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Yitian Gong, Luozhijie Jin, Kuangwei Chen, Dong Zhang, Ruifan Deng, Xiaogui Yang, Xin Zhang, Zhaoye Fei, Qinyuan Cheng, Shimin Li, Xipeng Qiu
| Challenge: | Existing speech codecs struggle to balance these objectives at low bitrates . XY-Tokenizer achieves stronger semantic alignment than representative semantic-distillation codec . |
| Approach: | They propose a low-bitrate speech codec that aligns discrete speech representations with text while preserving fine-grained acoustic details for reconstruction. |
| Outcome: | The proposed codec outperforms existing low-bitrate speech codecs in speech understanding and generation tasks. |
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios (2022.naacl-demo)
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Xinya Du, Zixuan Zhang, Sha Li, Pengfei Yu, Hongwei Wang, Tuan Lai, Xudong Lin, Ziqi Wang, Iris Liu, Ben Zhou, Haoyang Wen, Manling Li, Darryl Hannan, Jie Lei, Hyounghun Kim, Rotem Dror, Haoyu Wang, Michael Regan, Qi Zeng, Qing Lyu, Charles Yu, Carl Edwards, Xiaomeng Jin, Yizhu Jiao, Ghazaleh Kazeminejad, Zhenhailong Wang, Chris Callison-Burch, Mohit Bansal, Carl Vondrick, Jiawei Han, Dan Roth, Shih-Fu Chang, Martha Palmer, Heng Ji
| Challenge: | Existing methods for event prediction are incomplete and noisy. |
| Approach: | They propose to use news-related event schemas to extract newsworthy events . they build a demo website and include a video demonstrating the framework . |
| Outcome: | The proposed framework can be applied to a wide variety of newsworthy scenarios. |
HiRoPE: Length Extrapolation for Code Models Using Hierarchical Position (2024.acl-long)
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| Challenge: | Existing LLMs are constrained by their pre-trained context lengths, leading to performance issues . elucidating this limitation, we propose a training-free solution to the context length limitation in LLM applications . |
| Approach: | They propose a method that integrates hierarchical rotary position embedding into LLMs without extra training costs. |
| Outcome: | The proposed method improves performance on language modeling and long code completion tasks. |
UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model (2023.findings-emnlp)
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Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, Qin Jin, Liang He, Xin Lin, Fei Huang
| Challenge: | Existing studies for visually-situated language understanding have shown shallow zero-shot visual text recognition ability when fed a low-resolution image with salient text information. |
| Approach: | They propose a model for universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM) their model is jointly finetuned on a wide range of visually situated language understanding tasks via a unified instruction format. |
| Outcome: | The proposed model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks across 5 domains: documents, tables, charts, natural images, and webpage screenshots. |
A New Pipeline for Knowledge Graph Reasoning Enhanced by Large Language Models Without Fine-Tuning (2024.emnlp-main)
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| Challenge: | Conventional knowledge Graph Reasoning models learn the embeddings of KG components over the structure of a KG. |
| Approach: | They propose a pipeline to integrate knowledge from LLMs into KGs without fine-tuning . they propose knowledge alignment, KG reasoning and entity reranking to enhance conventional models . |
| Outcome: | The proposed pipeline can enhance the performance of conventional KGR models in incomplete and general situations. |
Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases (C18-1)
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| Challenge: | Existing methods for identifying semantic type of entities are incomplete even in large knowledge bases. |
| Approach: | They propose an attributed and predictive entity embedding method which can fully utilize various kinds of information comprehensively. |
| Outcome: | Experiments on two real DBpedia datasets show that the proposed method outperforms 8 state-of-the-art methods with 4.0% improvement in Mi-F1 and 5.2% improvement in Ma-F1. |
BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis (2024.emnlp-demo)
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Shuhang Lin, Wenyue Hua, Lingyao Li, Che-Jui Chang, Lizhou Fan, Jianchao Ji, Hang Hua, Mingyu Jin, Jiebo Luo, Yongfeng Zhang
| Challenge: | Recent advances in large language models have demonstrated impressive reasoning capabilities, indicating their potential to serve as the foundation for agents. |
| Approach: | They propose a detailed emulation system that combines large vision-language model and multi-agent system to emulate dynamic interactions between multiple agents over a period of time. |
| Outcome: | The proposed system combines large vision-language model and multi-agent system to emulate dynamic interactions between agents and their environments over a period of time. |
CARD: Cross-modal Agent Framework for Generative and Editable Residential Design (2025.emnlp-main)
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| Challenge: | Architectural design automation has made significant progress, but the complexity of open-world environments makes residential design a challenging task. |
| Approach: | They propose a framework that leverages a system of specialized cross-modal agents to adapt to open-world residential design. |
| Outcome: | The proposed framework enables users to generate and edit residential design without requiring specialized expertise. |
SCCS: Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment (2023.findings-acl)
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Jielin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin
| Challenge: | Existing methods for multimodal summarization ignore the structure and semantics of the whole video and article. |
| Approach: | They propose a semantic-consistent cross-domain summarization model that extracts features from video and article and uses fusion methods to select representative one. |
| Outcome: | The proposed model produces high-quality multimodal summaries on three MSMO datasets. |
Search-o1: Agentic Search-Enhanced Large Reasoning Models (2025.emnlp-main)
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Xiaoxi Li, Guanting Dong, Jiajie Jin, Yuyao Zhang, Yujia Zhou, Yutao Zhu, Peitian Zhang, Zhicheng Dou
| Challenge: | Large reasoning models (LRMs) have demonstrated impressive long stepwise reasoning capabilities through large-scale reinforcement learning. |
| Approach: | They propose a framework that enhances large reasoning models with an agentic retrieval-augmented generation mechanism and a Reason-in-Documents module for refining retrieved documents. |
| Outcome: | The proposed framework enhances LRMs with an agentic retrieval-augmented generation mechanism and Reason-in-Documents module for refining retrieved documents. |
CMQCIC-Bench: A Chinese Benchmark for Evaluating Large Language Models in Medical Quality Control Indicator Calculation (2025.findings-acl)
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Guangya Yu, Yanhao Li, Zongying Jiang, Yuxiong Jin, Li Dai, Yupian Lin, Ruihui Hou, Weiyan Zhang, Yongqi Fan, Qi Ye, Jingping Liu, Tong Ruan
| Challenge: | Medical quality control indicators are essential to assess the qualifications of healthcare institutions for medical services. |
| Approach: | They propose a Chinese electronic medical records-based dataset for MQCIC and propose CF-IR method that disentangles clinical fact verification and inferential rule reasoning actions. |
| Outcome: | The proposed method outperforms Chain-of-Thought methods on 20 representative LLMs, covering general and medical models. |
KnowCoder-X: Boosting Multilingual Information Extraction via Code (2025.findings-acl)
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Yuxin Zuo, Wenxuan Jiang, Wenxuan Liu, Zixuan Li, Long Bai, Hanbin Wang, Yutao Zeng, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | Empirical evidence indicates that Large Language Models exhibit spontaneous cross-lingual alignment in Information Extraction (IE) however, a significant imbalance across languages persists, highlighting an underlying deficiency. |
| Approach: | They propose a code LLM with advanced cross-lingual and multilingual capabilities for universal IE that standardizes the representation of multilingual schemas using Python classes and conducts IE alignment instruction tuning on translated instance prediction task. |
| Outcome: | The proposed model surpasses ChatGPT and SoTA by 30.17% without training in 29 unseen languages and significantly improves cross-lingual IE transferability. |
Selective Temporal Knowledge Graph Reasoning (2024.lrec-main)
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| Challenge: | Existing models cannot abstain from uncertain predictions, which will bring risks in real-world applications. |
| Approach: | They propose to abstain from uncertain future facts by using a confidence estimator . they take both the certainty of the current prediction and the accuracy of historical predictions into account . |
| Outcome: | The proposed abstention mechanism helps existing models make selective predictions instead of indiscriminate ones. |
From Pixels to Personas: Investigating and Modeling Self-Anthropomorphism in Human-Robot Dialogues (2024.findings-emnlp)
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| Challenge: | a recent study shows that robots display human-like characteristics in dialogues . this anthropomorphism raises concerns about the accuracy of AI and its capabilities . |
| Approach: | They propose to use a dataset to analyze self-anthropomorphic and non-self-anthropophilic responses in robots . they propose to combine these two types of responses to create a new category of bot responses . |
| Outcome: | The proposed approach preserves the original dialogues from existing corpora and enhances them with paired responses: self-anthropomorphic and non-self-anthropophilic for each original bot response. |
P²Net: Parallel Pointer-based Network for Key Information Extraction with Complex Layouts (2025.findings-acl)
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Kaiwen Wei, Jie Yao, Jiang Zhong, Yangyang Kang, Jingyuan Zhang, Changlong Sun, Xin Zhang, Fengmao Lv, Li Jin
| Challenge: | Existing methods for key information extraction are based on a limited set of entity categories and fixed layouts. |
| Approach: | They propose a large-scale, human-annotated dataset for key information extraction . it is based on a human-annotated layout and 1,162 entity categories . they propose 'parallel pointer-based network' that leverages implicit relationships . |
| Outcome: | Experiments on widely-used datasets show that the proposed model outperforms state-of-the-art methods while maintaining fast inference speeds. |
Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism (2025.findings-emnlp)
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Zhiwei Wang, Yunji Wang, Zhongwang Zhang, Zhangchen Zhou, Hui Jin, Tianyang Hu, Jiacheng Sun, Zhenguo Li, Yaoyu Zhang, Zhi-Qin John Xu
| Challenge: | Large language models struggle with complex reasoning tasks, such as mathematical problem-solving. |
| Approach: | They constructed a symbolic multi-step reasoning task to investigate the information propagation mechanisms in Transformer models when solving the task through direct answering and Chain-of-Thought (CoT) reasoning. |
| Outcome: | The proposed algorithm improves on 7 multi-step reasoning datasets, while introducing only 132 trainable parameters. |
KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches (2024.findings-emnlp)
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Jiayi Yuan, Hongyi Liu, Shaochen Zhong, Yu-Neng Chuang, Songchen Li, Guanchu Wang, Duy Le, Hongye Jin, Vipin Chaudhary, Zhaozhuo Xu, Zirui Liu, Xia Hu
| Challenge: | Long context capability is a crucial competency for large language models as it mitigates the human struggle to digest long-form texts. |
| Approach: | They propose to evaluate 10+ state-of-the-art approaches for long context-capable LLMs. |
| Outcome: | The proposed methods are compared against 10+ state-of-the-art approaches across seven categories of long context tasks. |
Is the Attention Matrix Really the Key to Self-Attention in Multivariate Long-Term Time Series Forecasting? (2026.acl-long)
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| Challenge: | In multivariate long-term time series forecasting, it is widely believed that the effectiveness of self-attention arises from its attention matrix. |
| Approach: | They propose a multi-branch MLP that isolates the ‘multi-brain mapping with element-wise operation’ structure from the Transformer and shows that it achieves competitive performance. |
| Outcome: | The proposed model outperforms three classic and three latest Transformer models and shows that it achieves competitive performance. |
LongMP-Bench: A Benchmark for Multimodal Persona Understanding in Long-Term Dialogues (2026.findings-acl)
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| Challenge: | Existing datasets suffer from limited persona diversity and static, overly simplified settings, making them insufficient for capturing the complexity of real-world interactions. |
| Approach: | They propose a benchmark to evaluate models' ability to understand evolving user personas within long-term multimodal dialogues by using a dataset that contains long conversations from 150 users. |
| Outcome: | The proposed benchmark aims to assess models' ability to track persona evolution, integrate visual and textual inputs, and apply persona understanding in realistic dialogue scenarios. |
Dynamic Model-Bank Test-Time Adaptation for Automatic Speech Recognition (2025.emnlp-main)
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| Challenge: | Existing ASR TTA methods struggle with instability under continual and long-term distribution shifts. |
| Approach: | They propose a continuous adaptive model-bank framework that adapts to domain shifts in ASR test-time scenarios. |
| Outcome: | Experiments on diverse, continuously shifting ASR benchmarks show that DMSUTA outperforms existing continual TTA baselines. |
Aspect-Category Enhanced Learning with a Neural Coherence Model for Implicit Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a major research topic in NLP since social networking services have increased . but the recognition of implicit sentiments that do not contain obvious opinion words remains unexplored . elcom captures document-level coherence by using contrastive learning and sentence-level by a hypergraph . |
| Approach: | They propose aspect-category enhanced learning with a neural coherence model . it captures document-level coherency by contrastive learning and sentence-level by a hypergraph . |
| Outcome: | The proposed model captures document-level coherence by using contrastive learning and sentence-level by a hypergraph to mine opinions from explicit sentences to aid implicit sentiment classification. |
Assist Non-native Viewers: Multimodal Cross-Lingual Summarization for How2 Videos (2022.emnlp-main)
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| Challenge: | Existing multimodal summarization methods are limited to monolingual videos . a proposed task aims to generate cross-lingual summaries from multimodal inputs . |
| Approach: | They propose a task to generate cross-lingual summaries from multimodal inputs of videos . they propose fusion network that integrates multimodal and cross-linguistic information . |
| Outcome: | The proposed task outperforms existing methods on a reorganized How2 dataset on the reorganized How2 data set. |
RIPRAG: Hack a Black-box Retrieval-Augmented Generation Question-Answering System with Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing methods to generate RAG documents require knowledge of the target RAG system’s internal composition and implementation details, whereas black-box methods are unable to utilize interactive information. |
| Approach: | They propose a RIPRAG attack framework that treats the target RAG system as a black box and leverages a Reinforcement Learning from Black-box Feedback (RLBF) method to optimize the generation model for poisoned documents. |
| Outcome: | The proposed method achieves an attack success rate (ASR) improvement of up to 0.72 compared to baseline methods. |
MdEval: Massively Multilingual Code Debugging (2026.findings-acl)
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Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li
| Challenge: | Existing benchmarks primarily focus on Python and are limited in terms of language diversity. |
| Approach: | They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions. |
| Outcome: | The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task. |
To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning. (2022.coling-1)
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| Challenge: | Reasoning and knowledge-related skills are considered as fundamental skills for natural language understanding (NLU) tasks. |
| Approach: | They propose a method to diagnose correlations between an NLU dataset and a specific skill. |
| Outcome: | The proposed method is able to diagnose correlations between dataset and logical reasoning skill on 8 MRC and 3 NLI datasets. |
Event Schema Induction with Double Graph Autoencoders (2022.naacl-main)
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| Challenge: | Experimental results show that a new method for learning event schemas from historical events is effective. |
| Approach: | They propose a new event schema induction framework which captures global dependencies among nodes in event graphs. |
| Outcome: | Experimental results show that the proposed model can learn event schemas with global consistency. |
Chain-of-Thought Tuning: Masked Language Models can also Think Step By Step in Natural Language Understanding (2023.emnlp-main)
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| Challenge: | Chain-of-Thought (CoT) is a technique that guides large language models to decompose complex tasks into multi-step reasoning processes. |
| Approach: | They propose a two-step reasoning framework based on prompt tuning to implement step-by-step thinking for MLMs on NLU tasks. |
| Outcome: | The proposed framework outperforms baselines and achieves state-of-the-art performance on two NLU tasks. |
MultiConIR: Towards Multi-Condition Information Retrieval (2025.findings-emnlp)
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Xuan Lu, Sifan Liu, Bochao Yin, Yongqi Li, Xinghao Chen, Hui Su, Yaohui Jin, Wenjun Zeng, Xiaoyu Shen
| Challenge: | MultiConIR is a benchmark designed to evaluate retrieval and reranking models under nuanced multi-condition query scenarios. |
| Approach: | They propose a benchmark to evaluate retrieval and reranking models under nuanced multi-condition query scenarios. |
| Outcome: | The proposed benchmark evaluates retrieval and reranking models under nuanced multi-condition query scenarios across five domains. |
Benchmarking Long-Context Language Models on Long Code Understanding (2025.acl-long)
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Jia Li, Xuyuan Guo, Lei Li, Kechi Zhang, Ge Li, Jia Li, Zhengwei Tao, Fang Liu, Chongyang Tao, Yuqi Zhu, Zhi Jin
| Challenge: | Currently, long-context language models are limited by the lack of a rigorous evaluation framework for long code understanding. |
| Approach: | They propose to use a long code understanding benchmark LongCodeU to evaluate LCLMs' long code comprehension ability for practical applications. |
| Outcome: | The proposed benchmarks show that current LCLMs are limited in their long code understanding ability, particularly when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows. |
How Do LLMs Acquire New Knowledge? A Knowledge Circuits Perspective on Continual Pre-Training (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have exceptional capabilities in knowledge-intensive tasks . however, they struggle with knowledge updates due to dynamic nature of world knowledge . |
| Approach: | They propose to identify computational subgraphs that facilitate knowledge storage and processing . they also identify a phase shift from formation to optimization in LLMs . |
| Outcome: | The proposed model can capture factual knowledge from pre-training corpus and encapsulate it as extensive parametric knowledge. |
Nested Event Extraction upon Pivot Element Recognition (2024.lrec-main)
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Weicheng Ren, Zixuan Li, Xiaolong Jin, Long Bai, Miao Su, Yantao Liu, Saiping Guan, Jiafeng Guo, Xueqi Cheng
| Challenge: | Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. |
| Approach: | They propose a new model that extracts nested events mainly based on recognizing PEs. |
| Outcome: | The proposed model can extract nested events based on recognizing PEs . it incorporates information from both event types and argument roles to improve performance . |
De-Confounded Variational Encoder-Decoder for Logical Table-to-Text Generation (2021.acl-long)
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| Challenge: | Logical table-to-text generation is challenging where deep learning models capture surface-level spurious correlations rather than the causal relationships between the table x and the sentence y. |
| Approach: | They propose to use variational inference to estimate the confounders in the latent space and cooperate with the causal intervention based on Pearl’s do-calculus to alleviate the spurious correlations. |
| Outcome: | The proposed model outperforms baselines and achieves new state-of-the-art performance on two logical table-to-text datasets in terms of logical fidelity. |
Chain-of-Specificity: Enhancing Task-Specific Constraint Adherence in Large Language Models (2025.coling-main)
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| Challenge: | Existing approaches to enhancing large language models fail to emphasize specific constraints and unlock the underlying knowledge. |
| Approach: | They propose a method that emphasizes specific constraints and unlocks knowledge within LLMs by iteratively emphasising on specific constraints. |
| Outcome: | The proposed method outperforms existing methods in enhancing generated content, especially in terms of specificity. |
Efficient KL Divergence Estimation via Truncated Top-K Integration for Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for estimating KL divergence using only top-k tokens suffer from high variance or systematic bias. |
| Approach: | They propose a top-k Importance-weighted KL Estimator that exploits the Zipfian structure of language model distributions by integrating only the top-K tokens. |
| Outcome: | The proposed estimator outperforms existing estimators on multiple benchmarks while exhibiting lower variance. |
MIND Your Reasoning: A Meta-Cognitive Intuitive-Reflective Network for Dual-Reasoning in Multimodal Stance Detection (2026.acl-long)
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| Challenge: | Existing methods operate by learning to fuse modalities, leading to frequent misjudgments. |
| Approach: | They propose a paradigm shift from *learning to fuse* to *learning the reason's process' inspired by the dual-process theory of human cognition, MIND operationalizes a self-improving loop. |
| Outcome: | The proposed model significantly outperforms baseline models and exhibits strong generalization. |
Graph Attention Network with Memory Fusion for Aspect-level Sentiment Analysis (2020.aacl-main)
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| Challenge: | Recent studies ignored the syntactic relationship between the aspect and its corresponding context words, leading the model to focus on syntaktically unrelated words mistakenly. |
| Approach: | They propose to extend the graph convolutional network by assigning different weights to edges of connected words. |
| Outcome: | The proposed method can improve on five datasets showing that it learns and exploits multiword relations and draws different weights of words to improve performance. |
KEEP CHATTING! An Attractive Dataset for Continuous Conversation Agents (2024.findings-acl)
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| Challenge: | Existing works about persona dialogue such as PersonaChat have greatly facilitated the chatbot with configurable and persistent personalities. |
| Approach: | They propose to collect a dataset called ContinuousChat and rewrite it in style-specific ways to increase users' willingness to continue chatting. |
| Outcome: | The proposed model increases users' willingness to continue talking to the chatbot by increasing their personas to detailed-personas through experiences, daily life, future plans, or interesting stories. |
Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction (2021.acl-long)
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| Challenge: | Existing methods to extract event records from text decompose complex structure prediction task into multiple subtasks. |
| Approach: | They propose a sequence-to-structure generation paradigm that can extract events from text . they propose unified event extraction, constrained decoding algorithm and curriculum learning algorithm . |
| Outcome: | The proposed method can achieve competitive performance using record-level annotations in both supervised learning and transfer learning settings. |
Internal Value Alignment in Large Language Models through Controlled Value Vector Activation (2025.acl-long)
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| Challenge: | Existing LLMs do not possess consistent values, but many have been developed to align them at the behavioral level, including supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). |
| Approach: | They propose a Controlled Value Vector Activation method that directly aligns the internal values of Large Language Models by interpreting how a value is encoded in their latent representations. |
| Outcome: | The proposed method achieves highest success rate across 10 basic values without hurting model performance and fluency, and ensures target values even with opposite and potentially malicious input prompts. |
Implicit Discourse Relation Recognition using Neural Tensor Network with Interactive Attention and Sparse Learning (C18-1)
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| Challenge: | Existing methods for implicit discourse relation recognition ignore bidirectional interactions between two arguments and sparsity of pair patterns. |
| Approach: | They propose a neural Tensor network framework with interactive attention and sparse learning for implicit discourse relation recognition. |
| Outcome: | The proposed framework is effective on PDTB and can be used in text summarization, conversation system and so on. |
AudioStealer: Extracting Audio Prompts via Shapley Value-Guided Query Search (2026.findings-acl)
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| Challenge: | prompt stealing is a new form of attack that aims to reconstruct high-value prompts that guide music generation. |
| Approach: | They propose a method to steal music prompts from audio domains using a black-box attack framework. |
| Outcome: | The proposed method recovers prompts with high textual consistency to the ground truth while maintaining strong perceptual similarity to the target recordings. |
PACHAT: Persona-Aware Speech Assistant for Multi-party Dialogue (2025.emnlp-main)
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| Challenge: | Extensive research on spoken dialogue systems has advanced the development of intelligent voice assistants, but integration of role information within speech remains an underexplored area. |
| Approach: | They propose a language-based spoken dialogue system that integrates role information within speech to generate contextually appropriate responses. |
| Outcome: | The proposed architecture achieves speaker-specific responses, character understanding, and the generation of targeted replies in multi-party dialogue scenarios, surpassing existing spoken dialogue systems. |
ReStyle-TTS: Relative and Continuous Style Control for Zero-Shot Speech Synthesis (2026.findings-acl)
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| Challenge: | Recent controllable zero-shot text-to-speech systems can synthesize speech for unseen speakers from a short reference audio clip, but they also inherit the speaking style present in the reference. |
| Approach: | They propose a framework that enables continuous and reference-relative style control in zero-shot text-to-speech systems by combining style-specific LoRAs with Orthogonal LoRA Fusion. |
| Outcome: | The proposed framework reduces the model's dependence on reference style while preserving text fidelity while maintaining intelligibility and speaker timbre. |
WordArt Designer: User-Driven Artistic Typography Synthesis using Large Language Models (2023.emnlp-industry)
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Jun-Yan He, Zhi-Qi Cheng, Chenyang Li, Jingdong Sun, Wangmeng Xiang, Xianhui Lin, Xiaoyang Kang, Zengke Jin, Yusen Hu, Bin Luo, Yifeng Geng, Xuansong Xie
| Challenge: | Existing typography solutions lack adaptability, creativity, and computational efficiency. |
| Approach: | They propose a user-driven framework for artistic typography synthesis based on the Large Language Model (LLM) the LLM Engine interprets user inputs and generates actionable prompts for the other modules, transforming abstract concepts into tangible designs. |
| Outcome: | The proposed framework incorporates four key modules: the LLM Engine, SemTypo, StyTyPo, and TexTyPO. |
Web Sitemap Knowledge Can Enhance Autonomous Browsing (2026.findings-acl)
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Yuyao Zhang, Hongyu Lu, Jiajie Jin, Hongjin Qian, Shiyu Li, Zhao Yang, Yutao Zhu, Ji-Rong Wen, Zhicheng Dou
| Challenge: | Existing web agents suffer from limited robustness, efficiency and task success due to lack of structural understanding of websites and lack of browsing priors in pre-trained models. |
| Approach: | They propose an agent-oriented sitemap protocol that integrates structured website knowledge into web agents. |
| Outcome: | The proposed agent-oriented sitemap improves robustness, efficiency and effectiveness without extra training. |
OS-Symphony: A Holistic Framework for Robust and Generalist Computer-Using Agents (2026.acl-long)
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Bowen Yang, Kaiming Jin, Zhenyu Wu, Zhaoyang Liu, Qiushi Sun, Zehao Li, JingJing Xie, Zhoumianze Liu, Fangzhi Xu, Kanzhi Cheng, Yian Wang, Qingyun Li, Yu Qiao, Zun Wang, Zichen Ding
| Challenge: | Vision-Language Models (VLMs) lack visual-aware tutorial retrieval and historical visual context curation and pruning. |
| Approach: | They propose a framework that integrates an orchestrator and a Reflection-Memory Agent for robust automation. |
| Outcome: | Experimental results show that OS-Symphony delivers substantial performance gains across model scales. |
Pan More Gold from the Sand: Refining Open-domain Dialogue Training with Noisy Self-Retrieval Generation (2022.coling-1)
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| Challenge: | Existing methods for generating open-domain dialogue systems underutilize training data. |
| Approach: | They propose a retrieval-generation training framework that takes advantage of heterogeneous training data by considering them as "evidence" they use BERTScore retrieval framework which gives better qualities of the training data, they show . |
| Outcome: | The proposed method performs well on zero-shot experiments and is more robust to real-world data. |
ChemAmp: Amplified Chemistry Tools via Composable Agents (2026.findings-acl)
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Zhucong Li, Powei Chang, Jin Xiao, Zhijian Zhou, Qianyu He, Jiaqing Liang, Fenglei Cao, Xu Yinghui, Yuan Qi
| Challenge: | LLM-based agents are powerful tools for automating complex scientific workflows, especially in chemistry, but their single-task performance is limited by tool constraints. |
| Approach: | They propose a framework that optimizes the collective capabilities of specialized tools by dynamic coordination within individual tasks. |
| Outcome: | The proposed framework outperforms chemistry-specialized models, generalist LLMs, and agent systems with tool orchestration. |
Solver-Independent Automated Problem Formulation via LLMs for High-Cost Simulation-Driven Design (2026.findings-acl)
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| Challenge: | Existing approaches to translating ambiguous design requirements into a mathematical optimization formulation are expensive and time-consuming. |
| Approach: | They propose a solver-independent framework that converts engineers’ natural language requirements into executable optimization models. |
| Outcome: | The proposed framework outperforms existing methods in the accuracy of requirement formalization and quality of resulting radiation efficiency curves on antenna design. |
Can We Trust the Performance Evaluation of Uncertainty Estimation Methods in Text Summarization? (2024.emnlp-main)
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| Challenge: | Text summarization is a key natural language generation task, but the high cost of inaccurate summaries raises concerns about the reliability of uncertainty estimation on text summarisation (UE-TS) evaluation methods. |
| Approach: | They propose a UE-TS benchmark that evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets. |
| Outcome: | The proposed benchmark evaluates the uncertainty estimation capabilities of two large language models and one pre-trained language model on three datasets, with human-annotation analysis incorporated where applicable. |
Trigger is Not Sufficient: Exploiting Frame-aware Knowledge for Implicit Event Argument Extraction (2021.acl-long)
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| Challenge: | Existing methods to extract event arguments focus on learning pair-wise information between arguments and the given trigger. |
| Approach: | They propose a framework to extract event-related arguments from a given event frame-level scope. |
| Outcome: | The proposed method achieves state-of-the-art on the RAMS dataset. |
RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation (2025.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated remarkable performance across a wide range of downstream tasks. |
| Approach: | They propose a framework that leverages a critic-guided agentic workflow to improve RAG capabilities autonomously. |
| Outcome: | The proposed framework improves RAG capabilities autonomously by leveraging a critic-guided agentic workflow. |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
Navigating Ethical Challenges in NLP: Hands-on strategies for students and researchers (2025.acl-tutorials)
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Luciana Benotti, Fanny Ducel, Karën Fort, Guido Ivetta, Zhijing Jin, Min-Yen Kan, Seunghun J. Lee, Minzhi Li, Margot Mieskes, Adriana Pagano
| Challenge: | This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues . participants will gain practical experience on when to flag a paper for ethics review . |
| Approach: | This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues . participants will gain practical experience on when to flag a paper for ethics review . |
| Outcome: | This tutorial will equip participants with basic guidelines for thinking deeply about ethical issues . participants will gain practical experience on when to flag a paper for ethics review . |
Discovery and Reinforcement of Tool-Integrated Reasoning Chains via Rollout Trees (2026.acl-long)
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| Challenge: | Existing approaches to augment Large Language Models (LLMs) with computational capabilities have focused on short Chain-of-thought (CoT) integrating tool-use into long CoT remains underexplored due to the scarcity of training data and the challenge of integrating it without compromising the model’s intrinsic long-chain reasoning. |
| Approach: | They propose a framework that enables spontaneous tool-use during long CoT reasoning without additional human annotation. |
| Outcome: | Experiments on AIME and GPQA-Diamond show that DART significantly outperforms existing methods, successfully harmonizing tool execution with long CoT reasoning. |
Sign2Vis: Automated Data Visualization from Sign Language (2025.findings-acl)
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| Challenge: | Existing methods to translate natural language descriptions into visualization queries focus on spoken languages, not sign languages. |
| Approach: | They propose a sign language interface that enables the DHH community to engage more fully with data analysis. |
| Outcome: | The proposed interface can be used by the deaf and hard-of-hearing community. |
Semantic Structure Enhanced Event Causality Identification (2023.acl-long)
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| Challenge: | Existing methods for Event Causality Identification (ECI) capture implicit associations between events, which are difficult because they lack the ability to understand the associations between two events. |
| Approach: | They propose a model that captures the implicit associations between two events and integrates the event-centric structure information into a GNN-based event aggregator. |
| Outcome: | The proposed model improves on three widely used datasets showing that it integrates event-centric and event-associated semantic elements and captures event associations. |
Contrastive Token-Wise Meta-Learning for Unseen Performer Visual Temporal-Aligned Translation (2023.findings-acl)
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| Challenge: | a novel generalization framework for visual temporal-aligned translation is proposed to transfer recognition skills to unseen performers . ambiguity in the visual sequence can hinder current methods for visual language translation . |
| Approach: | They propose a generalizable framework to transfer recognition skills to unseen performers . they use visual temporal-aligned translation to generate multiple words autoregressively . |
| Outcome: | The proposed framework is generalized to transfer recognition skills to unseen performers . it is compared with existing methods on lipreading and fingerspelling datasets . |
Towards Event Extraction with Massive Types: LLM-based Collaborative Annotation and Partitioning Extraction (2025.emnlp-main)
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| Challenge: | Event Extraction (EE) is a long-standing target, but lacks an efficient and effective annotation framework to construct the corresponding datasets. |
| Approach: | They propose an LLM-based collaborative annotation framework that refines annotations of triggers from distant supervision and carries out argument annotation. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on the largest EE dataset to date . it achieves the F1 scores of 90% and 85.3% on the human-annotated test set . |
Multi-Scale Spectral Selection and Entropy-Guided Uncertainty Fusion for Multimodal Rumor Detection (2026.findings-acl)
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| Challenge: | Existing methods for multimodal content detection fail to capture cross-modal semantic inconsistencies and ignore inherent noise in multimodal features. |
| Approach: | They propose a multimodal rumor detection method based on a frequency domain spectral selection method and entropy-guided uncertainty fusion method to capture cross-modal semantic inconsistencies. |
| Outcome: | The proposed method outperforms state-of-the-art methods in multimodal rumor detection . it shows stronger detection capability and robustness on multiple datasets . |
MLeVLM: Improve Multi-level Progressive Capabilities based on Multimodal Large Language Model for Medical Visual Question Answering (2024.findings-acl)
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Dexuan Xu, Yanyuan Chen, Jieyi Wang, Yue Huang, Hanpin Wang, Zhi Jin, Hongxing Wang, Weihua Yue, Jing He, Hang Li, Yu Huang
| Challenge: | Existing MVQA models ignore multi-level progressive capabilities due to unspecific data and plain architecture. |
| Approach: | They propose a multi-level visual language model for medical visual question answering (MVQA) which covers multi- level questions and answers as well as reasoning processes from visual clues to semantic cognition. |
| Outcome: | The proposed model outperforms existing medical multimodal large language models on a multi-level instruction dataset and a feature alignment module. |
S2-MAD: Breaking the Token Barrier to Enhance Multi-Agent Debate Efficiency (2025.naacl-long)
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Yuting Zeng, Weizhe Huang, Lei Jiang, Tongxuan Liu, XiTai Jin, Chen Tianying Tiana, Jing Li, Xiaohua Xu
| Challenge: | Large language models exhibit limitations when handling complex mathematical reasoning and logical inference tasks. |
| Approach: | They propose a sparsification strategy to reduce token costs within Multi-agent Debate (MAD) this strategy minimizes ineffective exchanges of information and unproductive discussions among agents . |
| Outcome: | The proposed approach reduces token costs by up to 94.5% while maintaining performance degradation below 2.0%. |
Med-MoE: Mixture of Domain-Specific Experts for Lightweight Medical Vision-Language Models (2024.findings-emnlp)
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| Challenge: | Recent advances in multimodal large language models have seen remarkable progress for medical decision-making, however, they are designated for specific classification or generative tasks and require model training or finetuning on large-scale datasets with sizeable parameters and tremendous computing. |
| Approach: | They propose a framework that tackles discriminative and generative multimodal medical tasks using multimodal alignment, instruction tuning and routing. |
| Outcome: | The proposed model can achieve superior performance to or on par with state-of-the-art baselines while only requiring 30%-50% of activated model parameters. |
Focused-DPO: Enhancing Code Generation Through Focused Preference Optimization on Error-Prone Points (2025.findings-acl)
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| Challenge: | Current code generation models produce errors concentrated at specific error-prone points, affecting accuracy of code. |
| Approach: | They propose a framework that focuses preference optimization on error-prone areas . focused-DPO improves the accuracy and reliability of code generation by reducing common errors . |
| Outcome: | The proposed framework improves code generation by focusing on error-prone areas. |
Saber: Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model in Code Generation (2026.acl-long)
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| Challenge: | Diffusion language models (DLMs) offer advantages in parallel generation and bidirectional context modeling, but they face a critical trade-off between inference speed and output quality for tasks with strict structural constraints such as code generation. |
| Approach: | They propose an efficient sampling algorithm that reduces the number of tokens unmasked per step based on the model’s evolving confidence. |
| Outcome: | The proposed method improves Pass@1 accuracy by 1.9% while achieving 251.4% inference speedup. |
GOME: Grounding-based Metaphor Binding With Conceptual Elaboration For Figurative Language Illustration (2024.emnlp-main)
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| Challenge: | Existing Large Language Models (LLMs) and multimodal models are unable to illustrate figurative language based on literal objects, ignoring the underlying groundings and associations across disparate metaphorical domains. |
| Approach: | They propose a grounding-based method for metaphor illustration that integrates metaphorical knowledge into systematic instructions for existing large language models. |
| Outcome: | The proposed method is superior to existing LLMs, diffusion models, or their direct collaboration. |
TrustAgent: Towards Safe and Trustworthy LLM-based Agents (2024.findings-emnlp)
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| Challenge: | Existing LLMs are primarily used for simple text-related tasks, but LLM-based agents can undertake more complex tasks that require planning and interaction with the physical world and humans. |
| Approach: | They propose an Agent-Constitution-based agent framework with a particular focus on improving the LLM-based agents' safety. |
| Outcome: | The proposed framework can enhance an LLM agent’s safety across multiple domains by identifying and mitigating potential dangers during the planning process. |
OS-Genesis: Automating GUI Agent Trajectory Construction via Reverse Task Synthesis (2025.acl-long)
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Qiushi Sun, Kanzhi Cheng, Zichen Ding, Chuanyang Jin, Yian Wang, Fangzhi Xu, Zhenyu Wu, Chengyou Jia, Liheng Chen, Zhoumianze Liu, Ben Kao, Guohao Li, Junxian He, Yu Qiao, Zhiyong Wu
| Challenge: | Graphical User Interface (GUI) agents powered by Vision-Language Models (VLMs) have demonstrated human-like computer control capability. |
| Approach: | They propose a GUI data synthesis pipeline that reverse engineers GUI trajectory construction process by executing pre-defined tasks. |
| Outcome: | The proposed GUI data synthesis pipeline overcomes the bottlenecks of previous methods that rely on pre-defined tasks and limited data diversity. |
CTFN: Hierarchical Learning for Multimodal Sentiment Analysis Using Coupled-Translation Fusion Network (2021.acl-long)
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| Challenge: | Existing methods for multimodal sentiment analysis require all modalities as input, thus are sensitive to missing modality at predicting time. |
| Approach: | They propose to model bi-direction interplay via couple learning and exploit multiple bi-directional translations to exploit multimodal fusion embeddings. |
| Outcome: | The proposed framework achieves state-of-the-art or often competitive performance on two multimodal benchmarks with extensive ablation studies. |
Persona-E²: A Human-Grounded Dataset for Personality-Shaped Emotional Responses to Textual Events (2026.acl-long)
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Yuqin Yang, Haowu Zhou, Haoran Tu, Zhiwen Hui, Shiqi Yan, HaoYang Li, Dong She, Xianrong Yao, Yang Gao, Zhanpeng Jin
| Challenge: | A critical bottleneck is the lack of ground-truth human data to link personality traits to emotional shifts. |
| Approach: | They propose a large-scale dataset to capture reader-based emotional variations across news, social media, and life narratives. |
| Outcome: | The proposed model captures reader-based emotional variations across news, social media, and life narratives. |
G2S: A General-to-Specific Learning Framework for Temporal Knowledge Graph Forecasting with Large Language Models (2025.findings-acl)
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| Challenge: | Recent studies have introduced Large Language Models (LLMs) for this task to enhance the models’ generalization abilities. |
| Approach: | They propose a General-to-Specific learning framework that disentangles the learning processes of two kinds of knowledge in a temporal temporal structure. |
| Outcome: | The proposed framework disentangles the learning processes of the above two kinds of knowledge and improves their generalization abilities. |
TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation (2024.findings-acl)
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Xize Cheng, Rongjie Huang, Linjun Li, Zehan Wang, Tao Jin, Aoxiong Yin, Chen Feiyang, Xinyu Duan, Baoxing Huai, Zhou Zhao
| Challenge: | Existing methods for talking head translation rely on cascading, resulting in delays and cascadic errors. |
| Approach: | They propose a model for talking head translation, TransFace, which can translate audio-visual speech into audio-visual speech in other languages. |
| Outcome: | The proposed model can translate audio-visual speech into audio-visual speech in other languages. |
Rethinking Repetition Problems of LLMs in Code Generation (2025.acl-long)
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| Challenge: | Recent studies have focused on content repetition, but structural repetition is a more prevalent problem in code generation. |
| Approach: | They propose a decoding approach that eliminates repetition problems in code generation by identifying grammar rules and strategically decaying the likelihood of critical tokens that contribute to repetitions. |
| Outcome: | The proposed approach outperforms baselines and humanEval benchmarks on CodeRepetEval dataset and MBPP benchmarks, effectively reducing repetitions and enhancing the quality of generated code. |
D-RAG: Differentiable Retrieval-Augmented Generation for Knowledge Graph Question Answering (2025.emnlp-main)
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Guangze Gao, Zixuan Li, Chunfeng Yuan, Jiawei Li, Wu Jianzhuo, Yuehao Zhang, Xiaolong Jin, Bing Li, Weiming Hu
| Challenge: | Existing approaches to Knowledge Graph Question Answering (KGQA) use Retrieval-Augmented Generation (RAG) but subgraph selection process is non-differentiable, preventing end-to-end training of the retriever and the generator. |
| Approach: | They propose a Differentiable RAG approach that optimizes the retriever and the generator for KGQA. |
| Outcome: | The proposed approach outperforms state-of-the-art approaches on WebQSP and CWQ. |
ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization (2025.findings-emnlp)
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Zhensheng Jin, Xinze Li, Yifan Ji, Chunyi Peng, Zhenghao Liu, Qi Shi, Yukun Yan, Shuo Wang, Furong Peng, Ge Yu
| Challenge: | Existing methods to train LLMs suffer from overthinking, leading to lengthy reasoning traces . Existing approaches to train large language models suffer from this problem . |
| Approach: | They propose a method to combine multiple reasoning chains for training LLMs . they use stepwise exploration and long-short switched sampling to evaluate reasoning paths . |
| Outcome: | The proposed method reduces reasoning lengths by approximately 30-50% . it also maintains or improves reasoning accuracy compared to baselines . |
CDA: A Contrastive Data Augmentation Method for Alzheimer’s Disease Detection (2023.findings-acl)
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| Challenge: | Existing methods for detecting AD are challenging and time-consuming due to lack of data and generalizability of the models. |
| Approach: | They propose a contrastive data augmentation method which simulates the cognitive impairment of a patient by randomly deleting a proportion of text from the transcript to create negative samples. |
| Outcome: | The proposed method achieves the best performance among language-based models on the benchmark ADReSS Challenge dataset. |
Rethinking the Multimodal Correlation of Multimodal Sequential Learning via Generalizable Attentional Results Alignment (2024.acl-long)
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| Challenge: | Existing studies have focused on the alignment of multimodal sequential learning using transformers. |
| Approach: | They propose a constrained scheme to align the multiple attentional results from both local and global perspectives. |
| Outcome: | The proposed scheme could align the multiple attentional results from both local and global perspectives, making the information capture more efficient. |
Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents (2026.findings-acl)
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Miao Su, Yucan Guo, Zhongni Hou, Long Bai, Zixuan Li, Yufei Zhang, Guojun Yin, Wei Lin, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing methods focus on point-wise memory, losing durative information that captures persistent states and evolving patterns. |
| Approach: | They propose a memory framework that models semantic time for point-wise memory and supports the construction and utilization of durative memory. |
| Outcome: | Experiments on LongMemEval and LoCoMo show that the proposed method outperforms existing methods and achieves up to 12.2% improvement in accuracy. |
M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue Database (2022.acl-long)
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| Challenge: | Existing data resources to support multimodal affective analysis in dialogues are limited in scale and diversity. |
| Approach: | They propose a multimodal multi-scene multi-label Emotional Dialogue dataset, M3ED, which contains 990 dyadic emotional dialogues from 56 different TV series. |
| Outcome: | The proposed dataset contains 990 dyadic emotional dialogues from 56 different TV series, a total of 9,082 turns and 24,449 utterances. |
Traces in the Brain: Neural Evidence for Syntactic Movement in English and Chinese (2026.findings-acl)
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Yuhan Huang, Zhengwu Ma, Yuqi Jin, Beth Chan, Zheng Shen, Jackie Yan-Ki Lai, John T. Hale, Jixing Li
| Challenge: | Syntactic movement is a core concept in generative linguistics to account for word-order variation and long-distance dependencies. |
| Approach: | They annotated every sentence in the audiobook The Little Prince using X-bar style tree annotations. |
| Outcome: | The proposed model shows that deep structure significantly predicts neural responses in English but not in Chinese. |
Integrating Deep Event-Level and Script-Level Information for Script Event Prediction (2021.emnlp-main)
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| Challenge: | Existing studies only consider a single event sequence corresponding to one common protagonist. |
| Approach: | They propose a Transformer-based model which integrates deep event-level and script-level information for script event prediction. |
| Outcome: | The proposed model is superior to existing models on the New York Times corpus . it utilizes rich information in the text to obtain more comprehensive representations . |
To Intervene or Not: Guiding Inference-time Alignment with Probabilistic Model Blending (2026.findings-acl)
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| Challenge: | Existing approaches to inference-time alignment are expensive and only offer guidances during output generation. |
| Approach: | They propose an inference-time alignment framework that shifts from binary decisions to creating hybrid distributions integrating both models’ knowledge. |
| Outcome: | The proposed framework reduces the number of inference-time alignment interventions and improves performance on challenging model pairs. |
Text Classification by Contrastive Learning and Cross-lingual Data Augmentation for Alzheimer’s Disease Detection (2020.coling-main)
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| Challenge: | Existing methods for AD detection are too expensive and time-consuming to cover all potential patients. |
| Approach: | They propose a contrastive learning method to obtain effective text representations based on monolingual embeddings of BERT and a cross-lingual data augmentation method by building autoencoders to learn the text representation shared by both languages. |
| Outcome: | The proposed method outperforms other methods on a Mandarin AD corpus and achieves 81.6% detection accuracy. |
Diagnosing the First-Order Logical Reasoning Ability Through LogicNLI (2021.emnlp-main)
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| Challenge: | Existing studies have focused on diagnosing LMs' reasoning abilities in natural language understanding tasks. |
| Approach: | They propose a diagnostic method for first-order logic reasoning with a proposed benchmark, LogicNLI. |
| Outcome: | The proposed method disentangles the target FOL reasoning from commonsense inference and can be used to diagnose LMs from four perspectives: accuracy, robustness, generalization, and interpretability. |
ReasonRec: A Reasoning-Augmented Multimodal Agent for Unified Recommendation (2026.findings-acl)
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Yihua Zhang, Mingfu Liang, Jiyan Yang, Rong Jin, Wen-Yen Chen, Yiping Han, Huayu Li, Buyun Zhang, Liang Luo, Luke Simon, Sijia Liu, Tianlong Chen, Xi Liu
| Challenge: | Recent advances in multimodal recommenders lack explicit reasoning and self-awareness of uncertainty. |
| Approach: | They propose a reasoning-augmented multimodal agent structured around a three-stage explicit reasoning pipeline. |
| Outcome: | The proposed agent improves ranking metrics and performance on four standard recommendation tasks across five real-world datasets. |
LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference (2024.findings-emnlp)
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| Challenge: | Long-context Multimodal Large Language Models (MLLMs) require substantial computational resources for inference . the growth of their multimodal Key-Value (KV) cache challenges memory and time efficiency. |
| Approach: | They propose a fine-tuning-free approach that efficiently reduces the multimodal KV cache size while maintaining performance comparable to a full cache. |
| Outcome: | The proposed method reduces the multimodal KV cache size while maintaining performance comparable to a full cache. |
DynamicNER: A Dynamic, Multilingual, and Fine-Grained Dataset for LLM-based Named Entity Recognition (2025.emnlp-main)
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Hanjun Luo, Yingbin Jin, Yiran Wang, Xinfeng Li, Tong Shang, Xuecheng Liu, Ruizhe Chen, Kun Wang, Hanan Salam, Qingsong Wen, Zuozhu Liu
| Challenge: | Existing datasets designed for Named Entity Recognition methods are inadequate for LLMs. |
| Approach: | They propose a dataset that is multilingual and multi-granular and enables LLMs to be applied to Named Entity Recognition methods. |
| Outcome: | The proposed dataset is multilingual and multi-granular, covering 8 languages and 155 entity types, with corpora spanning a diverse range of domains. |
WebQuality: A Large-scale Multi-modal Web Page Quality Assessment Dataset with Multiple Scoring Dimensions (2025.naacl-long)
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| Challenge: | Existing studies on web page quality assessment neglect the aspect of web page content. |
| Approach: | They propose a Chinese dataset for web page quality assessment . the dataset includes over 65,000 detailed an-notations spanning four sub-dimensions . |
| Outcome: | The proposed dataset includes over 65,000 detailed an-notations spanning four sub-dimensions and incorporates elements such as HTML+CSS, text, and visual screenshot. |
Narrative Order Aware Story Generation via Bidirectional Pretraining Model with Optimal Transport Reward (2023.findings-emnlp)
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| Challenge: | Existing storytelling systems suffer from insufficient understanding of event correlations and inadequate awareness of event temporal order. |
| Approach: | They propose a narrative order aware framework to generate coherent stories with flashbacks . they propose 'bidirectional pretraining model with Optimal Transport Reward' to improve quality . |
| Outcome: | The proposed framework generates coherent stories with flashbacks with a novel optimal transport reward. |
Large Language Model-Based Event Relation Extraction with Rationales (2025.coling-main)
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| Challenge: | Existing methods for ERE rely on large language models, but they face limitations. |
| Approach: | They propose an LLM-based approach with rationales for the ERE task . LLMERE transforms ERE into a question-and-answer task that may have multiple answers . |
| Outcome: | Experimental results show that LLMERE improves over existing methods. |
HearSay Benchmark: Do Audio LLMs Leak What They Hear? (2026.findings-acl)
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Jin Wang, Kaiwen Luo, Liang Lin, Weiliu Wang, Yitian Chen, Moayad Aloqaily, Xuehai Tang, Zhenhong Zhou, Kun Wang, Li Sun, Qingsong Wen
| Challenge: | Recent advances in audio large language models have led to their potential privacy implications unexplored. |
| Approach: | They propose a benchmark to examine whether ALLMs leak user privacy through acoustic voiceprints. |
| Outcome: | The proposed benchmark is constructed from over 22,000 real-world audio clips. |
Meeseeks: A Feedback-Driven, Iterative Self-Correction Benchmark evaluating LLMs’ Instruction Following Capability (2026.findings-acl)
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Jiaming Wang, Yunke Zhao, Peng Ding, Jun Kuang, Yibin Shen, Zhe Tang, Yilin Jin, ZongYu Wang, Xiaoyu Li, Xuezhi Cao
| Challenge: | Existing models lack the ability to adhere to instructions, resulting in suboptimal performance. |
| Approach: | They propose an automated iterative instruction-following benchmark with integrated feedback mechanism. |
| Outcome: | The proposed benchmark identifies erroneous components in model responses and provides feedback accurately. |
Generalization or Memorization: Data Contamination and Trustworthy Evaluation for Large Language Models (2024.findings-acl)
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| Challenge: | Considering the vast size and wide-ranging sources of LLMs’ training data, it could explicitly or implicitly include test data. |
| Approach: | They propose a Contamination Detection via output Distribution (CDD) which detects data contamination only by identifying the peakedness of LLM's output distribution. |
| Outcome: | The proposed method improves performance by 21.8%-30.2% on humanEval and TED: trustworthy evaluation via output distribution. |
BENTO: A Visual Platform for Building Clinical NLP Pipelines Based on CodaLab (2020.acl-demos)
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| Challenge: | CodaLab has limited support for creating reusable tools that can be easily applied to different datasets and composed into pipelines. |
| Approach: | They propose a workflow management platform with a graphic user interface built on top of CodaLab to facilitate the process of building clinical NLP pipelines. |
| Outcome: | The proposed workflow management platform, BENTO, is designed for clinical NLP tasks and can be easily used by researchers and developers. |
Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs (2024.findings-acl)
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Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy, Yu Zhang, Zheng Li, Ruirui Li, Xianfeng Tang, Suhang Wang, Yu Meng, Jiawei Han
| Challenge: | Existing studies suggest augmenting LLMs with external text corpora to alleviate hallucination problems. |
| Approach: | They propose to augment large language models with text units retrieved from external knowledge corpora to alleviate the issue. |
| Outcome: | The proposed framework outperforms baselines on GRBench with three LLMs and shows that iterative reasoning outperformed the baselines. |
TalkLoRA: Communication-Aware Mixture of Low-Rank Adaptation for Large Language Models (2026.acl-long)
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| Challenge: | Existing LoRA methods assume that experts operate independently, leading to unstable routing, expert dominance. |
| Approach: | They propose a communication-aware MoELoRA framework that relaxes this assumption by introducing expert-level communication prior to routing. |
| Outcome: | The proposed framework outperforms vanilla LoRA and MoELoRA on diverse language understanding tasks while maintaining expert dominance. |
QuantAgents: Towards Multi-agent Financial System via Simulated Trading (2025.findings-emnlp)
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| Challenge: | Existing LLM-based agent models exhibit significant deviations from real-world fund companies. |
| Approach: | They propose a multi-agent financial system that incorporates simulated trading . they propose simulated trades are evaluated without assuming actual risks . |
| Outcome: | The proposed system evaluates various investment strategies without assuming actual risks without involving real-world investors. |
Contextual Modeling for Document-level ASR Error Correction (2024.lrec-main)
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| Challenge: | Existing work on document-level ASR error correction ignores contextual information . however, there are limited studies on incorporating contextual information into AEC . |
| Approach: | They propose a context-aware method that retrieves contextual information from a datastore . they use two English and two Chinese datasets to model document-level AEC . |
| Outcome: | The proposed model can utilize contextual information to improve document-level AEC . the data store containing contextual information provides even better results . |
Rethinking the Reversal Curse of LLMs: a Prescription from Human Knowledge Reversal (2024.emnlp-main)
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Zhicong Lu, Li Jin, Peiguang Li, Yu Tian, Linhao Zhang, Sirui Wang, Guangluan Xu, Changyuan Tian, Xunliang Cai
| Challenge: | Existing methods for large language models (LLMs) are limited by their aggressive sample permutation and lack a detailed understanding of the underlying reasons for the reversal curse. |
| Approach: | They propose a method which enhances bidirectional entity correlation modeling and pairwise relationship reasoning to overcome the reversal curse. |
| Outcome: | The proposed method overcomes the reversal curse by augmenting the samples with entity order-reversals and semantically preserved question-answer pairs. |
SOP-Maze: Evaluating Large Language Models on Complicated Business Standard Operating Procedures (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are widely deployed as domain-specific agents, but evaluation of their capabilities in such contexts has not been fully explored. |
| Approach: | They propose a benchmark to evaluate LLMs' ability to follow instructions and make decisions in real-world scenarios. |
| Outcome: | The proposed benchmark is constructed from real-world business data and adapted into 23 complex SOP scenarios. |
Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design (2026.findings-acl)
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| Challenge: | Prior studies have focused on designing customized MAS for specific tasks . a critical research question remains: do LLM agent groups exhibit a form of "general intelligence" |
| Approach: | They find a Collective Intelligence factor in human groups that captures their general capability. |
| Outcome: | The proposed model predicts the ACI factor based on the features of LLM agent groups and can improve generalization abilities. |
DVD: Dynamic Contrastive Decoding for Knowledge Amplification in Multi-Document Question Answering (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) generate information with hallucinations due to uneven retrieval quality and irrelevant contents. |
| Approach: | They propose a decoding strategy which dynamically amplifies knowledge from selected documents during the generation phase. |
| Outcome: | The proposed method outperforms other decoding strategies on ALCE-ASQA, NQ, TQA and PopQA benchmarks. |
Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs (2024.lrec-main)
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| Challenge: | Large Language Models (LLMs) have made significant advances in code generation through the ‘Chain-of-Thought’ prompting technique. |
| Approach: | They propose a framework which aims to transfer LLMs’ reasoning capabilities to smaller models through distillation. |
| Outcome: | The proposed framework improves the smaller model's code generation performance by over 130% on the APPS benchmark. |
Uni-Dubbing: Zero-Shot Speech Synthesis from Visual Articulation (2024.acl-long)
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Songju Lei, Xize Cheng, Mengjiao Lyu, Jianqiao Hu, Jintao Tan, Runlin Liu, Lingyu Xiong, Tao Jin, Xiandong Li, Zhou Zhao
| Challenge: | Multimodal speech synthesis is a key challenge due to the scarcity of datasets that pair audio with corresponding video. |
| Approach: | They propose a method that incorporates modality alignment during the pre-training phase on multimodal datasets and freezes the video modality extraction component and the encoder module within the pretrained weights. |
| Outcome: | The proposed method achieves a reduced word error rate (WER) of 31.73%, surpassing the previous best of 33.9% with single-modality audio. |
CEMT:Controllable Element-Oriented Machine Translation via Structured Linguistic Reasoning (2026.findings-acl)
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| Challenge: | Large Language Models suffer from paraphrasing errors, omissions, or hallucinations when input contains translation-specific elements that require strict preservation or controlled transformation. |
| Approach: | They propose a Controllable Element-Oriented Machine Translation framework that decomposes the translation process into a linguistically grounded analysis, strategy formulation, and final generation. |
| Outcome: | The proposed framework improves on the WMT23/24 Chinese–English benchmarks while significantly reducing element-level constraint violations. |
Rethinking Positional Encoding in Tree Transformer for Code Representation (2022.emnlp-main)
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| Challenge: | Recent works have proposed novel tree Transformers to capture the syntactic structure in source code. |
| Approach: | They propose a novel tree Transformer encoding node positions based on a description method for tree structures to incorporate inductive bias into Transformer. |
| Outcome: | The proposed model outperforms baselines on code summarization and completion tasks across two languages, and it is able to perform better on both local and global paradigms. |
Exploring Fine-Grained Human Motion Video Captioning (2025.coling-main)
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Bingchan Zhao, Xinyi Liu, Zhuocheng Yu, Tongchen Yang, Yifan Song, Mingyu Jin, Sujian Li, Yizhou Wang
| Challenge: | Existing video captioning models fail to capture nuanced semantics of videos . existing models generate coarse descriptions of human motions, resulting in poor quality . |
| Approach: | They construct a fine-grained human motion video captioning dataset named BoFiT and a model that generates fine-grain descriptions of human motions via prompting. |
| Outcome: | The proposed model outperforms existing models on comprehensive metrics. |
Fine-Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks (D19-1)
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| Challenge: | Existing methods for inferring the fine-grained type of an entity from knowledge base are incomplete and lack type information. |
| Approach: | They propose a novel Deep Learning architecture to infer the fine-grained type of an entity from a knowledge base. |
| Outcome: | The proposed method significantly outperforms four state-of-the-art methods on two large-scale datasets. |
TMID: A Comprehensive Real-world Dataset for Trademark Infringement Detection in E-Commerce (2023.emnlp-industry)
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| Challenge: | Annually, e-commerce platforms incur substantial financial losses due to trademark infringements. |
| Approach: | They propose a dataset to detect trademark infringement in merchant registrations . they use legal rules and contextual information from Alipay to gather contextual information with annotations from legal experts. |
| Outcome: | The proposed dataset is sourced from Alipay, one of the world’s largest e-commerce and digital payment platforms. |
Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt (2024.naacl-long)
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Yongqi Wang, Ruofan Hu, Rongjie Huang, Zhiqing Hong, Ruiqi Li, Wenrui Liu, Fuming You, Tao Jin, Zhou Zhao
| Challenge: | Recent singing-voice-synthesis methods lack ability to control style attributes of synthesized singing. |
| Approach: | They propose a singing-voice-synthesis method that enables attribute controlling on singer gender, vocal range and volume with natural language. |
| Outcome: | The proposed method achieves favorable control ability and audio quality. |
H-MAS: Hierarchical Multi-Agent Scheduling for Multi-Tenant LLM Serving (2026.findings-acl)
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Yuhan Liu, Cong Xu, Qi Jia, Yihua Wang, Feiyu Chen, Liang Jin, Lu Liu, Yaqian Zhao, Yuting Ding, Xiang Li
| Challenge: | Multi-tenant Model-as-a-Service (MaaS) workloads exhibit non-stationarity across multiple time scales . existing request schedulers often rely on a fixed policy that remains unchanged at runtime . |
| Approach: | They propose a hierarchical multi-agent scheduler that operates in a layered closed loop . they propose to maintain 1.2–3.0 higher Goodput than SGLang and vLLM . |
| Outcome: | Experiments show that H-MAS achieves 1.2–3.0 higher Goodput than SGLang and vLLM . it maintains more stable QoS under diverse request lengths and heterogeneous SLO targets . |
Neuro-Symbolic Query Compiler (2025.findings-acl)
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| Challenge: | Retrieval-Augmented Generation (RAG) systems are limited in their ability to process information in open-source environments. |
| Approach: | They propose a neuro-symbolic framework inspired by linguistic grammar rules and compiler design to formalize complex queries using a minimal yet sufficient Backus-Naur Form grammar. |
| Outcome: | The proposed framework is based on a backus-naur form grammar and compiler design that maintains completeness while minimizing redundancy. |
ESCoT: Towards Interpretable Emotional Support Dialogue Systems (2024.acl-long)
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| Challenge: | Emotion-focused and strategy-driven chain-of-thought (ESCoT) is a new paradigm for emotional support dialogues. |
| Approach: | They propose an emotional support response generation scheme to improve interpretability . they generate a dataset and develop a model to generate dialogue responses with better interpretability. |
| Outcome: | The proposed scheme can generate dialogue responses with better interpretability. |
Event Causality Extraction via Implicit Cause-Effect Interactions (2023.emnlp-main)
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| Challenge: | Existing studies have not exploited the interactions between the cause and effect event that could provide crucial clues for causality reasoning. |
| Approach: | They propose an Implicit Cause-Effect interaction framework which captures the implicit intra- and inter-event interactions by incorporating the privileged information for reasoning. |
| Outcome: | The proposed framework captures the implicit intra- and inter-event interactions by incorporating the privileged information (ground truth event types and arguments) for reasoning. |
From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation (2026.acl-long)
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Kaiwen Wei, Kejun he, Xiaomian Kang, Jie Zhang, null Ymyang, Li Jin, Zhenyang Li, Jiang Zhong, Richard He Bai, Junnan Zhu
| Challenge: | Generative recommendation models inherently bias towards local contexts, failing to capture deeper historical dependencies necessary for understanding complex user intents. |
| Approach: | They propose a training framework that shifts the objective from simple next-step prediction to deep comprehension of history by entropy-guided masking policy and a curriculum learning scheduler to enhance the framework. |
| Outcome: | The proposed framework outperforms state-of-the-art generative models on three public datasets and shows that it is more accurate than current models. |
Self-Improvement Programming for Temporal Knowledge Graph Question Answering (2024.lrec-main)
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| Challenge: | Existing methods implicitly model time constraints by learning time-aware embeddings of questions and candidate answers, which is far from understanding the question comprehensively. |
| Approach: | They propose a temporal-based temporal programming method that leverages the in-context learning ability of Large Language Models to understand combinatory time constraints in questions. |
| Outcome: | The proposed method outperforms existing methods on multiTQ and CronQuestions datasets and is highly efficient on multi-level questions. |
RCTEA: Richness-guided Co-training for Temporal Entity Alignment (2026.findings-acl)
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| Challenge: | Existing TEA models fail to capture the orthogonal yet complementary effect between structural and temporal features. |
| Approach: | They propose a framework that jointly models structural and temporal aspects of Temporal Knowledge Graphs for entity alignment. |
| Outcome: | The proposed framework achieves state-of-the-art on public TEA benchmarks. |
FashionKLIP: Enhancing E-Commerce Image-Text Retrieval with Fashion Multi-Modal Conceptual Knowledge Graph (2023.acl-industry)
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Xiaodan Wang, Chengyu Wang, Lei Li, Zhixu Li, Ben Chen, Linbo Jin, Jun Huang, Yanghua Xiao, Ming Gao
| Challenge: | Recent advances in visual-language pre-trained (VLP) models have greatly improved cross-modal retrieval performance . however, the fine-grained interactions between objects from different modalities are far from well-established . e-commerce domain lacks sufficient training data and fine-granular cross-modulal knowledge . |
| Approach: | They propose a visual-language pre-trained (VLP) image-text retrieval model that integrates cross-modal knowledge into the model to improve performance. |
| Outcome: | The proposed model improves performance on e-commerce image-text retrieval task by a large margin. |
DICP: Deep In-Context Prompt for Event Causality Identification (2025.findings-emnlp)
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| Challenge: | Existing prompt-learning-based methods concatenate in-context examples only at the input layer, limiting the model’s ability to capture abstract semantic cues necessary for identifying complex causal relationships. |
| Approach: | They propose a model that injects in-context examples into the deeper layer of a pre-trained language model (PLM) this model leverages hierarchical semantic representations formed in deeper layers, thereby enhancing its capacity to learn high-level causal abstractions. |
| Outcome: | The proposed model improves on two widely used datasets and shows that it can learn high-level causal abstractions. |
Dual Low-Rank Multimodal Fusion (2020.findings-emnlp)
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| Challenge: | Existing tensor-based fusion methods make poor use of fine-grained temporal dynamics of multimodal sequential features. |
| Approach: | They propose a novel multimodal fusion method called Fine-Grained Temporal Low-Rank Multimodal Fusion that uses low-rank tensor approximation along dual dimensions of input features. |
| Outcome: | The proposed method outperforms the state-of-the-art tensor-based methods with a similar computational complexity. |
U-Fold: Dynamic Intent-Aware Context Folding for User-Centric Agents (2026.findings-acl)
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| Challenge: | Existing context-folding methods are designed for single-query or single-intent scenarios. |
| Approach: | They propose a dynamic context-folding framework tailored to user-centric tasks that preserves fine-grained information through dynamic context folding. |
| Outcome: | The proposed framework outperforms ReAct and previous folding frameworks on long, noisy tasks. |
Instilling Type Knowledge in Language Models via Multi-Task QA (2022.findings-naacl)
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| Challenge: | Current methods to learn entity types rely on coarse, noisy labels . current methods rely only on text-to-text pre-training on type-centric questions . |
| Approach: | They propose to instill fine-grained type knowledge in language models by pre-training on type-centric questions. |
| Outcome: | The proposed model achieves state-of-the-art in zero-shot dialog state tracking benchmarks and can accurately infer entity types in Wikipedia articles. |
MTR: A Dataset Fusing Inductive, Deductive, and Defeasible Reasoning (2023.findings-acl)
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| Challenge: | Existing datasets for logical reasoning focus on monotonic logic and a single form of reasoning. |
| Approach: | They propose to use a dataset to study the human-like reasoning in machine reading comprehension. |
| Outcome: | The proposed dataset shows that state-of-the-art neural models perform noticeably worse than expected. |
KoCo-Bench: Can Large Language Models Leverage Domain Knowledge in Software Development? (2026.acl-long)
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Xue Jiang, Ge Li, Jiaru Qian, Xianjie Shi, Chenjie Li, Hao Zhu, Ziyu Wang, Jielun Zhang, Zeyu Zhao, Kechi Zhang, Jia Li, Wenpin Jiao, Zhi Jin, Yihong Dong
| Challenge: | Existing domain-specific code benchmarks focus on assessing what knowledge LLMs possess rather than how they acquire and apply new knowledge. |
| Approach: | They propose a benchmark to evaluate domain specialization methods in real-world software development. |
| Outcome: | KOCO-bench is a new benchmark for evaluating domain specialization methods in real-world software development. |
DenseLoRA: Dense Low-Rank Adaptation of Large Language Models (2025.acl-long)
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| Challenge: | Low-rank adaptation (LoRA) is an efficient approach for adapting large language models (LLMs) but many of the weights in these matrices are redundant, leading to inefficiencies in parameter utilization. |
| Approach: | They propose a low-rank adaptation approach that fine-tunes two low-ranked matrices and adapts them through a dense low-Rank matrix, improving parameter utilization and adaptation efficiency. |
| Outcome: | The proposed approach achieves 83.8% accuracy with only 0.01% of trainable parameters compared to LoRA's 80.8% with 0.70% of trainability parameters on LLaMA3-8B. |
Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision? (2025.acl-long)
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| Challenge: | Graph Neural Networks (GNNs) with CLIP pipeline are difficult because of the scarcity of labeled data and text supervision, different levels of downstream tasks, and conceptual gaps between domains. |
| Approach: | They propose a multi-modal prompt learning paradigm to adapt pre-trained GNNs to downstream tasks with weak text supervision. |
| Outcome: | The proposed model can generalize graphs to unseen classes with weak text supervision. |
RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment (2025.findings-acl)
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Zhuoran Jin, Hongbang Yuan, Tianyi Men, Pengfei Cao, Yubo Chen, Jiexin Xu, Huaijun Li, Xiaojian Jiang, Kang Liu, Jun Zhao
| Challenge: | Existing retrieval augmented language models often overlook effective alignment with human preferences. |
| Approach: | They propose a benchmark to evaluate RMs in retrieval augmented language models . they incorporate 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity . |
| Outcome: | The proposed benchmark combines 18 RAG subsets, six retrievers, and 24 RALMs to increase diversity of data sources. |
Compilable Neural Code Generation with Compiler Feedback (2022.findings-acl)
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Xin Wang, Yasheng Wang, Yao Wan, Fei Mi, Yitong Li, Pingyi Zhou, Jin Liu, Hao Wu, Xin Jiang, Qun Liu
| Challenge: | Existing deep-learning approaches model code generation as text generation, but few of them account for compilability of the generated programs. |
| Approach: | They propose a three-stage pipeline utilizing compiler feedback for compilable code generation to improve compilability. |
| Outcome: | The proposed pipeline improves compilability of generated programs by combining compiler feedback, language model fine-tuning, and compilable discrimination. |
AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code (2026.findings-acl)
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ShangZhan Li, Xinyu Yin, Xuanyu Jin, Ye He, Yuxin Zhou, Yuxuan Li, Xu Han, Wanxiang Che, Qi Shi, Ting Liu, Maosong Sun
| Challenge: | Current development practices face a dichotomy between automation and performance. |
| Approach: | They propose a framework to empower LLMs with the capability of automated explicit vectorization. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the SSE and AVX subsets of SimdBench. |
TinyAlign: Boosting Lightweight Vision-Language Models by Mitigating Modal Alignment Bottlenecks (2026.findings-acl)
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Yuanze Hu, Xinyu Wang, Zhichao Yang, Gen Li, Ye Qiu, Zhaoxin Fan, Yifan Sun, Wenjun wu, Jin Dong, Xiaotie Deng
| Challenge: | Lightweight Vision-Language Models (VLMs) are indispensable for resource-constrained applications. |
| Approach: | They propose a framework that retrieves context from a memory bank to enhance alignment . they propose EMI-based approach to align vision and language models . |
| Outcome: | The proposed framework reduces training loss, accelerates convergence, and enhances task performance with negligible computational overhead. |
Capturing Conversational Interaction for Question Answering via Global History Reasoning (2022.findings-naacl)
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| Challenge: | Existing studies have studied history-dependent reasoning for question answering . utilizing global conversation history for enhancement is gaining interest . |
| Approach: | They propose to establish long-distance dependency among global utterances in multi-turn conversation. |
| Outcome: | The proposed method improves on QuAC by 1%, yielding the F1 score of 73.7%. |
Dynamic Low-rank Estimation for Transformer-based Language Models (2023.findings-emnlp)
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| Challenge: | RankDyna is a matrix decomposition method that can be used to compress Transformer-based language models. |
| Approach: | They propose a matrix decomposition method that enables dynamic rank resource allocation . they say it can outperform current SOTA methods under various parameter budget levels . |
| Outcome: | The proposed method outperforms current SOTA methods under various budget levels . the proposed method is more efficient with higher compression rates . |
Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora (2022.naacl-main)
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| Challenge: | Pretrained language models are typically learned over a large, static corpus and fine-tuned for various downstream tasks. |
| Approach: | They propose to continuously update a pretrained language model to adapt to emerging data and to keep track of the model's performance. |
| Outcome: | The proposed model can adapt to new corpora while retaining knowledge in earlier domains. |
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering (2023.acl-demo)
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Zijun Yao, Yuanyong Chen, Xin Lv, Shulin Cao, Amy Xin, Jifan Yu, Hailong Jin, Jianjun Xu, Peng Zhang, Lei Hou, Juanzi Li
| Challenge: | Existing knowledge base question answering systems that parse natural language questions into knowledge oriented program language (KoPL) . |
| Approach: | They propose a knowledge base question answering system that integrates human into the loop to edit and debug queries. |
| Outcome: | The proposed system can debug and edit knowledge base questions on a million-entity-level . it provides auto-completion for its knowledge base schema and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer. |
VulAgent: Hypothesis-Validation Driven Multi-Agent Architecture for Vulnerability Detection (2026.findings-acl)
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| Challenge: | Recent reports indicate that software vulnerabilities caused by insecure coding practices remain a major security threat. |
| Approach: | They propose a multi-agent vulnerability detection framework based on hypothesis validation . they use multi-view analyzers to localize and localize security-sensitive operations . |
| Outcome: | The proposed framework reduces false positives and increases accuracy by 6.6 percentage points on PrimeVul and SVEN. |
Entropy-Based Vocabulary Substitution for Incremental Learning in Multilingual Neural Machine Translation (2022.emnlp-main)
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| Challenge: | Existing methods to update a multilingual model with new language pairs are expensive and time-consuming. |
| Approach: | They propose an entropy-based vocabulary substitution method that walks through new language pairs for incremental learning while remaining the size of the original vocabulary. |
| Outcome: | The proposed method achieves better performance and saves excess overhead in a multilingual machine translation task. |
Enhanced Coherence-Aware Network with Hierarchical Disentanglement for Aspect-Category Sentiment Analysis (2024.lrec-main)
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| Challenge: | Aspect-category-based sentiment analysis (ACSA) is a popular approach for identifying aspect categories and predicting their sentiments. |
| Approach: | They propose an enhanced coherence-aware network with hierarchical disentanglement (ECAN) to capture contexts across the whole review and to help the implicit aspect and sentiment identification. |
| Outcome: | The proposed network decouples multiple aspects and sentiment features and achieves state-of-the-art (SOTA) performance. |
Taming the Real-world Complexities in CPT E/M Coding with Large Language Models (2025.emnlp-industry)
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Islam Nassar, Yang Lin, Yuan Jin, Rongxin Zhu, Chang Wei Tan, Zenan Zhai, Nitika Mathur, Thanh Tien Vu, Xu Zhong, Long Duong, Yuan-Fang Li
| Challenge: | Evaluation and Management (E/M) coding is performed by physicians and trained human coders who review clinical encounter notes and electronic health record data to assign appropriate codes. |
| Approach: | They propose a framework that automates evaluation and management coding tasks using the Current Procedural Terminology (CPT) taxonomy. |
| Outcome: | The proposed framework achieves an increase in coding accuracy of more than 36% over a commercial CPT E/M coding system and almost 5% over our strongest single-prompt baseline. |
Establishing Trustworthy LLM Evaluation via Shortcut Neuron Analysis (2025.acl-long)
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| Challenge: | Recent studies have focused on building dynamic benchmarks to address data contamination issues. |
| Approach: | They propose a method for identifying shortcut neurons through comparative and causal analysis to suppress shortcut neurons. |
| Outcome: | The proposed method overestimates contaminated models and is highly generalizable across benchmarks and hyperparameter settings. |
GraphLoRA: Structure-Aware Low-Rank Adaptation for Large Language Model Recommendation (2026.findings-acl)
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| Challenge: | Existing methods for translating collaborative information into textual prompts or injecting pre-trained embeddings into the LLM treat structural information as static input and fail to capture high-order relational dependencies. |
| Approach: | They propose a framework that generalizes low-rank adaptation from independent to structure-aware propagation by embedding a trainable graph message-passing network within the low-ranked adaptation pathway. |
| Outcome: | Experiments on multiple benchmarks show that GraphLoRA outperforms state-of-the-art recommendation methods and achieves superior generalization. |
Residual Learning of Neural Text Generation with n-gram Language Model (2022.findings-emnlp)
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| Challenge: | Experimental results show that n-gram models can achieve satisfactory performance on a large proportion of testing cases. |
| Approach: | They propose to learn a neural LM that fits the residual between an n-gram LM and the real-data distribution. |
| Outcome: | The proposed model achieves additional performance gains over popular standalone models on three typical language tasks. |
Missing Modality Imagination Network for Emotion Recognition with Uncertain Missing Modalities (2021.acl-long)
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| Challenge: | Existing multimodal fusion models trained on full-modality samples fail when partial modalities are missing. |
| Approach: | They propose a model to deal with the uncertain missing modality problem by learning robust joint multimodal representations that can predict the representation of any missing modal given available modalities under different missing-modality conditions. |
| Outcome: | The proposed model significantly improves performance under uncertain missing-modality testing conditions and full-modalities ideal testing conditions. |
Spotlight and Shadow: Attention-Guided Dual-Anchor Introspective Decoding for MLLM Hallucination Mitigation (2026.findings-acl)
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| Challenge: | Multimodal Large Language Models (MLLMs) excel in tasks ranging from image captioning to complex reasoning. |
| Approach: | They propose a contrastive decoding framework that dynamically calibrates each token generation by mining the model’s internal perceptual discrepancies. |
| Outcome: | The proposed framework mitigates hallucination while enhancing general reasoning capabilities. |
CodeDPO: Aligning Code Models with Self Generated and Verified Source Code (2025.acl-long)
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| Challenge: | Existing training methods for code generation do not improve code correctness and efficiency. |
| Approach: | They propose a framework that integrates preference learning into code generation to improve code correctness and efficiency. |
| Outcome: | The proposed framework improves code correctness and efficiency by integrating preference learning into code generation. |
On Construction of the ASR-oriented Indian English Pronunciation Dictionary (2020.lrec-1)
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| Challenge: | Indian English (IE) has distinctive characteristics, especially phonologically, from other varieties of English. |
| Approach: | They build a small IE spontaneous speech corpus and use a linguistically-guided IE pronunciation dictionary to apply it to IE. |
| Outcome: | The proposed system performs better on IE spontaneous speech data than the one trained with CMUdict. |
AIRepr: An Analyst-Inspector Framework for Evaluating Reproducibility of LLMs in Data Science (2025.findings-emnlp)
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| Challenge: | Large language models are increasingly used to automate data analysis, but data science tasks often admit multiple statistically valid solutions. |
| Approach: | They propose a framework to evaluate LLM-generated code and assess its reproducibility . they introduce two reproducibility-enhancing prompting strategies and benchmark them against standard prompting . |
| Outcome: | The proposed framework improves reproducibility of large language models . it provides a foundation for transparent, reliable, and efficient human–AI collaboration in data science. |
RedundancyLens: Revealing and Exploiting Visual Token Processing Redundancy for Efficient Decoder-Only MLLMs (2025.findings-acl)
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| Challenge: | Current decoder-only architectures achieve higher performance but lower efficiency . cross-attention-based architectures skip visual token computations . |
| Approach: | They propose a training-free framework for analyzing trained MLLMs to investigate redundancy . they propose 'probe-activated Dynamic FFN and Hollow Attention' algorithms for visual token reductions and a layer ranking algorithm for inference acceleration. |
| Outcome: | The proposed framework achieves comparable performance to or better than state-of-the-art methods while remaining compatible with them. |
AgentsCourt: Building Judicial Decision-Making Agents with Court Debate Simulation and Legal Knowledge Augmentation (2024.findings-emnlp)
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Zhitao He, Pengfei Cao, Chenhao Wang, Zhuoran Jin, Yubo Chen, Jiexin Xu, Huaijun Li, Kang Liu, Jun Zhao
| Challenge: | Recent advances in deep learning have significantly impacted the legal domain. |
| Approach: | They propose a multi-agent framework for judicial decision-making that simulates the court trial process . they propose 420 Chinese judgment documents to support their framework and build a large-scale legal knowledge base . |
| Outcome: | The proposed framework outperforms existing methods in various aspects, especially in generating legal articles. |
DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis (2022.coling-1)
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| Challenge: | Emotion Recognition in Conversation (ERC) has attracted increasing research attention in recent years. |
| Approach: | They propose to model the emotional interactions between speakers to simulate the emotional inertia, emotional stimulus, global and local emotional evolution in dialogues. |
| Outcome: | The proposed model can achieve superior performance compared to state-of-the-art methods on four ERC benchmark datasets, IEMOCAP, MELD, EmoryNLP and DailyDialog. |
Your Inference Request Will Become a Black Box: Confidential Inference for Cloud-based Large Language Models (2026.acl-long)
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Chung-ju Huang, Huiqiang Zhao, Yuanpeng He, Lijian Li, Wenpin Jiao, Zhi Jin, Peixuan Chen, Leye Wang
| Challenge: | Existing approaches fail to ensure privacy, maintain model performance, and preserve computational efficiency simultaneously. |
| Approach: | They propose a confidential inference framework that partitions the LLM pipeline between a client-verified Confidential Virtual Machine (CVM) and the public cloud to protect client data without compromising the cloud’s model intellectual property or inference quality. |
| Outcome: | The proposed framework can defend against state-of-the-art token inference attacks while preserving model privacy, performance, and efficiency. |
A Survey of Link Prediction in N-ary Knowledge Graphs (2025.emnlp-main)
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Jiyao Wei, Saiping Guan, Da Li, Zhongni Hou, Miao Su, Yucan Guo, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng
| Challenge: | N-ary Knowledge Graphs (NKGs) capture n-ary facts containing more than two entities. |
| Approach: | They present the first comprehensive survey of link prediction in NKGs . they provide an overview of the field and analyze their performance and application scenarios . |
| Outcome: | The proposed methods provide an overview of the field and analyze performance and application scenarios. |
CARE-STaR: Constraint-aware Self-taught Reasoner (2025.findings-acl)
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Zhiliang Li, Bo Tang, Yijun Niu, Beihong Jin, Qiwen Shi, Yuchen Feng, Zhiyu Li, Jie Hu, Mingchuan Yang, Feiyu Xiong
| Challenge: | Recent research on instruction following has demonstrated that LLMs can handle complex instructions. |
| Approach: | They propose to assign constraints to different levels of constraints in instructions . they use chain-of-thought and self-taught reasoner methods to identify constraints . |
| Outcome: | The proposed method outperforms supervised fine-tuning (SFT) on three instruction-following benchmarks. |
AgentMark: Utility-Preserving Behavioral Watermarking for Agents (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have improved text generation and reasoning. |
| Approach: | They propose a behavioral watermarking framework that embeds multi-bit identifiers into planning decisions while preserving utility. |
| Outcome: | The proposed framework embeds multi-bit provenance into planning decisions while preserving utility. |
Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest (2022.coling-1)
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| Challenge: | Medical relation extraction (MRE) tasks aims to extract relations between entities in medical literature. |
| Approach: | They propose to combine semantic and syntactic information from medical texts by using causal explanation theory. |
| Outcome: | Empirically, the proposed model outperforms existing methods on benchmark medical datasets. |
Enhancing Knowledge Selection for Grounded Dialogues via Document Semantic Graphs (2022.naacl-main)
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| Challenge: | Existing conversation models treat knowledge selection as a sentence ranking problem where each sentence is handled individually, ignoring the internal semantic connection between sentences. |
| Approach: | They propose to automatically convert background knowledge documents into document semantic graphs and perform knowledge selection over such graphs. |
| Outcome: | The proposed model improves on the knowledge selection task and the response generation task on HollE and generalizes on unseen topics in WoW. |
PIPER: Benchmarking and Prompting Event Reasoning Boundary of LLMs via Debiasing-Distillation Enhanced Tuning (2025.acl-long)
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Zhicong Lu, Changyuan Tian, PeiguangLi PeiguangLi, Li Jin, Sirui Wang, Wei Jia, Ying Shen, Guangluan Xu
| Challenge: | Existing studies on Large Language Models (LLMs) have failed to evaluate their performance in event reasoning with a single event relational type or reasoning format. |
| Approach: | They propose a benchmark to evaluate LLMs' event reasoning capability using a single event relational type or reasoning format. |
| Outcome: | The proposed model improves on 10K diverse instruction-tuning demonstrations to alleviate event reasoning-oriented data scarcity. |
RL-PLUS: Countering Capability Boundary Collapse of LLMs in Reinforcement Learning with Hybrid-policy Optimization (2026.acl-long)
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Yihong Dong, Xue Jiang, Yongding Tao, Huanyu Liu, Kechi Zhang, Lili Mou, Rongyu Cao, Yingwei MA, Jue Chen, Binhua Li, Zhi Jin, Fei Huang, Yongbin Li, Ge Li
| Challenge: | Reinforcement Learning with Verifiable Reward (RLVR) has significantly advanced the complex reasoning abilities of Large Language Models (LLMs). |
| Approach: | They propose a hybrid-policy optimization approach that synergizes internal exploitation with external data to achieve stronger reasoning capabilities. |
| Outcome: | The proposed approach achieves state-of-the-art performance on six math reasoning benchmarks and superior performance on out-of distribution reasoning tasks. |
Knowledge Transfer in Incremental Learning for Multilingual Neural Machine Translation (2023.acl-long)
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| Challenge: | Existing studies focus on overcoming catastrophic forgetting on original language pairs while lacking encouragement to learn new knowledge from incremental learning. |
| Approach: | They propose a knowledge transfer method that can adapt original MNMT models to diverse incremental language pairs by flexibly introducing knowledge from external models into original models, which encourages the models to learn new language pairs. |
| Outcome: | The proposed method outperforms baselines on multiple languages while maintaining performance on original language pairs. |
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter (2024.findings-acl)
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Meng Cao, Haoran Tang, Jinfa Huang, Peng Jin, Can Zhang, Ruyang Liu, Long Chen, Xiaodan Liang, Li Yuan, Ge Li
| Challenge: | Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries. |
| Approach: | They propose to conduct efficient text-video Retrieval with a salient-and-correlated AdaPter . they propose a low-rank modulation module to refine per-image features from frozen CLIP backbone . |
| Outcome: | Experiments on four TVR datasets show that the proposed method performs better than other methods. |
Safety Alignment via Constrained Knowledge Unlearning (2025.acl-long)
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Zesheng Shi, Yucheng Zhou, Jing Li, Yuxin Jin, Yu Li, Daojing He, Fangming Liu, Saleh Alharbi, Jun Yu, Min Zhang
| Challenge: | Existing defense mechanisms have not fully deleted harmful knowledge in large language models (LLMs) Existing methods to address safety alignment have not completely deleted harmful information in LLMs. |
| Approach: | They propose a safety alignment strategy that uses scoring neurons to identify useful knowledge in LLMs and pruning the gradients of neurons in U to preserve beneficial information. |
| Outcome: | The proposed method significantly improves model safety while maintaining utility compared to existing methods. |
Scaling Law for Multimodal Large Language Model Supervised Fine-Tuning (2026.acl-long)
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YiFan Zhang, Tao Yu, Feng Li, Chaoyou Fu, Yibo Hu, Kun Wang, Qingsong Wen, Zhang Zhang, Liang Wang, Rong Jin
| Challenge: | supervised fine-tuning (SFT) is crucial for multimodal large language models, yet a comprehensive scaling law is lacking . et al.: scaling laws focus on model size, pre-training tokens, and MLLM SFT data volumes . |
| Approach: | They propose two scaling laws to guide optimal model-data configuration . they propose one applicable when training data volumes are well defined by researchers . |
| Outcome: | The proposed scaling laws provide valuable recommendations for optimal resource allocation . they show that the proposed laws are more accurate than existing models . |
Learn to Not Link: Exploring NIL Prediction in Entity Linking (2023.findings-acl)
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| Challenge: | Entity linking models have been successful in capturing semantic features, but the NIL prediction problem has not been addressed. |
| Approach: | They propose an entity linking dataset that categorizes mentions linking to NIL into Missing Entity and Non-Entity Phrases. |
| Outcome: | The proposed dataset categorizes mentions linking to NIL into Missing Entity and Non-Entity Phrase categories and ensures the presence of mentions by human annotation and entity masking. |
CodecLM: Aligning Language Models with Tailored Synthetic Data (2024.findings-naacl)
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Zifeng Wang, Chun-Liang Li, Vincent Perot, Long Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister
| Challenge: | Recent work on generating diverse instructions and applying LLM to increase instruction complexity neglects downstream use cases. |
| Approach: | They propose a framework for generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs. |
| Outcome: | Experiments on four open-domain instruction using the proposed framework validate the effectiveness of CodecLM over the current state-of-the-art. |