Papers by Jie Zhang
Dual Prompt Tuning based Contrastive Learning for Hierarchical Text Classification (2024.findings-acl)
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| Challenge: | Existing methods focus on hierarchy-aware text feature by exploiting explicit parent-child relationships, resulting in label confusion within each layer. |
| Approach: | They propose a dual-prompt tuning method which emphasizes discrimination among peer labels by performing contrastive learning on each hierarchical layer. |
| Outcome: | The proposed method outperforms existing methods on benchmark datasets and is available on github. |
Identifying Semantic Induction Heads to Understand In-Context Learning (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable performance, but lack of transparency in their inference logic raises concerns about their trustworthiness. |
| Approach: | They conduct a detailed analysis of the operations of attention heads to understand their in-context learning of LLMs. |
| Outcome: | The proposed analysis of attention heads reveals that they increase the output logits of object tokens and recall objects . the proposed model is a novel approach to understand the in-context learning of large language models. |
E2-LLM: Efficient and Extreme Length Extension of Large Language Models (2024.findings-acl)
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Jiaheng Liu, ZhiqiBai ZhiqiBai, Yuanxing Zhang, Chenchen Zhang, YuangZh YuangZh, Ge Zhang, JiakaiWang JiakaiWang, Haoran Que, Yukang Chen, Wenbo Su, Tiezheng Ge, Jie Fu, Wenhu Chen, Bo Zheng
| Challenge: | Existing techniques for extending context capabilities in LLMs require additional training procedures and access to datasets with long context (e.g., sequences of 32K tokens). |
| Approach: | They propose a solution to extend context capabilities in Large Language Models by training a single process over a sequence of 4K tokens. |
| Outcome: | The proposed solution significantly reduces the cost of continual-pretraining or fine-tuning over short sequences and improves robustness to diverse relative positions. |
Boosting Multi-modal Keyphrase Prediction with Dynamic Chain-of-Thought in Vision-Language Models (2025.emnlp-main)
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Qihang Ma, Shengyu Li, Jie Tang, Dingkang Yang, null Chenshaodong, Yingyi Zhang, Chao Feng, Ran Jiao
| Challenge: | Multi-modal keyphrase prediction (MMKP) aims to produce concise, informative phrases that capture the essence of cross-modal inputs. |
| Approach: | They propose to use vision-language models to generate conclusive phrases using multiple modalities of input information. |
| Outcome: | The proposed methods outperform existing methods on absence and unseen scenarios and overestimate model capability due to overlap in training tests. |
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification (2021.emnlp-main)
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| Challenge: | Data augmentation aims to alleviate the overfitting issue in low-resource or class-imbalanced situations. |
| Approach: | They propose a framework called Text AutoAugment to enhance training samples . they use a Bayesian optimization algorithm to search for the best policy . |
| Outcome: | The proposed framework outperforms baseline methods on six benchmark datasets. |
Mirroring Users: Towards Building Preference-aligned User Simulator with User Feedback in Recommendation (2026.acl-long)
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| Challenge: | Large Language Models lack specific task alignment and large-scale simulations are challenging due to their ambiguity, noise and massive volume. |
| Approach: | They propose a framework that leverages user feedback in RSs with advanced LLM capabilities to generate high-quality simulation data. |
| Outcome: | The proposed framework boosts the alignment with human preferences and in-domain reasoning capabilities of the fine-tuned LLMs. |
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models (2022.coling-1)
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| Challenge: | Prompting has shown to be sample efficient compared to fine-tuning with pre-trained models. |
| Approach: | They propose a fully automatic prompting method that uses natural language prompts on sequence-to-sequence models and a beam search method to generate a large amount of label sequence candidates. |
| Outcome: | The proposed method significantly outperforms other no-manual-design methods on single label words and generates large amount of label sequence candidates. |
The Tug of War Within: Mitigating the Fairness-Privacy Conflicts in Large Language Models (2025.acl-long)
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| Challenge: | Existing methods to enhance an LLM's privacy awareness with thousands of samples decrease its fairness awareness. |
| Approach: | They propose a training-free method to Suppress the Privacy and faIrness coupled Neurons (SPIN) which theoretically and empirically decreases the mutual information between fairness and privacy awareness. |
| Outcome: | The proposed method reduces the mutual information between fairness and privacy awareness without compromising general capabilities. |
N24News: A New Dataset for Multimodal News Classification (2022.lrec-1)
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| Challenge: | Current news datasets focus on text features and rarely leverage the feature of images. |
| Approach: | They propose a news dataset that uses both images and text to achieve better news classification. |
| Outcome: | The proposed model improves on the existing dataset N24News with text and image information. |
CamoQuery: Language-Guided Reasoning Camouflaged Object Segmentation (2026.acl-long)
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| Challenge: | Existing methods for camouflaged object segmentation are limited to vision-only mask prediction under fixed task assumptions. |
| Approach: | They propose a language-guided reasoning camouflaged object segmentation task that generates an intent-consistent segmentation mask from an image and an implicit query text instruction. |
| Outcome: | The proposed task can generate an intent-consistent segmentation mask from a camouflaged image and an implicit query text instruction. |
FLAIR: Steering LLM Mathematical Problem Solving based on A Fuzzy-Logic-AssIsted Reasoner (2026.acl-long)
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Hao Wu, Hongru Sun, Wanqing Li, Xinguo Yu, Hao Ming, Xiao Luo, Wenbin Zhang, Jiahong Zhao, Yi Guo, Jie Yang
| Challenge: | Existing approaches to mathematical reasoning rely on static heuristics or pre-determined reasoning strategies. |
| Approach: | They propose an adaptive framework that integrates fuzzy theory into LLM-based mathematical reasoning. |
| Outcome: | The proposed framework outperforms state-of-the-art models while offering effective and interpretable diagnostics of intermediate problem-solving states. |
Confidence Based Bidirectional Global Context Aware Training Framework for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing studies focus on how to effectively exploit bidirectional global contexts in neural machine translation models. |
| Approach: | They propose a Confidence Based Bidirectional Global Context Aware training framework for NMT . they incorporate bidirectional global context to the NMT model on unconfidently-predicted target words . |
| Outcome: | The proposed framework improves the NMT model on three large-scale translation datasets by +1.02, +0.57 BLEU scores. |
HERB: Measuring Hierarchical Regional Bias in Pre-trained Language Models (2022.findings-aacl)
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| Challenge: | Existing methods do not examine social groups categorised by geographical information, leaving the region-related biases in pre-trained LMs unexplored. |
| Approach: | They propose a hierarchical regional bias evaluation method to quantify regional bias in pre-trained language models. |
| Outcome: | The proposed method evaluates regional bias with regard to comprehensive topics and measures potential regional bias that can be propagated to downstream tasks. |
ProvBench: A Benchmark of Legal Provision Recommendation for Contract Auto-Reviewing (2025.acl-long)
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Xiuxuan Shen, Zhongyuan Jiang, Junsan Zhang, Junxiao Han, Yao Wan, Chengjie Guo, Bingcheng Liu, Jie Wu, Renxiang Li, Philip S. Yu
| Challenge: | Contract review is labor-intensive, time-consuming, and costly . a benchmark is proposed to detect potential legal conflicts . |
| Approach: | They propose a benchmark for legal provision recommendation and conflict detection for contract auto-reviewing which aims to recommend the legal provisions related to contract clauses and detect possible legal conflicts. |
| Outcome: | The proposed task recommends legal provisions related to contract clauses and detects legal conflicts. |
Subgraph Retrieval Enhanced Model for Multi-hop Knowledge Base Question Answering (2022.acl-long)
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| Challenge: | Existing retrieval methods for knowledge base question answering are either heuristic or interwoven with the reasoning, causing reasoning on the partial subgraphs. |
| Approach: | They propose a subgraph retrieval framework that decouples the retrieval from the subsequent reasoning process and trains subgraphs for easier reasoning. |
| Outcome: | The proposed framework improves retrieval and QA performance over existing methods. |
FlowRAG: Synergizing Explicit Reasoning via Frequency-Aware Multi-Granularity Graph Flow (2026.findings-acl)
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| Challenge: | Existing methods for Graph-based retrieval-augmented generation rely on implicit semantic relevance propagation. |
| Approach: | They propose a semantic-aware retrieval framework that improves both semantic recall and explicit reasoning. |
| Outcome: | Extensive experiments show that FlowRAG improves both semantic recall and explicit reasoning. |
Diversify Question Generation with Continuous Content Selectors and Question Type Modeling (2020.findings-emnlp)
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| Challenge: | Existing methods to generate questions based on answers and relevant contexts are not suitable for all questions . |
| Approach: | They propose a method to generate questions from a given answer and its relevant context. |
| Outcome: | The proposed method achieves a better trade-off between generation quality and diversity compared with existing approaches. |
Causal Intervention Improves Implicit Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing neural models struggle with implicit sentiment analysis because they latch onto spurious correlations, resulting in poor generalization and robustness. |
| Approach: | They propose a CausaL intervention model for implicit sEntiment ANalysis using instrumental variable to eliminate confounding causal effects and extract the pure causal effect between sentence and sentiment. |
| Outcome: | The proposed model extracts the pure causal effect between sentence and sentiment using instrumental variable. |
Towards Better Modeling Hierarchical Structure for Self-Attention with Ordered Neurons (D19-1)
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| Challenge: | Recent studies have shown that a hybrid of self-attention networks (SANs) and recurrent neural networks (RNNs) outperforms both individual architectures, while not much is known about why the hybrid models work. |
| Approach: | They propose to use an advanced variant of self-attention networks (SANs) to enhance the strength of hybrid models by introducing a syntax-oriented inductive bias to perform tree-like composition. |
| Outcome: | The proposed model outperforms both individual models and a standard hybrid model on a machine translation task. |
MemSearcher: Iterative Memory Integration for Search Agent via End-to-End Reinforcement Learning (2026.findings-acl)
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Qianhao Yuan, Jie Lou, Zichao Li, Jiawei Chen, Yaojie Lu, Hongyu Lin, Le Sun, Debing Zhang, Xianpei Han
| Challenge: | Recent LLM-based search agents often concatenate the full interaction history into the context, producing long and noisy inputs and increasing compute cost and memory overhead. |
| Approach: | They propose an agent framework that maintains a compact memory during multi-turn interactions. |
| Outcome: | The proposed framework outperforms strong history-concatenation (ReAct-style) baselines on a range of public datasets while maintaining nearly constant token counts across multi-turn interactions. |
Length Generalization of Causal Transformers without Position Encoding (2024.findings-acl)
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| Challenge: | Besides Transformers without position encodings, the success of NoPE provides a new way to overcome the challenge of generalizing to longer sentences. |
| Approach: | They propose a parameter-efficient tuning for searching attention heads’ best temperature hyper-parameters, which substantially expands NoPE’s context size. |
| Outcome: | The proposed tuning significantly expands NoPE's context size, allowing it to generalize to longer sentences with state-of-the-art generalization algorithms. |
Reasoning in a Combinatorial and Constrained World: Benchmarking LLMs on Natural-Language Combinatorial Optimization (2026.findings-acl)
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| Challenge: | NLCO evaluates large language models for combinatorial optimization (CO) . existing evaluations emphasize relatively simple reasoning competencies . |
| Approach: | They propose a combinatorial optimization benchmark that evaluates large language models on CO reasoning. |
| Outcome: | The proposed model can handle combinatorial optimization without writing code or calling external solvers. |
Make Imagination Clearer! Stable Diffusion-based Visual Imagination for Multimodal Machine Translation (2025.acl-long)
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Andong Chen, Yuchen Song, Kehai Chen, Xuefeng Bai, Muyun Yang, Liqiang Nie, Jie Liu, Tiejun Zhao, Min Zhang
| Challenge: | Experimental results show that our model significantly outperforms existing multimodal MT and text-only MT. |
| Approach: | They propose a stable diffusion-based imagination network into a multimodal large language model to generate an image for each source sentence. |
| Outcome: | The proposed model outperforms existing multimodal and text-only MT and achieves an average improvement of 14 BLEU points on Multi30K and MSCOCO multimodal MT benchmarks. |
SafetyQuizzer: Timely and Dynamic Evaluation on the Safety of LLMs (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have been used to evaluate the safety of their users . however, evaluation questions in current benchmarks are too straightforward and difficult to update with practical relevance due to their lack of correlation with real-world events. |
| Approach: | They propose a question-generation framework to evaluate the safety of LLMs in the Chinese context. |
| Outcome: | The proposed framework reduces decline rate while maintaining similar attack success rate. |
TPA: Next Token Probability Attribution for Detecting Hallucinations in RAG (2026.acl-long)
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| Challenge: | Existing approaches attribute hallucinations to a binary conflict between internal knowledge stored in FFNs and the retrieved context. |
| Approach: | They propose a framework which mathematically attributes each next-token probability to seven distinct sources and aggregates source attributions by POS tags to quantify contribution of each model component to the generation of specific linguistic categories within a response. |
| Outcome: | Extensive experiments show that the proposed framework achieves state-of-the-art performance. |
Diffusion Theory as a Scalpel: Detecting and Purifying Poisonous Dimensions in Pre-trained Language Models Caused by Backdoor or Bias (2023.findings-acl)
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| Challenge: | Existing methods to fine-tune pre-trained language models (PLMs) are not safe, since the fine-uning process is invisible to the user. |
| Approach: | They propose a technique to study the dynamic process of fine-tuning for finding poisonous dimensions using diffusion theory. |
| Outcome: | The proposed approach can detect poisonous dimensions with abnormal dynamics, purify them and fine-tune them on a clean dataset. |
Conditional Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation (2022.acl-long)
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| Challenge: | Existing approaches to improve neural machine translation use token-level adaptive training . however, standard models make predictions on condition of previous contexts . |
| Approach: | They propose a target-context-aware metric which can be supplemented by statistical metrics . they propose an adaptive training approach based on token- and sentence-level CBMI . |
| Outcome: | The proposed model outperforms the Transformer baseline and other similar approaches on English-German and Chinese-English tasks. |
Knowledge Inheritance for Pre-trained Language Models (2022.naacl-main)
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Yujia Qin, Yankai Lin, Jing Yi, Jiajie Zhang, Xu Han, Zhengyan Zhang, Yusheng Su, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
| Challenge: | Existing large-scale pre-trained language models are mainly trained from scratch individually, ignoring that many well-taught PLMs are available. |
| Approach: | They propose a pre-training framework called knowledge inheritance and propose auxiliary supervision to efficiently learn larger PLMs. |
| Outcome: | The proposed framework can be used to train large-scale language models with huge parameters and a large dataset can be adapted to domain adaptation and knowledge transfer. |
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis (D19-1)
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| Challenge: | Existing models use aspect-independent encoders for sentence representation generation. |
| Approach: | They propose an aspect-guided deep transition model which guides the sentence encoding from scratch with a specially-designed deep transition architecture. |
| Outcome: | The proposed model outperforms existing models on multiple datasets on aspect-category sentiment analysis and aspectterm sentiment analysis without additional features. |
Are Intermediate Layers and Labels Really Necessary? A General Language Model Distillation Method (2023.findings-acl)
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| Challenge: | Existing knowledge distillation methods rely on intermediate layer features and golden labels, which require aligned model architecture and labeled data respectively. |
| Approach: | They propose a general language model distillation method that performs two-stage word prediction distillation and vocabulary compression, which is simple and shows extremely strong performance. |
| Outcome: | The proposed method outperforms 25 state-of-the-art methods on the SuperGLUE benchmark, achieving an average score that surpasses the best method by 3%. |
PositionID: LLMs can Control Lengths, Copy and Paste with Explicit Positional Awareness (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have impressive capabilities across various domains, including role-playing, creative writing, mathematical reasoning, and coding. |
| Approach: | They propose two methods to improve the model’s adherence to length constraints and copy-paste accuracy without compromising response quality. |
| Outcome: | The proposed methods improve the model’s adherence to length constraints and copy-paste accuracy without compromising response quality. |
Modeling Graph Structure in Transformer for Better AMR-to-Text Generation (D19-1)
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| Challenge: | Recent studies on AMR-to-text generation formalize the task as a sequence-tosequence learning problem . previous approaches only consider the relations between directly connected concepts while ignoring the rich structure in AMR graphs. |
| Approach: | They propose a structure-aware self-attention approach to model the relations between indirectly connected concepts in the seq2seq model. |
| Outcome: | The proposed approach outperforms the state-of-the-art on English AMR benchmarks . it significantly outperformed the state of the art on the benchmarks, with 29.66 and 31.82 BLEU scores . |
Prompt-based Generation of Natural Language Explanations of Synthetic Lethality for Cancer Drug Discovery (2024.lrec-main)
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| Challenge: | Synthetic lethality (SL) is a genetic interaction where a single gene mutation allows cell survival, but simultaneous mutations in two genes lead to cell death. |
| Approach: | They propose a prompt-based pipeline for generating natural language explanations using a dataset derived from New Bing . |
| Outcome: | The proposed pipeline improves on existing biomedical language models in terms of text quality and explainability. |
Simple and Effective Curriculum Pointer-Generator Networks for Reading Comprehension over Long Narratives (P19-1)
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Yi Tay, Shuohang Wang, Anh Tuan Luu, Jie Fu, Minh C. Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, Aston Zhang
| Challenge: | Using a pointer-generator framework for reading/sampling over large documents, we propose a framework for learning over long narratives where documents easily span over thousands of tokens. |
| Approach: | They propose a curriculum learning (CL) based pointer-generator framework for reading/sampling over large documents, enabling diverse training of the neural model based on the notion of alternating contextual difficulty. |
| Outcome: | The proposed framework improves on the NarrativeQA reading comprehension benchmark and reaches state-of-the-art performance. |
Calibrated Language Model Fine-Tuning for In- and Out-of-Distribution Data (2020.emnlp-main)
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| Challenge: | Pre-trained language models suffer from severe miscalibration for both in-distribution and out-of-difference data due to over-parameterization. |
| Approach: | They propose a regularized method to improve in-distribution and out-of-distance calibrations by using on-manifold regularization and off-manfold regularisation. |
| Outcome: | The proposed method outperforms existing methods for text classification in terms of expectation calibration error, misclassification detection, and OOD detection on six datasets. |
Governance in Motion: Co-evolution of Constitutions and AI models for Scalable Safety (2025.emnlp-main)
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Chenhao Huang, Ziyu Shen, Yicong Ren, Huiyuan Zheng, Jiazheng Zhang, Mingxu Chai, Ming Zhang, Shihan Dou, Fan Mo, Jie Shi, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to align large language models with human preferences lack flexibility . static alignment preferences lack the ability to correct misaligned behaviors as they emerge . |
| Approach: | They propose a framework that enables dynamic and continuous alignment of large language models with human preferences. |
| Outcome: | The proposed framework improves safety and accuracy of a 7B model with human annotations. |
LaMP-Val: Large Language Models Empower Personalized Valuation in Auction (2025.findings-emnlp)
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Jie Sun, Tianyu Zhang, Houcheng Jiang, Kexin Huang, Xiang Shu, Zhibo Zhu, Lintao Ma, Xingyu Lu, Jun Zhou, Junkang Wu, Chi Luo, An Zhang, Jiancan Wu, Xiang Wang
| Challenge: | Currently, most research focuses on the bidding algorithms used within auction mechanisms. |
| Approach: | They propose a personalized valuation framework that integrates Large Language Models to incorporate personalized semantic preference into users valuation process. |
| Outcome: | The proposed framework incorporates Large Language Models to incorporate personalized semantic preference into users valuation process. |
KARL: Reinforcement Learning for LLM Agents on Multi-Turn Knowledge-Intensive Agentic Tasks (2026.acl-long)
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Xueqiao Sun, Xiao Liu, Bowen Lv, Hanchen Zhang, Bohao Jing, Zehan Qi, Yifan Xu, Yuxiao Dong, Jie Tang
| Challenge: | Large Language Models have shown remarkable potential as autonomous agents, but their effectiveness in knowledge-intensive tasks remains limited by passive knowledge utilization. |
| Approach: | They propose a framework that enables LLM agents to dynamically explore structured knowledge sources through multi-turn interactions. |
| Outcome: | The proposed framework outperforms existing retrieval-augmented approaches on knowledge graph and database tasks while maximizing tool-use behaviors end-to-end. |
Adaptive and Representative Multi-Interest Modeling for Recommendation with Large Language Model (2026.findings-acl)
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| Challenge: | Existing methods for multi-interest analysis of users rely on heuristic assumptions . however, the granularity of raw generation of LLMs is agnostic, leading to overly fine or coarse interest grouping. |
| Approach: | They propose an LLM-driven adaptive and representative multi-interest modeling framework that exploits the agnostic granularity of LLMs for multi-interest analysis. |
| Outcome: | The proposed model outperforms baselines on real-world datasets. |
Message Passing on Semantic-Anchor-Graphs for Fine-grained Emotion Representation Learning and Classification (2024.emnlp-main)
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| Challenge: | Emotion classification is an important task with applications in education, virtual reality, and robotics. |
| Approach: | They propose to use token embeddings to generate a "semantic-anchor graph" using semantic anchors, sentences can be projected onto them to form a graph . |
| Outcome: | Empirically, the proposed system can generate meaningful semantic anchors and discriminative graph patterns for different emotion. |
Tell Me More! Towards Implicit User Intention Understanding of Language Model Driven Agents (2024.acl-long)
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Cheng Qian, Bingxiang He, Zhong Zhuang, Jia Deng, Yujia Qin, Xin Cong, Zhong Zhang, Jie Zhou, Yankai Lin, Zhiyuan Liu, Maosong Sun
| Challenge: | Current language model-driven agents lack mechanisms for effective user participation, which is crucial given the vagueness commonly found in user instructions. |
| Approach: | They propose a benchmark to inspect users’ implicit intentions through explicit queries and a model expert as the upstream in agent design to enhance user-agent interaction. |
| Outcome: | The proposed approach excels at identifying vague user tasks, recovering and summarizing critical missing information, setting precise and necessary agent execution goals, and minimizing redundant tool usage, thus boosting overall efficiency. |
Retrieval-Augmented Machine Translation with Unstructured Knowledge (2025.findings-emnlp)
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| Challenge: | Retrieval-augmented generation (RAG) is a new approach to enhance large language models (LLMs). |
| Approach: | They propose a multi-task training method to teach LLMs how to use information from multilingual documents during their translation. |
| Outcome: | The proposed method improves LLMs by 1.6-3.1 BLEU and 1.0-2.0 COMET scores in En-Zh, and 1.7-2.9 BLUE and 2.1-2.7 COMET score in En de. |
Neural Dialogue State Tracking with Temporally Expressive Networks (2020.findings-emnlp)
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| Challenge: | Existing models ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. |
| Approach: | They propose to combine temporal feature dependencies in spoken dialogues by using recurrent networks and probabilistic graphical models. |
| Outcome: | The proposed model improves turn-level-state prediction and state aggregation on standard datasets. |
Modeling Recurrence for Transformer (N19-1)
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| Challenge: | Existing studies show that the lack of recurrence modeling hinders the development of a translation model. |
| Approach: | They propose to model recurrence for Transformer with an additional recurrent encoder. |
| Outcome: | The proposed model outperforms the deep model on EnglishGerman and ChineseEnglish translation tasks. |
LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System (2025.emnlp-demos)
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Yu Chao, Siyu Lin, Xiaorong Wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, Jie Zhou, Zhiyuan Liu, Maosong Sun
| Challenge: | Generating high-quality long-form survey articles poses significant challenges to AI Agent systems. |
| Approach: | They propose a hierarchically modular agent system for long-form survey generation . they use atomic models to implement skeleton initialization, digest construction, and skelet refinement . human evaluations demonstrate system surpasses representative baselines . |
| Outcome: | The proposed system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning. |
Enhancing Dialogue Generation with Conversational Concept Flows (2023.findings-eacl)
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| Challenge: | Existing studies show that explicitly modeling concept flows with a large commonsense knowledge graph improves response quality, but there is a gap between the knowledge graph and the conversation. |
| Approach: | They propose to model human conversational concept flows with a commonsense knowledge graph . they extract abundant concepts and relations from natural conversations and build a conversation-aware knowledge graph. |
| Outcome: | The proposed method performs better than baselines on a large-scale reddit conversation dataset. |
ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning (2025.emnlp-main)
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Ziqing Qiao, Yongheng Deng, Jiali Zeng, Dong Wang, Lai Wei, Guanbo Wang, Fandong Meng, Jie Zhou, Ju Ren, Yaoxue Zhang
| Challenge: | Existing methods for fine-tuning-based compression suffer from verbose outputs, increasing computational overhead. |
| Approach: | They propose a framework to generate concise reasoning chains using Confidence Injection and Early Stopping. |
| Outcome: | The proposed framework reduces the length of the model by up to 50% while maintaining high task accuracy. |
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling (2024.acl-long)
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Jun Zhan, Junqi Dai, Jiasheng Ye, Yunhua Zhou, Dong Zhang, Zhigeng Liu, Xin Zhang, Ruibin Yuan, Ge Zhang, Linyang Li, Hang Yan, Jie Fu, Tao Gui, Tianxiang Sun, Yu-Gang Jiang, Xipeng Qiu
| Challenge: | Existing language models that use discrete representations for unified processing of various modalities are limited to text generation and do not include multimodal output. |
| Approach: | They propose a multimodal language model that utilizes discrete representations for unified processing of various modalities. |
| Outcome: | The proposed model can be trained stably without any alterations to existing models or training paradigms. |
Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation (P19-1)
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| Challenge: | Experimental results show that the Reinforce-NAT system surpasses the baseline NAT system by a significant margin on BLEU without decelerating the decoding speed. |
| Approach: | They propose a sequence-level training method and a Transformer decoder to fuse the target sequential information into the top layer of the decoded Transformer. |
| Outcome: | The proposed model surpasses the baseline NAT system on BLEU without decelerating the decoding speed and achieves comparable translation performance to the autoregressive Transformer model with considerable speedup. |
Improving Multi-turn Dialogue Modelling with Utterance ReWriter (P19-1)
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| Challenge: | Recent research has achieved impressive results in single-turn dialogue modelling, but multi-turn models still remain challenging. |
| Approach: | They propose to rewrite human utterances as a pre-process to help multi-turn dialgoue modelling. |
| Outcome: | The proposed architecture achieves remarkably good performance on the utterance rewriting task. |
A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition (2023.findings-acl)
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Limao Xiong, Jie Zhou, Qunxi Zhu, Xiao Wang, Yuanbin Wu, Qi Zhang, Tao Gui, Xuanjing Huang, Jin Ma, Ying Shan
| Challenge: | Existing models for named entity recognition (NER) are based on large-scale labeled datasets, which always obtain using crowdsourcing. |
| Approach: | They propose a CONfidence-based partial Label Learning method to integrate prior and posterior confidences for crowd-annotated named entity recognition models. |
| Outcome: | The proposed model improves on real-world and synthetic datasets compared with baselines. |
PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning (2026.acl-long)
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Jingcheng Hu, Yinmin Zhang, Shijie Shang, Xiaobo Yang, Yue Peng, Zhewei Huang, Hebin Zhou, Xin Wu, Jie Cheng, Fanqi Wan, Xiangwen Kong, Chengyuan Yao, Kaiwen Yan, Ailin Huang, Hongyu Zhou, Qi Han, Zheng Ge, Xiangyu Zhang, Heung-Yeung Shum
| Challenge: | Parallel Coordinated Reasoning (PaCoRe) overcomes a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window. |
| Approach: | They propose a training-and-inference framework to overcome a central limitation of language models: their inability to scale test-time compute (TTC) under a fixed context window. |
| Outcome: | The proposed model scales to multi-million-token effective TTC without exceeding context limits. |
Unveiling the Flaws: Exploring Imperfections in Synthetic Data and Mitigation Strategies for Large Language Models (2024.findings-emnlp)
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| Challenge: | acquiring large amounts of high-quality data can be challenging due to data scarcity, privacy concerns, and high costs. |
| Approach: | They propose a method which reverses instruction-following issues caused by uniform format of synthetic data and proposes unlearning techniques to mitigate these flaws. |
| Outcome: | The proposed method reverses instruction-following issues caused by pattern overfitting without compromising performance on benchmarks at relatively low cost. |
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck (2022.coling-1)
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| Challenge: | Event argument extraction (EAE) aims to extract arguments with given roles from texts. |
| Approach: | They propose a multi-format transfer learning model with variational information bottleneck to learn from existing datasets. |
| Outcome: | The proposed model improves on three benchmark datasets and obtains state-of-the-art performance on EAE. |
Exploring Mathematical Extrapolation of Large Language Models with Synthetic Data (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have shown excellent capabilities in language understanding, text generation and many other tasks, but struggle in complex multi-step reasoning problems such as mathematical reasoning. |
| Approach: | They propose to fine tune an open-llama-3B model to perform well on multi-step reasoning tasks via synthetic data. |
| Outcome: | The proposed model can reach a zero-shot pass@1 at 0.44 on the in-domain dataset and demonstrates certain generalization capabilities on the out-of-domain data. |
Self-Training with Pseudo-Label Scorer for Aspect Sentiment Quad Prediction (2024.acl-long)
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| Challenge: | Aspect Sentiment Quad Prediction (ASQP) aims to predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review. |
| Approach: | They propose a self-training framework with a pseudo-label scorer to assess the match between reviews and their pseudo-labels and train a generative model on it. |
| Outcome: | The proposed framework can predict all quads (aspect term, aspect category, opinion term, sentiment polarity) for a given review, and it can significantly improve self-training. |
INFACT: A Diagnostic Benchmark for Induced Faithfulness and Factuality Hallucinations in Video-LLMs (2026.acl-long)
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| Challenge: | Existing benchmarks only evaluate models in clean settings due to hallucinations . |
| Approach: | They propose a diagnostic benchmark that evaluates models in four modes for faithfulness and factuality. |
| Outcome: | The proposed benchmark evaluates models in four modes: Base (clean), Visual Degradation, Evidence Corruption, and Temporal Intervention for order-sensitive items. |
Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language Model (2023.findings-acl)
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Xiao Wang, Weikang Zhou, Qi Zhang, Jie Zhou, SongYang Gao, Junzhe Wang, Menghan Zhang, Xiang Gao, Yun Wen Chen, Tao Gui
| Challenge: | Pretrained language models have achieved remarkable success in various natural language processing tasks. |
| Approach: | They propose to use end-task knowledge to select a tiny subset of pretraining corpus to influence performance. |
| Outcome: | The proposed model outperforms pretrained models on eight datasets covering four domains with 0.45% of the data and a three-orders-of-magnitude lower computational cost. |
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack (2020.coling-main)
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| Challenge: | Existing approaches to zero-shot slot filling ignore constraints in the latent space and lack robustness. |
| Approach: | They propose a Contrastive Zero-Shot Learning with Adversarial Attack method for slot filling . they propose to map slot value contextual representations to slot description representations . |
| Outcome: | The proposed method outperforms state-of-the-art models under zero-shot and few-shot settings. |
CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging (2026.findings-acl)
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Jie Cao, Zhenxuan Fan, Zhuonan Wang, Tianwei Lin, Ziyuan Zhao, Rolan Yan, Wenqiao Zhang, Feifei Shao, Hongwei Wang, Jun Xiao, Siliang Tang
| Challenge: | Existing PEFT methods suffer from limited parameter efficiency and coarse-grained adaptation due to proliferation of LoRA experts and instance-level routing. |
| Approach: | They propose a new MoE-LoRA framework that incorporates expert diversity, parameter efficiency, and fine-grained adaptation. |
| Outcome: | The proposed framework outperforms existing methods on multiple tasks while maintaining parameter efficiency. |
Domain Generalization via Causal Adjustment for Cross-Domain Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing approaches to domain adaptation fail to generalize well on unknown test data. |
| Approach: | They propose a backdoor adjustment-based causal model to disentangle domain-specific and domain-invariant representations that play essential roles in tackling domain shift. |
| Outcome: | The proposed model disentangles domain-specific and domain-invariant representations that play essential roles in tackling domain shift. |
Forget the Unneeded: Backdooring Large Language Models via Contrastive-enhanced Machine Unlearning (2025.findings-emnlp)
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| Challenge: | Existing methods for prompt tuning for Large Language Models find backdoor attacks to be significant in data-rich scenarios. |
| Approach: | They propose a backdoor attacks through contrastive-enhanced machine unlearning in data-limited scenarios . they use a machine un learning method to capture precise backdoor patterns . |
| Outcome: | The proposed method captures precise backdoor patterns without association between triggers and backdoors, reducing side effects. |
AgentDropout: Dynamic Agent Elimination for Token-Efficient and High-Performance LLM-Based Multi-Agent Collaboration (2025.acl-long)
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| Challenge: | Existing methods for MAS suffer from high token consumption and inefficiency due to frequent generation and communication among multiple agents. |
| Approach: | They propose a multi-agent system based on large language models that identifies redundant agents and communication across different communication rounds by optimizing the adjacency matrices of the communication graphs and eliminates them to enhance both token efficiency and task performance. |
| Outcome: | The proposed method reduces prompt token consumption and completion token consumption by 18.4% and improves task performance by 1.14. |
ToM: Leveraging Tree-oriented MapReduce for Long-Context Reasoning in Large Language Models (2025.emnlp-main)
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| Challenge: | Experimental results show ToM outperforms existing divide-and-conquer frameworks . RAG relies on similarity-based rankings to retrieve and reason over chunks based on logical coherence . |
| Approach: | They propose a Tree-oriented MapReduce framework for long-context reasoning . it leverages the hierarchical structure of long documents by constructing a DocTree . |
| Outcome: | Experimental results show that ToM outperforms existing divide-and-conquer frameworks and RAGs . the proposed framework improves logical coherence and long-context reasoning on 70B+ LLMs compared to existing approaches . |
Finite State Automata Inside Transformers with Chain-of-Thought: A Mechanistic Study on State Tracking (2025.acl-long)
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| Challenge: | Existing studies show that Chain-of-thought (CoT) can enhance the performance of large language models (LLMs) however, there is limited understanding of the algorithms that Transformer+CoT can learn. |
| Approach: | They propose two metrics to evaluate Transformer+CoT's state tracking capabilities and identify the circuit responsible for tracking the world state. |
| Outcome: | The proposed model achieves 100% accuracy for each state, highlighting an implicit finite state automaton (FSA) embedded within the model. |
Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)
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Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang
| Challenge: | Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets. |
| Approach: | They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains. |
| Outcome: | The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings. |
Multi-Granularity Semantic Revision for Large Language Model Distillation (2026.acl-long)
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Xiaoyu Liu, Yun Zhang, Wei Li, Simiao Li, Xudong Huang, Hanting Chen, Yehui Tang, Jie Hu, Zhiwei Xiong, Yunhe Wang
| Challenge: | Existing methods for generating large language models rely on student-generated outputs, which introduce generation errors and misguide the distillation process. |
| Approach: | They propose a multi-granularity semantic revision method for LLM distillation that corrects errors using teacher-generated tokens and re-generates the sequence to minimize errors. |
| Outcome: | The proposed method reduces errors and misguides distillation on student models and improves consistency between teacher and student outputs. |
Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data (2026.acl-long)
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Shiping Yang, Jie Wu, Wenbiao Ding, Ning Wu, Shining Liang, Ming Gong, Hongzhi Li, Hengyuan Zhang, Angel X. Chang, Dongmei Zhang
| Challenge: | Existing studies on robustness to explicit noise (e.g., document semantics) but overlook implicit noise (spurious features). |
| Approach: | They propose a framework to quantify the robustness of RAGs against spurious features by integrating a data synthesis pipeline and a taxonomy. |
| Outcome: | The proposed framework quantifies the robustness of RALMs against spurious features. |
Enhancing Language Model Hypernetworks with Restart: A Study on Optimization (2025.naacl-long)
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| Challenge: | a comprehensive investigation into optimization strategies for hypernetworks remains lacking. |
| Approach: | They propose restart optimization strategies to improve hypernetworks' performance for language models. |
| Outcome: | The proposed restart strategy improves hypernetworks' performance for language models, compared to conventional deep neural networks. |
Critic-CoT: Boosting the Reasoning Abilities of Large Language Model via Chain-of-Thought Critic (2025.findings-acl)
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Xin Zheng, Jie Lou, Boxi Cao, Xueru Wen, Yuqiu Ji, Hongyu Lin, Yaojie Lu, Xianpei Han, Debing Zhang, Le Sun
| Challenge: | Existing approaches to improve the reasoning performance of large language models rely on intuitive instance-level feedback, which limits the reasoning capabilities. |
| Approach: | They propose a framework that pushes LLMs toward System-2-like critic capability by using a step-wise CoT reasoning paradigm and automatic construction of weak-supervision data without human annotation. |
| Outcome: | The proposed model significantly improves task-solving performance by filtering out invalid solutions or iterative refinement. |
SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue (2026.findings-acl)
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| Challenge: | Large Language Models have demonstrated remarkable capabilities in open-domain dialogues, but their performance in service dialogues remains suboptimal. |
| Approach: | They propose a framework that enables agents to learn effective strategies without large-scale human annotations. |
| Outcome: | The proposed framework decouples user modeling into two components that provide adaptive training scenarios rather than acting as an unfair adversary. |
MTP-RL: Acceleration of Reinforcement Learning Rollouts with Policy-Aligned Multi-Token Prediction (2026.findings-acl)
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| Challenge: | Reinforcement learning (RL) is widely applied to boost the performance of pretrained models, yet its training efficiency is severely constrained by rollout generation. |
| Approach: | They propose a framework that accelerates the rollout phase for diverse models by equipping a pipeline to equip the multi-layer parameter-sharing MTP for all models and an advantage-aware MTP optimization strategy. |
| Outcome: | The proposed framework achieves stable growth of acceptance length during RL training, and also accelerates RL rollouts, achieving an average 23.1%–55.3% reduction in rollout time compared to baselines. |
LongDPO: Unlock Better Long-form Generation Abilities for LLMs via Critique-augmented Stepwise Information (2025.findings-acl)
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| Challenge: | Recent advances in large language models have improved their capacity to handle long text inputs, but current models still exhibit unsatisfactory performance in long-form generation. |
| Approach: | They propose a method to enhance long-form text generation through step-level supervision by leveraging Monte Carlo Tree Search to collect stepwise preference pairs and employ a global memory pool to maintain factual accuracy. |
| Outcome: | The proposed method improves performance on long-form generation benchmarks while maintaining lossless performance on several general benchmarks. |
GUI-explorer: Autonomous Exploration and Mining of Transition-aware Knowledge for GUI Agent (2025.acl-long)
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| Challenge: | GUI automation is a key challenge in dynamic environments. |
| Approach: | They propose a training-free GUI agent that integrates two mechanisms to explore trajectories in GUIs. |
| Outcome: | The proposed GUI-explorer shows significant improvements over existing agents. |
An Operation Network for Abstractive Sentence Compression (C18-1)
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| Challenge: | Sentence compression is a natural language generation task that condenses a sentence . Delete-based models remove unimportant words from the source sentence and generate a shorter sentence if the source is not a word deletion problem. |
| Approach: | They propose a neural network approach for abstractive sentence compression . they model the sentence compression process as an editing procedure . |
| Outcome: | The proposed approach outperforms state-of-the-art models in the abstractive sentence compression field. |
ZoomRAG: Hierarchical Random-walk Zooming across Multi-scale Information Graphs for Fast and Accurate RAG (2026.findings-acl)
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Xianming Hu, Jingyang Chen, Bin Tang, Yihe Liu, Yihong Huang, Hongbo Zhao, Nuoyi Chen, Jie Zhang, Ping Li, Kai Zhang
| Challenge: | retrieval-augmented generation (RAG) is a powerful tool for NLP applications . but it is challenging to encode large knowledge bases as compact offline structures . |
| Approach: | They propose a coarse-to-fine hierarchical graph inference method that uses random walks to retrieve information from a corpus of documents. |
| Outcome: | The proposed method reduces offline indexing costs and accelerates retrieval. |
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search (2023.findings-emnlp)
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| Challenge: | Existing methods for generating adversarial code examples face challenges such as limted availability of substitute variables and the creation of adversarials with noticeable perturbations. |
| Approach: | They propose a search seed based on historical attacks to find adversarial substitutes . they employ a pre-trained variable name encoder to map the search seed to a continuous vector space . |
| Outcome: | The proposed approach outperforms baseline methods in terms of ASR and QT. |
AlignBench: Benchmarking Chinese Alignment of Large Language Models (2024.acl-long)
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Xiao Liu, Xuanyu Lei, Shengyuan Wang, Yue Huang, Andrew Feng, Bosi Wen, Jiale Cheng, Pei Ke, Yifan Xu, Weng Lam Tam, Xiaohan Zhang, Lichao Sun, Xiaotao Gu, Hongning Wang, Jing Zhang, Minlie Huang, Yuxiao Dong, Jie Tang
| Challenge: | Effective evaluation of alignment for emerging Chinese LLMs is still significantly lacking, calling for real-scenario grounded, open-ended, challenging and automatic evaluations tailored for alignment. |
| Approach: | They propose a multi-dimensional benchmark for evaluating LLMs’ alignment in Chinese with 8 main categories, 683 real-scenario rooted queries and corresponding human verified references. |
| Outcome: | The benchmark uses a human-in-the-loop data curation pipeline, 683 real-scenario rooted queries and human verified references. |
A Survey of Post-Training Scaling in Large Language Models (2025.acl-long)
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Hanyu Lai, Xiao Liu, Junjie Gao, Jiale Cheng, Zehan Qi, Yifan Xu, Shuntian Yao, Dan Zhang, Jinhua Du, Zhenyu Hou, Xin Lv, Minlie Huang, Yuxiao Dong, Jie Tang
| Challenge: | Large language models (LLMs) have demonstrated proficiency in understanding and generating human natural languages. |
| Approach: | They propose a framework for scaling large language models using supervised fine-tuning, RLxF and test-time compute methodologies. |
| Outcome: | The proposed model can be used to understand and generate human natural languages. |
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks (2025.acl-long)
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Yushi Bai, Shangqing Tu, Jiajie Zhang, Hao Peng, Xiaozhi Wang, Xin Lv, Shulin Cao, Jiazheng Xu, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li
| Challenge: | Existing benchmarks on longcontext large language models fail to reflect their deep understanding capabilities across diverse tasks. |
| Approach: | They propose a benchmark to assess the ability of long-context large language models to handle long-text problems. |
| Outcome: | The proposed model achieves 50.1% accuracy when directly answering the questions . human experts achieve only 53.7% accuracy under a 15-minute time constraint . |
RPC-Bench: A Fine-grained Benchmark for Research Paper Comprehension (2026.acl-long)
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Yelin Chen, Fanjin Zhang, Suping Sun, Yunhe Pang, Yuanchun Wang, Jian Song, XiaoYan Li, Lei Hou, Shu Zhao, Jie Tang, Juanzi Li
| Challenge: | Existing benchmarks for understanding research papers offer limited fine-grained evaluation at scale. |
| Approach: | They propose a large-scale question-answering benchmark built from review–rebuttal exchanges of high-quality computer science papers. |
| Outcome: | The proposed model is based on human-verified QA pairs and contains 15K questions. |
VENUS: A VLLM-driven Video Content Discovery System for Real Application Scenarios (2025.emnlp-industry)
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Minyi Zhao, Yi Liu, Jianfeng Wen, Boshen Zhang, Hailang Chang, Zhiheng Ouyang, Jie Wang, Wensong He, Shuigeng Zhou
| Challenge: | Video Content Discovery (VCD) is to identify specific videos defined by a pre-specified text policy. |
| Approach: | They propose a Vision-Language Large Model-driven video content discovery system called VENUS to solve these problems. |
| Outcome: | The proposed system generates high-quality, VCD-specific data for model training and extends it to support it better. |
LongCLI-Bench: A Preliminary Benchmark and Study for Long-horizon Agentic Programming in Command-Line Interfaces (2026.findings-acl)
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Yukang Feng, Jianwen Sun, Zelai Yang, Jiaxin Ai, Chuanhao Li, Zizhen Li, Fanrui Zhang, Kang He, Rui Ma, Jifan Lin, Jie Sun, Yang Xiao, Sizhuo Zhou, Wenxiao Wu, Yiming Liu, Pengfei Liu, Shenglin Zhang, Kaipeng Zhang
| Challenge: | Existing benchmarks for agentic programming in long-horizon command-line interface tasks are limited by short task horizons, data contamination from GitHub scraping, and a lack of fine-grained evaluation metrics. |
| Approach: | They propose a benchmark to evaluate agentic capabilities across long-horizon command-line interface tasks. |
| Outcome: | The proposed benchmarks cover four engineering categories: from scratch, feature addition, bug fixing, and refactoring. |
MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving (2022.findings-naacl)
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| Challenge: | Existing work on math word problem solvers replace real numbers with symbolic placeholders to focus on logic reasoning. |
| Approach: | They propose to inject numerical properties into symbolic placeholders with contextualized representation learning schema to solve number representation dilemma. |
| Outcome: | The proposed model can solve MWP problems on English and Chinese benchmarks. |
Multi-Level Cross-Modal Alignment for Speech Relation Extraction (2024.emnlp-main)
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Liang Zhang, Zhen Yang, Biao Fu, Ziyao Lu, Liangying Shao, Shiyu Liu, Fandong Meng, Jie Zhou, Xiaoli Wang, Jinsong Su
| Challenge: | Existing studies use synthetic speech to train and evaluate SpeechRE models, hindering their development . modality gap issue limits performance of existing models, limiting future researches . |
| Approach: | They propose to use speech data to train and evaluate SpeechRE models by using real speech . they propose to train a cross-modal alignment model to bridge the modality gap . |
| Outcome: | The proposed model can train to bridge the modality gap between speech encoder and text decoder . the proposed model is based on two real SpeechRE datasets . |
CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs (2026.findings-acl)
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Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, Qiang Zhu
| Challenge: | Existing approaches to constraint-aware planning fail to enhance the model’s intrinsic focus on constraints. |
| Approach: | They propose a constraint-aware reinforcement learning framework that encourages constraint focus and penalizes neglect of LLMs. |
| Outcome: | The proposed framework outperforms existing frameworks and state-of-the-art reasoning models in a number of real-world applications. |
HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering (2025.findings-emnlp)
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| Challenge: | graph neural networks capture structured graph information, but lack integration at the reasoning level. |
| Approach: | They propose a framework that leverages graph structural information to reason interpretable academic QA results. |
| Outcome: | The proposed framework outperforms sota baselines on OpenAlex and DBLP datasets. |
The Rise of Darkness: Safety-Utility Trade-Offs in Role-Playing Dialogue Agents (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) demonstrate their utility in character simulations, but they pose a risk of generating unsafe content. |
| Approach: | They propose a method which dynamically adjusts safety-utility preferences based on the degree of risk coupling and guides the model to generate responses biased toward utility or safety. |
| Outcome: | The proposed method improves safety metrics while maintaining utility. |
TTVS: Boosting Self-Exploring Reinforcement Learning via Test-time Variational Synthesis (2026.findings-acl)
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| Challenge: | Existing test-time methods are limited in specialized or novel domains where supervision is prohibitively expensive or unavailable. |
| Approach: | They propose a framework that augments training stream from unlabeled test queries. |
| Outcome: | Extensive experiments show TTVS outperforms state-of-the-art RL-based techniques on unlabeled test-time data. |
Unifying Discrete and Continuous Representations for Unsupervised Paraphrase Generation (2023.emnlp-main)
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Mingfeng Xue, Dayiheng Liu, Wenqiang Lei, Jie Fu, Jian Lan, Mei Li, Baosong Yang, Jun Xie, Yidan Zhang, Dezhong Peng, Jiancheng Lv
| Challenge: | Existing unsupervised paraphrase generation methods require large-scale, manually annotated paraphrase datasets, which are labor-intensive to build. |
| Approach: | They propose a self-supervised pseudo-data construction method that generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
| Outcome: | The proposed method generates diverse pseudo-paraphrases in distinct surface structures for a given sentence. |
MoA: Heterogeneous Mixture of Adapters for Parameter-Efficient Fine-Tuning of Large Language Models (2026.acl-long)
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Jie Cao, Tianwei Lin, Bo Yuan, Rolan Yan, Hongyang He, Wenqiao Zhang, Juncheng Li, Dongping Zhang, Siliang Tang, Yueting Zhuang
| Challenge: | Existing methods for parameter-efficient fine-tuning (PEFT) are limited by computational costs and performance degradation. |
| Approach: | They propose a method that integrates Low-Rank Adaptation and Mixture-of-Experts (MoE) they propose combining expert load imbalance and representation collapse to improve LLM performance . |
| Outcome: | The proposed method outperforms homogeneous MoE-LoRA architectures in performance and parameter efficiency. |
Beyond Text: Incorporating Metadata and Label Structure for Multi-Label Document Classification using Heterogeneous Graphs (2021.emnlp-main)
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| Challenge: | Existing methods for multi-label document classification ignore the heterogeneous graphical structures of metadata and labels. |
| Approach: | They propose a neural network based approach to multi-label document classification that uses two heterogeneous graphs to model metadata and labels. |
| Outcome: | The proposed approach outperforms state-of-the-art models on two benchmark datasets. |
Uncover the Ground-Truth Relations in Distant Supervision: A Neural Expectation-Maximization Framework (D19-1)
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| Challenge: | Existing methods for relation extraction assume that text is noisy, but its corresponding labels are clean. |
| Approach: | They propose a framework that combines neural network and probabilistic modelling to denoise noisy relation labels. |
| Outcome: | The proposed framework improves the current art in uncovering the ground-truth relation labels. |
Target-oriented Fine-tuning for Zero-Resource Named Entity Recognition (2021.findings-acl)
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| Challenge: | Named entity recognition (NER) is one of the fundamental tasks in natural language processing. |
| Approach: | They propose four practical guidelines to guide knowledge transfer and task finetuning . they propose a framework to exploit data from three aspects in a unified training manner . |
| Outcome: | The proposed framework improves on six benchmarks and shows that it is state-of-the-art in five languages. |
Towards Tracing Trustworthiness Dynamics: Revisiting Pre-training Period of Large Language Models (2024.findings-acl)
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| Challenge: | Existing studies focus on pre-trained LLMs to better understand and improve their trustworthiness. |
| Approach: | They apply linear probing to LLMs to explore five key dimensions of trustworthiness: reliability, privacy, toxicity, fairness, and robustness. |
| Outcome: | The proposed model can distinguish concepts in each trustworthiness dimension, suggesting that it can be trained in early pre-training. |
Repulsive Attention: Rethinking Multi-head Attention as Bayesian Inference (2020.emnlp-main)
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Bang An, Jie Lyu, Zhenyi Wang, Chunyuan Li, Changwei Hu, Fei Tan, Ruiyi Zhang, Yifan Hu, Changyou Chen
| Challenge: | Existing studies show that multi-head attention is an effective module in deep neural networks, but there are no explicit mechanisms guaranteeing this property. |
| Approach: | They propose a non-parametric approach that explicitly improves the repulsiveness in multi-head attention and consequently strengthens model’s expressiveness. |
| Outcome: | The proposed approach improves the repulsiveness in multi-head attention and strengthens model’s expressiveness. |
Deep Cognitive Reasoning Network for Multi-hop Question Answering over Knowledge Graphs (2021.findings-acl)
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| Challenge: | Knowledge Graphs (KGs) store structured human knowledge with nodes and edges being entities and relations between them. |
| Approach: | They propose a deep cognitive reasoning network that uses two phases to find answers in large candidate entity sets. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on benchmark datasets. |
On-Policy Self-Alignment with Fine-grained Knowledge Feedback for Hallucination Mitigation (2025.findings-acl)
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Xueru Wen, Jie Lou, Xinyu Lu, Yuqiu Ji, Xinyan Guan, Yaojie Lu, Hongyu Lin, Ben He, Xianpei Han, Debing Zhang, Le Sun
| Challenge: | Large language models exhibit behavior that deviates from the boundaries of their knowledge during response generation. |
| Approach: | They propose a framework that allows large language models to explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals. |
| Outcome: | The proposed framework enables LLMs to explore their knowledge boundaries and self-correct generation behavior through fine-grained feedback signals. |
CDT: A Comprehensive Capability Framework for Large Language Models Across Cognition, Domain, and Task (2025.findings-emnlp)
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| Challenge: | Existing benchmarks focus on isolated abilities, lacking a holistic framework for assessing LLM capabilities. |
| Approach: | They propose a Cognition-Domain-Task framework which measures a model’s capabilities across three dimensions. |
| Outcome: | The proposed framework improves performance on dataset evaluation and data selection, while achieving higher scores on general and specific benchmarks. |
Disentangle-based Continual Graph Representation Learning (2020.emnlp-main)
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| Challenge: | Existing graph embedding methods overlook streaming nature of incoming data in real-world applications. |
| Approach: | They propose a disentangle-based continual graph representation learning framework inspired by the human’s ability to learn procedural knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art continual graph representation learning framework and alleviate catastrophic forgetting problem. |
NCRF++: An Open-source Neural Sequence Labeling Toolkit (P18-4)
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| Challenge: | Existing statistical approaches to neural sequence labeling have been used for many tasks. |
| Approach: | They describe a toolkit for neural sequence labeling that provides a CRF inference layer for quick implementation. |
| Outcome: | The toolkit is based on PyTorch and can be run on GPUs. |
Data Augmentation for Multiclass Utterance Classification – A Systematic Study (2020.coling-main)
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| Challenge: | a lack of sufficient training data for some categories can cause imbalanced data distributions . a weak classifier may miscategorize a request, resulting in customer dissatisfaction . |
| Approach: | They propose to use random resampling, word-level transformations and neural text generation to augment existing data to cope with imbalanced data. |
| Outcome: | The proposed methods improve utterance classification results by drawing on utterant variation. |
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. |
Emergent Modularity in Pre-trained Transformers (2023.findings-acl)
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Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Chaojun Xiao, Xiaozhi Wang, Xu Han, Zhiyuan Liu, Ruobing Xie, Maosong Sun, Jie Zhou
| Challenge: | Existing studies on pre-trained Transformers show that they learn fine-grained neuron functions. |
| Approach: | They examine the presence of modularity in pre-trained Transformers . they focus on Mixture-of-Experts, a promising candidate for modularity . |
| Outcome: | The proposed structure stabilizes at the early stage, which is faster than neuron stabilization. |
CM-Align: Consistency-based Multilingual Alignment for Large Language Models (2025.findings-emnlp)
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| Challenge: | Current large language models (LLMs) show a significant performance gap in alignment between English and other languages. |
| Approach: | They propose a consistency-based method to construct high-quality multilingual preference data for improving multilingual alignment. |
| Outcome: | The proposed method is based on three LLMs and three common tasks and shows that it performs better than current methods. |
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. |
Constructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation (2021.findings-emnlp)
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| Challenge: | Existing models for dialogue empathy focus on the emotion flow in one direction, from context to response. |
| Approach: | They propose a dual-generative model to construct emotional consensus and use unpaired data to produce pseudo paired empathetic samples. |
| Outcome: | The proposed model outperforms baseline models in producing coherent and empathetic responses. |
TIU-Bench: A Benchmark for Evaluating Large Multimodal Models on Text-rich Image Understanding (2025.findings-emnlp)
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| Challenge: | Existing text-rich image understanding benchmarks lack scale and fragmented scenarios . a new full-image structured output format is proposed to enable fine-grained evaluation of perception and reasoning capabilities. |
| Approach: | They propose a large-scale, multilingual benchmark that includes over 100,000 annotations and 22,000 question-answer pairs. |
| Outcome: | The proposed framework provides a comprehensive platform for developing and evaluating next-generation multimodal AI systems. |
Internalizing Multi-Agent Reasoning for Accurate and Efficient LLM-based Recommendation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) are reshaping recommender systems by leveraging extensive world knowledge and semantic reasoning to interpret user intent. |
| Approach: | They propose a single-agent Trajectory-Aligned Recommender to integrate reasoning capabilities into a model by a multi-agend teacher system. |
| Outcome: | The proposed model surpasses its teacher by 8.7% to 39.5% while eliminating iterative latency. |
Glyph: Scaling Context Windows via Visual-Text Compression (2026.acl-long)
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Jiale Cheng, Yusen Liu, Xinyu Zhang, Yulin Fei, Wenyi Hong, Ruiliang Lyu, Weihan Wang, Zhe Su, Xiaotao Gu, Xiao Liu, Yushi Bai, Jie Tang, Hongning Wang, Minlie Huang
| Challenge: | Large language models (LLMs) traditionally represent text as sequences of discrete tokens . a long-context scaling problem requires processing more tokens more efficiently . |
| Approach: | They propose a framework that renders long texts into compact visual pages and processes them with a vision-language model. |
| Outcome: | The proposed framework renders long texts into compact visual pages and processes them with a vision-language model. |
The Good and The Bad: Exploring Privacy Issues in Retrieval-Augmented Generation (RAG) (2024.findings-acl)
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Shenglai Zeng, Jiankun Zhang, Pengfei He, Yiding Liu, Yue Xing, Han Xu, Jie Ren, Yi Chang, Shuaiqiang Wang, Dawei Yin, Jiliang Tang
| Challenge: | Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model generation with proprietary and private data, where data privacy is . a privacy issue that is currently under-explored, is posed by RAG. |
| Approach: | They propose to use retrieval-augmented generation (RAG) to facilitate language model generation with proprietary and private data where data privacy is a pivotal concern. |
| Outcome: | The proposed attack methods demonstrate that RAG can mitigate the old risks, i.e., leakage of the LLMs’ training data. |
Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information (2023.findings-emnlp)
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| Challenge: | Argument structure extraction (ASE) aims to identify the discourse structure of arguments within documents. |
| Approach: | They propose an Efficient Context-aware ASE model that fully exploits contextual information by augmenting modeling capacity and augmenting training data. |
| Outcome: | The proposed model can extract argumentative discourse structure from documents and reduce reliance on specific words or less informative sentences. |
Benchmarking Diverse-Modal Entity Linking with Generative Models (2023.findings-acl)
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Sijia Wang, Alexander Hanbo Li, Henghui Zhu, Sheng Zhang, Pramuditha Perera, Chung-Wei Hang, Jie Ma, William Yang Wang, Zhiguo Wang, Vittorio Castelli, Bing Xiang, Patrick Ng
| Challenge: | Existing models for diverse-mode entity linking (EL) work well on per modality configurations, but it is more challenging to design a unified model for diverse modality. |
| Approach: | They propose a generative diverse-modal model that integrates text, image and table . they propose combining a multimodal encoder-decoder paradigm with a fine-tuning GDMM . |
| Outcome: | The proposed model outperforms state-of-the-art models by 8.51 F1 on average for diverse-modal EL. |
Controllable Style Arithmetic with Language Models (2025.acl-long)
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| Challenge: | Existing methods for linguistic style control lack fine-grained control, require extensive computation, or introduce significant latency. |
| Approach: | They propose a parameter-space approach that extracts style-specific representations by analyzing parameter differences between models trained on contrasting styles and incorporates them into a model with precise control over style intensity. |
| Outcome: | The proposed approach achieves three key capabilities while achieving optimal computational efficiency. |
Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models (2026.acl-long)
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| Challenge: | Recent Large Reasoning Models (LRMs) excel at complex reasoning tasks but often suffer from overthinking. |
| Approach: | They propose a two-stage fine-tuning strategy that progressively inspires LRMs’ difficulty cognition and redundancy cognition of LRM. |
| Outcome: | The proposed model significantly reduces inference costs by over 70% on easy tasks and 40% on complex ones without compromising performance. |
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models (2024.findings-acl)
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Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Noah Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Wenhao Huang, Chenghua Lin, Jie Fu
| Challenge: | a recent study shows that large language models have limited generalization in low-resource languages like Chinese. |
| Approach: | They propose to evaluate the zero-shot generalizability of large language models to the Chinese language . they release only half of the dataset publicly, with the remainder kept private . |
| Outcome: | The Chinese Instruction-Following Benchmark evaluates the generalizability of LLMs to the Chinese language. |
Divide and Denoise: Learning from Noisy Labels in Fine-Grained Entity Typing with Cluster-Wise Loss Correction (2022.acl-long)
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| Challenge: | Existing FET noise learning methods rely on prediction distributions in instance-independent manner, which causes confirmation bias. |
| Approach: | They propose a clustering-based loss correction framework to address confirmation bias in FET . they first train a coarse backbone model as a feature extractor and noise estimator . |
| Outcome: | The proposed framework achieves the best performance over existing systems on three public datasets and is stable to hyperparameters. |
ChatGLM-Math: Improving Math Problem-Solving in Large Language Models with a Self-Critique Pipeline (2024.findings-emnlp)
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Yifan Xu, Xiao Liu, Xinghan Liu, Zhenyu Hou, Yueyan Li, Xiaohan Zhang, Zihan Wang, Aohan Zeng, Zhengxiao Du, Zhao Wenyi, Jie Tang, Yuxiao Dong
| Challenge: | Large language models (LLMs) have shown excellent mastering of human language but struggle in real-world applications that require mathematical problem-solving. |
| Approach: | They propose a pipeline to train a general Math-Critique model from the LLM itself to provide feedback signals and employ rejective fine-tuning and direct preference optimization over the Llm's own generations for data collection. |
| Outcome: | The proposed pipeline outperforms existing LLMs that could be two times larger. |
Thinking Economically: A Hierarchical Framework for Adaptive-Complexity Reasoning in LLMs (2026.findings-acl)
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Yubo Gao, Haotian Wu, Hong Chen, Junquan Huang, Yibo Yan, Jungang Li, Zihao Dongfang, Sicheng Tao, PS Tan, Jie Zhang, Xuming Hu
| Challenge: | Existing efficiency methods for Chain-of-Thought (CoT) generate excessively long rationales without commensurate accuracy gains. |
| Approach: | They propose a training framework that operationalizes this principle through coarse-to-fine budgeting. |
| Outcome: | Experiments on GSM8K and MATH500 show that HAB surpasses standard CoT in accuracy and reduces token usage, achieving stronger performance-efficiency trade-off than baselines. |
Chameleons and Guardians: Unveiling the Divergence in Personality Plasticity and Cognitive Resistance across LLMs (2026.findings-acl)
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| Challenge: | Existing studies on LLMs argue for its immutability, but prior studies have not found that personality-inducing contexts can be intentionally reshaped. |
| Approach: | They propose a personality-inducing framework that reshapes LLMs via multi-agent collaboration . they paraphrase MBTI questions to create semantically equivalent but expressively diverse inducing contexts . |
| Outcome: | Experiments on worldwide mainstream LLMs show that PIF transforms their original personalities into desired target personalities. |
Localized Low-Rank Adaptation within Clustered Parameter Subspaces (2026.acl-long)
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| Challenge: | Low-Rank Adaptation (LoRA) for large language models has been successful in various domains. |
| Approach: | They propose to perform low-rank updates within clustered parameter subspaces . they group rows/columns of update matrix into locally coherent, uncorrelated subspace blocks . |
| Outcome: | Empirical results show that low-rank Adaptation (LoRA) is better than global adaptations in various domains. |
Hierarchy-Aware Global Model for Hierarchical Text Classification (2020.acl-main)
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| Challenge: | Existing methods for hierarchical text classification are limited and lack holistic structural information. |
| Approach: | They propose a hierarchy-aware global model with two variants that learn hierarchy-based label embeddings through an encoder and conduct inductive fusion of label-alike text features. |
| Outcome: | The proposed model improves on three benchmark datasets. |
HOSMEL: A Hot-Swappable Modularized Entity Linking Toolkit for Chinese (2022.acl-demo)
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| Challenge: | Existing studies have explored the use of entity linking (EL) in downstream tasks. |
| Approach: | They propose a modularized entity linking toolkit for easy task adaptation. |
| Outcome: | The proposed toolkit achieves significantly better accuracy and less time and spaceconsumption than existing methods. |
FFAEval: Evaluating Dialogue System via Free-For-All Ranking (2023.findings-emnlp)
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| Challenge: | Existing evaluation metrics for open-domain dialogue systems show poor correlation with human assessment. |
| Approach: | They propose a free-for-all human evaluation framework that shares dialogue history with annotators for multi-turn scoring. |
| Outcome: | The proposed framework achieves a strong correlation with human assessment on English and Chinese dialogue systems. |
MentalBERT: Publicly Available Pretrained Language Models for Mental Healthcare (2022.lrec-1)
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| Challenge: | Existing pretrained language models for mental health detection are inadequate . one in four people worldwide suffers from mental disorders . |
| Approach: | They train and release two pretrained masked language models to benefit machine learning for mental healthcare research . they demonstrate that language representations pretrained in the target domain improve the performance of mental health detection tasks. |
| Outcome: | The proposed models improve mental health detection tasks on several benchmarks and are available for free. |
MovieChats: Chat like Humans in a Closed Domain (2020.emnlp-main)
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| Challenge: | Currently, open-domain chatbots are far from satisfactory. |
| Approach: | They propose a unified, readily scalable neural approach which reconciles all subtasks like intent prediction and knowledge retrieval. |
| Outcome: | The proposed approach outperforms commercial systems replying on complex rules on static and interactive tests and shows that the results are remarkably good. |
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. |
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding (2024.lrec-main)
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| Challenge: | Existing methods for decoding target language are degenerate, hallucinating or empty. |
| Approach: | They propose a method that tunes down the Softmax temperature to reduce autoregressive over-smoothness by label smoothing the output distributions. |
| Outcome: | The proposed method improves MBR in various settings. |
TopoDIM: One-shot Topology Generation of Diverse Interaction Modes for Multi-Agent Systems (2026.findings-acl)
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| Challenge: | Existing communication topologies rely on spatio-temporal dialogues, which incur high latency and computation. |
| Approach: | They propose a framework for one-shot Topology generation with Diverse Interaction Modes that enables agents to construct heterogeneous communication without iterative coordination. |
| Outcome: | The proposed framework reduces total token consumption by 46.41% while improving average performance by 1.50% over state-of-the-art methods. |
BMInf: An Efficient Toolkit for Big Model Inference and Tuning (2022.acl-demo)
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Xu Han, Guoyang Zeng, Weilin Zhao, Zhiyuan Liu, Zhengyan Zhang, Jie Zhou, Jun Zhang, Jia Chao, Maosong Sun
| Challenge: | Recent years, pre-trained language models (PLMs) have achieved promising results on various NLP tasks. |
| Approach: | They propose an open-source toolkit for big model inference and tuning which can support big model tuning at extremely low computation cost. |
| Outcome: | The proposed toolkit can support big model inference and tuning at extremely low computation cost. |
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. |
Generating Authentic Adversarial Examples beyond Meaning-preserving with Doubly Round-trip Translation (2022.naacl-main)
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| Challenge: | Existing approaches to generate adversarial examples for NMT use the meaning-preserving restriction. |
| Approach: | They propose a new definition for adversarial examples based on the Doubly Round-Trip Translation (DRTT) they introduce masked language models to construct bilingual adversarials based upon DRTT . |
| Outcome: | The proposed approach significantly improves the robustness of the NMT model on clean and noisy test sets. |
XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners (2024.naacl-long)
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| Challenge: | Existing methods for active learning rely on model uncertainty or disagreement to pick unlabeled data, leading to over-confidence in superficial patterns and lack of exploration. |
| Approach: | They propose to use a bi-directional encoder and a uni-directional decoder to generate and score an explanation for low-resource text classification. |
| Outcome: | The proposed model improves on 9 strong baselines on six datasets and can generate explanations for its predictions. |
Evaluating Cognitive-Behavioral Fixation via Multimodal User Viewing Patterns on Social Media (2025.emnlp-main)
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| Challenge: | Digital media platforms often contribute to cognitive-behavioral fixation, a phenomenon in which users exhibit sustained and repetitive engagement with narrow content domains. |
| Approach: | They propose a multimodal topic extraction module and a cognitive-behavioral fixation quantification module that collaboratively enable adaptive, hierarchical, and interpretable assessment of user behavior. |
| Outcome: | The proposed framework lays the groundwork for scalable computational analysis of cognitive fixation. |
Enhancing Byzantine-Resistant Aggregations with Client Embedding (2024.findings-emnlp)
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| Challenge: | Existing Byzantine-resistant aggregations detect poisonous clients but cannot defend against backdoor injection by malicious attackers in natural language tasks. |
| Approach: | They propose to embed client parameters to enhance Byzantine-resistant aggregations. |
| Outcome: | The proposed client embeddings detect poisonous clients and discard them . the proposed algorithms can't defend against backdoor injection by malicious attackers in natural language tasks . |
RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification (2023.findings-emnlp)
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| Challenge: | Recent studies have shown that current TMSC systems rely on textual information, and the progress in tackling this task has slowed down. |
| Approach: | They propose to integrate both visual and textual information to improve the performance of TMSC by considering multimodal information. |
| Outcome: | The proposed model integrates both visual and textual information to improve performance. |
GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models (2025.findings-acl)
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Mufan Qiu, Xinyu Hu, Fengwei Zhan, Sukwon Yun, Jie Peng, Ruichen Zhang, Bhavya Kailkhura, Jiekun Yang, Tianlong Chen
| Challenge: | Foundation models for single-cell RNA sequencing ignore biological prior knowledge encoded in gene regulatory relationships and fail to leverage multi-omics signals. |
| Approach: | They propose a framework that integrates multi-scale gene regulatory networks into RNA foundation model training. |
| Outcome: | The proposed framework improves on state-of-the-art models on three downstream tasks . it integrates multi-scale gene regulatory networks (GRNs) from multi-omics data into training . |
Multilingual Knowledge Editing with Language-Agnostic Factual Neurons (2025.coling-main)
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| Challenge: | Existing methods to update factual knowledge overlook connections of same knowledge between different languages, resulting in knowledge conflicts and limited edit performance. |
| Approach: | They propose a method to edit multilingual knowledge simultaneously that avoids knowledge conflicts and improves edit performance. |
| Outcome: | The proposed method avoids knowledge conflicts and improves edit performance on bi-ZsRE and MzsRE benchmarks. |
FR-Spec: Accelerating Large-Vocabulary Language Models via Frequency-Ranked Speculative Sampling (2025.acl-long)
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Weilin Zhao, Tengyu Pan, Xu Han, Yudi Zhang, Sun Ao, Yuxiang Huang, Kaihuo Zhang, Weilun Zhao, Yuxuan Li, Jie Zhou, Hao Zhou, Jianyong Wang, Maosong Sun, Zhiyuan Liu
| Challenge: | Speculative sampling is an efficient way to accelerate the auto-regressive generation process of large language models. |
| Approach: | They propose a frequency-ranked speculative sampling framework that optimizes draft candidate selection through vocabulary space compression. |
| Outcome: | Experiments show that FR-Spec reduces LM Head computation overhead by 75% while ensuring the equivalence of the final output distribution. |
A Comparative Study on Schema-Guided Dialogue State Tracking (2021.naacl-main)
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| Challenge: | Recent work proposes using natural language descriptions to define domain ontologies for dialog state tracking. |
| Approach: | They propose to use natural language descriptions to define domain ontologies instead of tag names for each intent or slot . they introduce a set of newly designed bench-marking descriptions and show model robustness . |
| Outcome: | The proposed model is robust on homogeneous and heterogeneously described descriptions in training and evaluation. |
The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters (2025.acl-long)
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Chulun Zhou, Qiujing Wang, Mo Yu, Xiaoqian Yue, Rui Lu, Jiangnan Li, Yifan Zhou, Shunchi Zhang, Jie Zhou, Wai Lam
| Challenge: | Theory-of-Mind (ToM) is a psychological capability that allows humans to understand and interpret the mental states of others. |
| Approach: | They propose a CharToM-QA benchmark to assess the importance of comprehensive contextual understanding about personal backgrounds in ToM. |
| Outcome: | The proposed model outperforms existing models on 1,035 ToM questions based on classic novels and shows that educated participants perform better when they have read the novels than non-educated participants. |
Evidence-Aligned Entity Verification for Hallucination Detection in Retrieval-Augmented Generation (2026.findings-acl)
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| Challenge: | Existing methods for hallucination detection depend on internal signals like uncertainty and self-consistency checks to identify unreliable outputs. |
| Approach: | They propose a retrieval-augmented generation method to enhance hallucination detection by addressing information updating challenges. |
| Outcome: | The proposed method improves on existing methods with strong generalization capabilities. |
MoEfication: Transformer Feed-forward Layers are Mixtures of Experts (2022.findings-acl)
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| Challenge: | Recent work has shown that feed-forward networks (FFNs) in pre-trained Transformers are a key component, storing various linguistic and factual knowledge. |
| Approach: | They propose to convert a model into its MoE version with the same parameters and build expert routers to decide which experts will be used for each input. |
| Outcome: | The proposed model can use 10% to 30% of FFN parameters while maintaining over 95% original performance. |
Selecting Stickers in Open-Domain Dialogue through Multitask Learning (2022.findings-acl)
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| Challenge: | Existing methods to select appropriate stickers in open-domain dialogues have not been explored. |
| Approach: | They propose a multitask learning method consisting of three auxiliary tasks to combine multimodal information to enhance the understanding of dialogue history, emotion and semantic meaning of stickers. |
| Outcome: | The proposed model can combine multimodal information and achieve significantly higher accuracy over strong baselines. |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
RU22Fact: Optimizing Evidence for Multilingual Explainable Fact-Checking on Russia-Ukraine Conflict (2024.lrec-main)
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| Challenge: | Existing methods to verify factuality of claims do not provide sufficient evidence for explainable fact-checking systems. |
| Approach: | They propose a method to automatically retrieve and summarize evidence from the Web and a novel multilingual explainable fact-checking dataset on the Russia-Ukraine conflict in 2022. |
| Outcome: | The proposed method can retrieve and summarize evidence from the Web and generate explanations in 16 languages. |
Huatuo-26M, a Large-scale Chinese Medical QA Dataset (2025.findings-naacl)
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Xidong Wang, Jianquan Li, Shunian Chen, Yuxuan Zhu, Xiangbo Wu, Zhiyi Zhang, Xiaolong Xu, Junying Chen, Jie Fu, Xiang Wan, Anningzhe Gao, Benyou Wang
| Challenge: | Large Language Models are a powerful tool for medical research, but the data is a bottleneck. |
| Approach: | They propose to use the largest ever medical Question Answering dataset with 26 Million QA pairs as a fine-tuning data for training large language models. |
| Outcome: | The proposed dataset demonstrates that it can be used to train large language models and improves zero-shot performance on other datasets. |
DrKGC: Dynamic Subgraph Retrieval-Augmented LLMs for Knowledge Graph Completion across General and Biomedical Domains (2025.findings-emnlp)
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| Challenge: | Knowledge graph completion (KGC) aims to predict missing triples in knowledge graphs . current approaches encode graph context in textual form, which fails to exploit its potential . |
| Approach: | a new method is proposed to predict missing triples in knowledge graphs by leveraging existing triples and textual information. |
| Outcome: | The proposed model learns structural embeddings and logical rules within the KG and extracts a subgraph for each query guided by the learned rules. |
Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances (2021.acl-long)
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| Challenge: | Recent intelligent open-domain chatbots have made substantial progress thanks to the rapid development of large-scale pre-training approaches. |
| Approach: | They propose a dynamic flow mechanism to model the context flow and a model to capture the information dynamics across dialogue utterances. |
| Outcome: | The proposed model outperforms the DialoGPT on the dialogue generation task. |
WeTS: A Benchmark for Translation Suggestion (2022.emnlp-main)
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| Challenge: | Existing studies focus on overall performance of machine translation but ignore TS performance, authors say . if TS is applied into post-editing, it will reduce the time and cost of post-production. |
| Approach: | They propose to use a golden corpus annotated by experts to generate a translation suggestion model. |
| Outcome: | The proposed model improves on the golden corpus annotated by translators on four translation directions. |
MAVIS: Multi-Agent Video Retrieval via Structured Video Understanding (2026.findings-acl)
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| Challenge: | Existing methods for video retrieval rely on embedding-based full-corpus scanning, but there is a bottleneck in semantic asymmetry and computational redundancy. |
| Approach: | They propose a multi-agent framework that rethinks retrieval as cooperative reasoning . they parse raw videos into a structured semantic library, enabling explicit attribute-level indexing . |
| Outcome: | The proposed framework bridges the granularity mismatch gap by parsing raw videos into a structured semantic library . it employs a Logic-aware Debate mechanism with a strict veto protocol . the proposed framework achieves competitive performance without task-specific fine-tuning . |
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision (2021.acl-long)
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Si Sun, Yingzhuo Qian, Zhenghao Liu, Chenyan Xiong, Kaitao Zhang, Jie Bao, Zhiyuan Liu, Paul Bennett
| Challenge: | Neural information retrieval models have shown advanced results in many ranking scenarios where massive relevance labels or clickthrough data are available. |
| Approach: | They propose a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains. |
| Outcome: | The proposed method improves the few-shot ranking accuracy of Neu-IR models on three TREC benchmarks in the web, news, and biomedical domains. |
Plug-and-Play Knowledge Injection for Pre-trained Language Models (2023.acl-long)
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Zhengyan Zhang, Zhiyuan Zeng, Yankai Lin, Huadong Wang, Deming Ye, Chaojun Xiao, Xu Han, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
| Challenge: | Existing knowledge injection methods are not suitable for enhancing pre-trained language models with external knowledge bases. |
| Approach: | They propose a plug-and-play knowledge injection method where knowledge bases are injected into frozen existing downstream models by a knowledge plugin. |
| Outcome: | The proposed method improves the performance of knowledge injection on knowledge-driven tasks while keeping model parameters frozen. |
Advancing SMoE for Continuous Domain Adaptation of MLLMs: Adaptive Router and Domain-Specific Loss (2025.acl-long)
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| Challenge: | Recent studies have explored Continual Instruction Tuning (CIT) in Multimodal Large Language Models (MLLMs), with a primary focus on Task-incremental CIT, where MLLM are required to continuously acquire new tasks. |
| Approach: | They propose a Sparse Mixture of Expert (SMoE) based method for domain-incremental CIT in Multimodal Large Language Models (MLLMs) . they equip the SMoA module with a domain-specific autoregressive loss (DSAL) they establish a new benchmark to evaluate the efficacy of their method . |
| Outcome: | The proposed method outperforms all baselines and is based on a Sparse Mixture of Experts (SMoE) module . |
AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents (2025.acl-long)
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Yifan Xu, Xiao Liu, Xueqiao Sun, Siyi Cheng, Hao Yu, Hanyu Lai, Shudan Zhang, Dan Zhang, Jie Tang, Yuxiao Dong
| Challenge: | Existing studies on Android agents lack systematic research on open-source and closed-source models. |
| Approach: | They propose a framework for Android agents that includes an operation environment and a reproducible benchmark. |
| Outcome: | The proposed framework lifts the success rate of open-source LLMs and LMMs from 4.59% to 21.50% for LLM and 1.93% to 13.28% for LMM. |
Knowledge-to-Verification: Exploring RLVR for LLMs in Knowledge-Intensive Domains (2026.acl-long)
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Zhonghang Yuan, Zhefan Wang, Fang Hu, Zihong Chen, Jinzhe Li, Gang Li, Jie Ying, Huanjun Kong, Songyang Zhang, Nanqing Dong
| Challenge: | Recent large language models (LLMs) have demonstrated remarkable progress in reasoning, but their applications on knowledge-intensive domains have not been explored due to the scarcity of high-quality verifiable data. |
| Approach: | They propose a framework that extends reinforcement learning with verifiable rewards (RLVR) to knowledge-intensive domains through automated verififiability data synthesis while enabling verification of the LLM's reasoning process. |
| Outcome: | Extensive experiments show that the proposed framework enhances the reasoning of large language models in knowledge-intensive domains without significantly compromising the model’s general capabilities. |
Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues (2022.emnlp-main)
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| Challenge: | Existing methods to augment dialogue datasets are labor-intensive and time-consuming . Currently, smoking is harmful for your health. |
| Approach: | They propose a data augmentation method to augment dialogue responses with different semantics by counterfactual inference. |
| Outcome: | The proposed method outperforms baselines on multiple downstream tasks. |
On Orthogonality Constraints for Transformers (2021.acl-short)
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Aston Zhang, Alvin Chan, Yi Tay, Jie Fu, Shuohang Wang, Shuai Zhang, Huajie Shao, Shuochao Yao, Roy Ka-Wei Lee
| Challenge: | a dedicated study on orthogonality constraints for transformers has been lacking . plug-and-play constraints increase the BLEU of transformers . |
| Approach: | They propose to use plug-and-play constraints to encourage matrices to be orthogonal for numerical stability. |
| Outcome: | The proposed constraint increases the BLEU on the large-scale WMT’16 EnDe benchmark by a factor of 28.4 to 29.6. |
CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation (2024.lrec-main)
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| Challenge: | Metaphors are a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication. |
| Approach: | They propose a large-scale high quality annotated Chinese Metaphor Corpus . they use a set of guidelines to ensure the accuracy and consistency of their annotations . |
| Outcome: | The proposed corpus generates metaphors that resonate more with real-world intuition. |
The Stochastic Parrot on LLM’s Shoulder: A Summative Assessment of Physical Concept Understanding (2025.naacl-long)
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| Challenge: | Recent years have witnessed remarkable advancements in large language models (LLMs) many researchers argue that LLMs may not * Equal contribution. |
| Approach: | They propose a task that summarises the memorization issue by using grid inputs that abstractly describe physical phenomena. |
| Outcome: | The proposed task alleviates the memorization issue by using grid-format inputs that abstractly describe physical phenomena. |
Robust Self-Augmentation for Named Entity Recognition with Meta Reweighting (2022.naacl-main)
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| Challenge: | Prior research has focused on reducing noise for specific methods to achieve an effective integration. |
| Approach: | They propose to use token substitution and mixup to improve named entity recognition (NER) using a meta-reweighting strategy, which is extensible and requires little effort. |
| Outcome: | The proposed method is extensible, imposing little effort on a specific self-augmentation method. |
Dynamic Scaling of Unit Tests for Code Reward Modeling (2025.acl-long)
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| Challenge: | Existing large language models struggle to produce accurate responses on the first attempt for complex reasoning tasks like code generation. |
| Approach: | They propose a lightweight yet effective unit test generator that scales unit tests based on problem difficulty. |
| Outcome: | The proposed approach significantly improves performance on three benchmarks. |
OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems (2024.acl-long)
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Chaoqun He, Renjie Luo, Yuzhuo Bai, Shengding Hu, Zhen Thai, Junhao Shen, Jinyi Hu, Xu Han, Yujie Huang, Yuxiang Zhang, Jie Liu, Lei Qi, Zhiyuan Liu, Maosong Sun
| Challenge: | Large Language Models (LLMs) and Large Multimodal Models have exceeded general human capabilities in various tasks. |
| Approach: | They present an Olympiad-level bilingual multimodal scientific benchmark featuring 8,476 problems from Olympiad level mathematics and physics competitions. |
| Outcome: | The best performing model, GPT-4V, attains an average score of 17.97% on OlympiadBench, with a mere 10.74% in physics, highlighting the benchmark rigor and the intricacy of physical reasoning. |
Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks (2021.emnlp-main)
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| Challenge: | Pre-trained models can be fine-tuned on domain-specific unlabeled data . however, most further pre-training works just keep running the conventional pre- training task . |
| Approach: | They propose to add a further pre-training phase to the model to improve downstream tasks . they propose to use a domain-adaptive pre-tuning phase to fine-tune the models on unlabeled data . |
| Outcome: | The proposed method improves multiple task-oriented dialogue downstream tasks. |
Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning (2026.acl-long)
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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Kaiyu Huang, Yufeng Chen, Xu Jinan, Jie Zhou
| Challenge: | Current Large Reasoning Models exhibit two critical limitations when processing non-English languages: (1) They struggle to maintain input-output language consistency; (2) They generally perform poorly with wrong reasoning paths and lower answer accuracy compared to English. |
| Approach: | They propose a language-consistency reward and a cross-lingual thinking alignment reward to improve the model's interpretability and accuracy. |
| Outcome: | The proposed model achieves nearly 100% language consistency and superior performance on two multilingual benchmarks (MMATH and PolyMath). |
Beyond Text: Unveiling Privacy Vulnerabilities in Multi-modal Retrieval-Augmented Generation (2025.emnlp-main)
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| Challenge: | Large Multimodal Models (LMMs) can process text, images, and audio, but they introduce privacy vulnerabilities. |
| Approach: | They propose a compositional structured prompt attack to exploit MRAG privacy vulnerabilities . they show that LMMs can generate outputs resembling retrieved content . |
| Outcome: | The proposed approach generates outputs resembling retrieved content and exposes sensitive information. |
Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency (2021.findings-acl)
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| Challenge: | Existing methods to evaluate consistency capacity of open-domain chatbots are costly and low-efficient. |
| Approach: | They propose an efficient framework for evaluating consistency of open-domain chatbots . they use human judges to interact with chatbot, which is costly and low-efficient . |
| Outcome: | The proposed framework can assess the consistency capacity of chatbots and achieve a high ranking correlation with the human evaluation. |
Scheduled Dialog Policy Learning: An Automatic Curriculum Learning Framework for Task-oriented Dialog System (2021.findings-acl)
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| Challenge: | et al., 2013) show that dialog policy learning is an important component of the task-oriented dialogue system. |
| Approach: | They propose a framework that integrates curriculum learning and policy optimization . they propose to train dialog agents from easy dialogues to complex ones . |
| Outcome: | The proposed framework outperforms the state-of-the-art model on multi-task dialogues. |
GLGE: A New General Language Generation Evaluation Benchmark (2021.findings-acl)
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Dayiheng Liu, Yu Yan, Yeyun Gong, Weizhen Qi, Hang Zhang, Jian Jiao, Weizhu Chen, Jie Fu, Linjun Shou, Ming Gong, Pengcheng Wang, Jiusheng Chen, Daxin Jiang, Jiancheng Lv, Ruofei Zhang, Winnie Wu, Ming Zhou, Nan Duan
| Challenge: | Multi-task benchmarks focus on a range of Natural Language Understanding (NLU) tasks without considering the Natural Language Generation (NLG) models. |
| Approach: | They propose a multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks. |
| Outcome: | The proposed benchmarks are based on GLUE and Su-perGLUE for English and several other languages. |
Cascaded Semantic and Positional Self-Attention Network for Document Classification (2020.findings-emnlp)
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| Challenge: | Existing approaches to document classification combine semantic information with positional information (word orders) . document classification is one of the fundamental problems in natural language processing . |
| Approach: | They propose a new architecture to combine semantic and positional information using a semantic self-attention layer cascaded with Bi-LSTM. |
| Outcome: | The proposed model can exploit the interaction between semantics and word positions in a more interpretable and adaptive manner while preserving a compact model size and high convergence rate. |
Adversarial Preference Learning for Robust LLM Alignment (2025.findings-acl)
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Yuanfu Wang, Pengyu Wang, Chenyang Xi, Bo Tang, Junyi Zhu, Wenqiang Wei, Chen Chen, Chao Yang, Jingfeng Zhang, Chaochao Lu, Yijun Niu, Keming Mao, Zhiyu Li, Feiyu Xiong, Jie Hu, Mingchuan Yang
| Challenge: | Modern language models rely on Reinforcement Learning from Human Feedback (RLHF) to encourage safe behaviors, but they remain vulnerable to adversarial attacks due to three key limitations: (1) the inefficiency and high cost of human annotation; (2) the vast diversity of potential adversarials; and (3) the risk of feedback bias and reward hacking. |
| Approach: | They propose an iterative adversarial training method that incorporates three key innovations to address these challenges. |
| Outcome: | Experiments on Mistral-7B-Instruct-v0.3 show that the proposed method significantly enhances robustness and reduces harmful outputs from 5.88% to 0.43%. |
Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction (2020.acl-main)
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| Challenge: | Existing studies focus on aspect-opinion relation detection, but neglect to recognize the relations between aspects and opinion expressions. |
| Approach: | They propose a Synchronous Double-channel Recurrent Network to deal with AOPE task . they propose an opinion entity extraction unit, a relation detection unit, and a synchronization unit . |
| Outcome: | The proposed system achieves state-of-the-art in opinion entity extraction . it is based on three datasets based upon SemEval 2014 and 2015 benchmarks . |
Compositional Mathematical Encoding for Math Word Problems (2023.findings-acl)
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| Challenge: | Existing MWP encoders work in a unimodal setting and map problem description to latent representation, then for decoding. |
| Approach: | They propose a Compositional Math Word Problem Solver which maps problem description to latent representation and decodes it in an interactive way. |
| Outcome: | Extensive experiments show that the proposed model outperforms state-of-the-art models on public benchmarks. |
MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)
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Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang
| Challenge: | Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences. |
| Approach: | They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner. |
| Outcome: | The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner. |
RolePlot: A Systematic Framework for Evaluating and Enhancing the Plot-Progression Capabilities of Role-Playing Agents (2025.acl-long)
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| Challenge: | Existing research has focused on role-playing agents’ ability to portray specified characters, but their ability to advance the plot requires substantial improvements to deliver more engaging interaction. |
| Approach: | They propose a role-playing framework to evaluate and enhance the plot-progression capabilities of role-players. |
| Outcome: | The proposed framework improves RPAs’ ability to time plot developments and yields a significant increase in conversation turns and sustained higher arousal levels. |
CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding (D19-1)
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| Challenge: | Existing models for slot filling and intent detection fail to fully utilize cooccurrence relations between slots and intents, which restricts their potential performance. |
| Approach: | They propose a novel Collaborative Memory Network (CM-Net) that captures slot-specific and intent-specific features in a collaborative manner. |
| Outcome: | The proposed network outperforms existing models on two benchmarks and a self-collected corpus. |
Design Challenges and Misconceptions in Neural Sequence Labeling (C18-1)
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| Challenge: | Existing neural sequence labeling models have been used for many tasks such as POS tagging, chunking and named entity recognition (NER). |
| Approach: | They propose to replicate twelve neural sequence labeling models and compare them to three benchmarks to find out which models are effective and which are inconsistent. |
| Outcome: | The proposed models are compared on NER, Chunking, and POS tagging benchmarks. |
DataSciBench: An LLM Agent Benchmark for Data Science (2026.findings-acl)
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Dan Zhang, Sining Zhoubian, Min Cai, Fengzu Li, Lekang Yang, Wei Wang, Tianjiao Dong, Ziniu Hu, Jie Tang, Yisong Yue
| Challenge: | Existing benchmarks focus on single task, simple evaluation metrics, and readily available ground truth (GT) DataSciBench is built on curated, natural, and challenging prompts with complex evaluation criteria and uncertain GT. |
| Approach: | They propose a benchmark for evaluating Large Language Models in data science that integrates LLM-based self-consistency and human verification to ensure accuracy. |
| Outcome: | The proposed framework outperforms open-source models in all metrics and offers rigorous insights into LLM strengths and weaknesses. |
Transferable and Efficient Non-Factual Content Detection via Probe Training with Offline Consistency Checking (2024.acl-long)
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| Challenge: | Existing factuality detection methods are not effective for large language models (LLMs). |
| Approach: | They propose a probing model that trains on offline consistency checking results. |
| Outcome: | The proposed model reduces the computational burden of generating multiple responses by online consistency verification and improves on factuality detection and question answering benchmarks. |
Abstract then Play: A Skill-centric Reinforcement Learning Framework for Text-based Games (2023.findings-acl)
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| Challenge: | Existing reinforcement learning frameworks fail to decompose the task and abstract the action autonomously. |
| Approach: | They propose a skill-centric reinforcement learning framework capable of abstracting the action in an end-to-end manner. |
| Outcome: | Empirical experiments on the Jericho environment validate the proposed framework against state-of-the-art baselines. |
TASO: Task-Aligned Sparse Optimization for Parameter-Efficient Model Adaptation (2025.emnlp-main)
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Daiye Miao, Yufang Liu, Jie Wang, Changzhi Sun, Yunke Zhang, Demei Yan, Shaokang Dong, Qi Zhang, Yuanbin Wu
| Challenge: | Existing studies have shown that LoRA introduces substantial parameter redundancy, which not only increases the number of trainable parameters but also hinders the effectiveness of fine-tuning. |
| Approach: | They propose a method that leverages importance information from the pretrained model’s weights to mitigate LoRA redundancy. |
| Outcome: | The proposed method significantly reduces the number of trainable parameters required for task adaptation while providing a task-aligned perspective for LoRA redundancy reduction. |
Sequential-NIAH: A Needle-In-A-Haystack Benchmark for Extracting Sequential Needles from Long Contexts (2025.emnlp-main)
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Yifei Yu, Qian-Wen Zhang, Lingfeng Qiao, Di Yin, Fang Li, Jie Wang, Chen Zeng Xi, Suncong Zheng, Xiaolong Liang, Xing Sun
| Challenge: | Recent models have extended Corresponding Author. context lengths to millions of tokens while maintaining reasoning and comprehension capabilities. |
| Approach: | They propose a benchmark to evaluate the ability of large language models to extract sequential information items from long contexts. |
| Outcome: | The proposed model achieves maximum accuracy of 63.50% on six well-known LLMs. |
DecorateLM: Data Engineering through Corpus Rating, Tagging, and Editing with Language Models (2024.emnlp-main)
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Ranchi Zhao, Zhen Thai, Yifan Zhang, Shengding Hu, Jie Zhou, Yunqi Ba, Jie Cai, Zhiyuan Liu, Maosong Sun
| Challenge: | Large Language Models (LLMs) are pre-trained on vast datasets composed of billions of tokens harvested from diverse text sources. |
| Approach: | They propose a data engineering method to refine the pretraining corpus through data rating, tagging and editing. |
| Outcome: | The proposed method improves the quality of the pretraining corpus by enhancing 100 billion tokens of the training corpus. |
Read Anywhere Pointed: Layout-aware GUI Screen Reading with Tree-of-Lens Grounding (2024.emnlp-main)
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| Challenge: | Existing models for GUI understanding ignore a key GUI-referring task: screen reading based on user-indicated points. |
| Approach: | They propose a Tree-of-Lens agent that constructs a Hierarchical Layout Tree based on user input points and a GUI screenshot. |
| Outcome: | The proposed agent can interpret the Screen Point-and-Read task on mobile, web, and operating systems. |
Jailbreaking Prompt Attack: A Controllable Adversarial Attack against Diffusion Models (2025.findings-naacl)
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| Challenge: | Text-to-image (T2I) models can be used to generate harmful content such as sexually explicit, unfaithful, and misleading or Not-Safe-for-Work (NSFW) images. |
| Approach: | They propose a more practical and universal attack that does not require the presence of a target model. |
| Outcome: | The proposed attack bypasses both text and image safety checkers while preserving high semantic alignment with the target prompt. |
SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning (2024.findings-acl)
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| Challenge: | Recent advances in parameter-efficient fine-tuning (PEFT) techniques allow for adjustments to only a minor fraction of the parameters of large language models. |
| Approach: | They propose a SImple BOoster to enhance parameter-efficient fine-tuning techniques by injecting an initial residual into the model. |
| Outcome: | The proposed model improves performance on 22 benchmark datasets and can be extended to a range of state-of-the-art techniques. |
A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking (2020.acl-main)
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| Challenge: | Existing methods for dialogue state tracking ignore the slot imbalance problem and treat all slots indiscriminately, which limits the learning of hard slots. |
| Approach: | They propose to employ a contextual hierarchical attention network to enhance the DST by learning contextual representations. |
| Outcome: | The proposed approach achieves 52.68% and 58.55% joint accuracy on multiWOZ 2.0 and MultiWOZ 2.1 datasets and significantly improves performance (+1.24% and +5.98%) |
On the Vulnerability of Text Sanitization (2025.naacl-long)
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| Challenge: | Existing reconstruction attacks on text sanitization are not able to accurately assess their effectiveness. |
| Approach: | They propose to use ASR to measure the effectiveness of reconstruction attacks to evaluate sanitization performance. |
| Outcome: | The proposed reconstruction attacks achieve a 46.4% improvement in ASR over the state-of-the-art baseline with a privacy budget of =4.0 on the SST-2 dataset. |
Teaching Your Models to Understand Code via Focal Preference Alignment (2025.emnlp-main)
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Jie Wu, Haoling Li, Xin Zhang, Xiao Liu, Yangyu Huang, Jianwen Luo, Yizhen Zhang, Zuchao Li, Ruihang Chu, Yujiu Yang, Scarlett Li
| Challenge: | Existing methods for supervised fine-tuning focus on unit test feedback to construct preference pairs. |
| Approach: | They propose a preference alignment framework that mimics human iterative debugging to refine Code LLMs. |
| Outcome: | Experiments show that Preference Learning improves on BigCodeBench and BigCodeBind tasks. |
Categorizing Semantic Representations for Neural Machine Translation (2022.coling-1)
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| Challenge: | Modern neural machine translation models suffer limitation in compositional generalization, resulting in weakened translation performance on unseen compounds. |
| Approach: | They propose to introduce categorization to the contextualized representations to improve generalization by reducing sparsity and overfitting. |
| Outcome: | The proposed method reduces compositional generalization error rates by 24% on a dedicated MT dataset. |
Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition (2021.naacl-main)
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| Challenge: | Existing models fail to fully utilize contextual information which plays an important role in interpreting sentences. |
| Approach: | They propose a graph-based Context Tracking Network to model the discourse context for IDRR. |
| Outcome: | The proposed model can integrate sentence-level and token-level contextual semantics better than existing models. |
COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)
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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin
| Challenge: | Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. |
| Approach: | They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets. |
| Outcome: | The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark. |
Lightweight and Efficient Neural Natural Language Processing with Quaternion Networks (P19-1)
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| Challenge: | Existing models for natural language processing are heavily parameterized and memory inefficient. |
| Approach: | They propose a series of lightweight and memory efficient neural architectures for NLP tasks . they propose quaternion algebra and hypercomplex spaces for computation . |
| Outcome: | The proposed models enable expressive inter-component interactions and significantly reduce parameter size without loss of performance. |
Less, but Better: Efficient Multilingual Expansion for LLMs via Layer-wise Mixture-of-Experts (2025.acl-long)
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| Challenge: | Existing large language models (LLMs) have remarkable ability in high-resource languages, but their performance in multilingual scenarios is still limited. |
| Approach: | They propose a layer-wise expert allocation algorithm to determine the appropriate number of new experts for each layer. |
| Outcome: | The proposed method outperforms the previous state-of-the-art baseline with 60% fewer experts in the single-expansion setting and 33.3% fewer in the lifelong-expanding setting. |
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder (2021.findings-acl)
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| Challenge: | Recent work has shown that models can be easily fooled by intentionally designed adversarial examples. |
| Approach: | They propose two efficient approaches for generating adversarial perturbations on embeddings and propose two new approaches to help model learn adversarials more efficiently. |
| Outcome: | The proposed approaches outperform strong baselines on various text classification datasets and the model's performance drops less under adversarial attack. |
LongSafety: Evaluating Long-Context Safety of Large Language Models (2025.acl-long)
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Yida Lu, Jiale Cheng, Zhexin Zhang, Shiyao Cui, Cunxiang Wang, Xiaotao Gu, Yuxiao Dong, Jie Tang, Hongning Wang, Minlie Huang
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities in understanding and generating long sequences. |
| Approach: | They propose a benchmark to evaluate LLM safety in open-ended long-context tasks . they find that relevant context and extended input sequences can exacerbate safety risks . |
| Outcome: | The proposed benchmark identifies significant safety vulnerabilities in 16 LLMs . strong safety performance in short-context scenarios does not correlate with safety in long-contact tasks . |
Evolving Agentic Workflow Driven by Human-Agent Collaboration (2026.findings-acl)
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Yuxin Liu, Jinxuan Zhang, Yuezhang Peng, Hefeng Zhou, Xiangfeng Wang, Jiong Lou, Chentao Wu, Jie LI, Jingjing Qu, Chaochao Lu
| Challenge: | Existing approaches to generate agentic workflows using large language models are limited by high manual design costs, inefficient agentic search, and poor dynamic adaptability to new tasks and human preferences. |
| Approach: | They propose an evolutionary framework for generating agentic workflows through human-agent collaboration using evolutionary algorithms that mutate and cross over their structures, prompts, and LLM backbones. |
| Outcome: | The proposed framework surpasses other automated baselines by 27.34% while achieving comparable performance to o1-preview at only one-fourth of the cost. |
Medical Dialogue System: A Survey of Categories, Methods, Evaluation and Challenges (2024.findings-acl)
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Xiaoming Shi, Zeming Liu, Li Du, Yuxuan Wang, Hongru Wang, Yuhang Guo, Tong Ruan, Jie Xu, Xiaofan Zhang, Shaoting Zhang
| Challenge: | Existing medical dialogue systems have significant potential to simplify diagnostic procedure and reduce the cost of collecting information from patients. |
| Approach: | They analyze 325 papers from well-known computer science, natural language processing conferences and journals to find out the major challenges of medical dialog systems. |
| Outcome: | The proposed systems have been surveyed in the medical community but have not been evaluated from a technical perspective. |
UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs (2024.acl-demos)
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Chaoqun He, Renjie Luo, Shengding Hu, Ranchi Zhao, Jie Zhou, Hanghao Wu, Jiajie Zhang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing evaluation platforms are complex and poorly modularized, hindering seamless incorporation into researcher’s workflows. |
| Approach: | They propose a lightweight evaluation framework characterized by lightweight, comprehensiveness, modularity, and efficiency that integrates models, data, and metrics into a unified evaluation workflow. |
| Outcome: | The proposed evaluation framework is lightweight, comprehensive, modular, and efficient. |
NaturalCodeBench: Examining Coding Performance Mismatch on HumanEval and Natural User Queries (2024.findings-acl)
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| Challenge: | Large language models (LLMs) generate code for productive activities, but current benchmarks for code synthesis are oriented towards introductory tasks on algorithm and data science. |
| Approach: | They propose a code benchmark to mirror the complexity and variety of scenarios in real-world coding tasks. |
| Outcome: | The proposed benchmark improves on 39 large language models with close HumanEval scores and achieves an efficiency increase of more than 4 times. |
Related Knowledge Perturbation Matters: Rethinking Multiple Pieces of Knowledge Editing in Same-Subject (2025.naacl-short)
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Zenghao Duan, Wenbin Duan, Zhiyi Yin, Yinghan Shen, Shaoling Jing, Jie Zhang, Huawei Shen, Xueqi Cheng
| Challenge: | Existing knowledge editing methods struggle when tasked with editing multiple related knowledge pieces for the same subject. |
| Approach: | They propose a benchmark to assess the effectiveness of knowledge editing methods . they use same-subject edits to ensure comprehensive updates to entity-centric knowledge . |
| Outcome: | The proposed method over-relys on subject information, neglecting other critical factors, resulting in reduced editing effectiveness. |
Graph-to-Tree Learning for Solving Math Word Problems (2020.acl-main)
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| Challenge: | Existing tree-based neural models do not capture the relationships and order information among the quantities well. |
| Approach: | They propose a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. |
| Outcome: | The proposed framework outperforms the state-of-the-art on two available datasets significantly. |
Variator: Accelerating Pre-trained Models with Plug-and-Play Compression Modules (2023.findings-emnlp)
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Chaojun Xiao, Yuqi Luo, Wenbin Zhang, Pengle Zhang, Xu Han, Yankai Lin, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Large language models (LLMs) have been successful on NLP tasks but require huge parameter sizes and computational resources. |
| Approach: | They propose a parameter-efficient acceleration method that enhances computational efficiency through plug-and-play compression plugins. |
| Outcome: | The proposed method saves 53% computational costs using only 0.9% additional parameters with a performance drop of less than 2%. |
HyperNetwork-based Decoupling to Improve Model Generalization for Few-Shot Relation Extraction (2023.emnlp-main)
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| Challenge: | Existing studies cannot generalize well to unseen relations using Prototypical Networks . current approaches are dependent on large amount of labeled data and cannot deal with unseense relations well. |
| Approach: | They propose a HyperNetwork-based Decoupling approach to improve FSRE generalization . they propose FSre models with an encoder, network generator and refined classifiers . |
| Outcome: | The proposed method improves the generalization of few-shot relation extraction models. |
A Survey of Reinforcement Learning for Large Language Models under Data Scarcity: Challenges and Solutions (2026.acl-long)
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Zhiyin Yu, Yuchen Mou, Juncheng Yan, Junyu Luo, Chunchun Chen, Xing Wei, Yunhui Liu, Hongru Sun, Yuxing Zhang, Jun Xu, Yatao Bian, Ming Zhang, Wei Ye, Tieke He, Jie Yang, Guanjie Zheng, Zhonghai Wu, Bo Zhang, Lei Bai, Xiao Luo
| Challenge: | Existing research on reinforcement learning for LLMs under data scarcity has not been unified. |
| Approach: | They propose a top-up hierarchical framework built around three complementary perspectives: data-centric, training-centric and framework-centric. |
| Outcome: | The proposed framework provides a clear conceptual foundation for understanding the design space of data-efficient RL for large language models and to guide researchers working in this emerging area. |
Uncertainty Quantification for In-Context Learning of Large Language Models (2024.naacl-long)
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Chen Ling, Xujiang Zhao, Xuchao Zhang, Wei Cheng, Yanchi Liu, Yiyou Sun, Mika Oishi, Takao Osaki, Katsushi Matsuda, Jie Ji, Guangji Bai, Liang Zhao, Haifeng Chen
| Challenge: | Existing studies on in-context learning have focused on quantifying the uncertainty associated with the model's response, but they neglect the complexity of the LLM and the uniqueness of in-constitut learning. |
| Approach: | They propose a method to quantify the uncertainty associated with in-context learning and propose corresponding estimation method to quantify both types of uncertainties. |
| Outcome: | The proposed method offers an unsupervised way to understand the prediction of in-context learning in a plug-and-play fashion. |
XDailyDialog: A Multilingual Parallel Dialogue Corpus (2023.acl-long)
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Zeming Liu, Ping Nie, Jie Cai, Haifeng Wang, Zheng-Yu Niu, Peng Zhang, Mrinmaya Sachan, Kaiping Peng
| Challenge: | Existing datasets for open-domain dialogue modeling limited to a single language . absence of multilingual datasets hinders development of robust open- domain dialog systems . |
| Approach: | They propose a multilingual parallel open-domain dialog dataset to explore multilingual and cross-lingual open- domain dialog. |
| Outcome: | The proposed model can be used to explore multilingual and cross-lingual open-domain dialogs in other languages. |
Lightweight LLM Agent Memory with Small Language Models (2026.acl-long)
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Jiaquan Zhang, Chaoning Zhang, Shuxu Chen, Zhenzhen Huang, Pengcheng Zheng, Zhicheng Wang, Ping Guo, Fan Mo, Sung-Ho Bae, Jie Zou, Jiwei Wei, Yang Yang
| Challenge: | Existing external memory systems for LLMs have low online overhead but are unstable in accumulating latency over long interactions. |
| Approach: | They propose a lightweight memory system for better agent memory driven by Small Language Models . lightmem modularizes memory retrieval, writing, and long-term consolidation . they show consistent gains across model scales and high efficiency . |
| Outcome: | The proposed system improves agent memory but has low latency and low online overhead . it separates online processing from offline consolidation to enable efficient memory invocation . the proposed system achieves an average F1 improvement of 2.5 over A-MEM on LoCoMo . |
SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval (2024.findings-acl)
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Siwei Wu, Yizhi Li, Kang Zhu, Ge Zhang, Yiming Liang, Kaijing Ma, Chenghao Xiao, Haoran Zhang, Bohao Yang, Wenhu Chen, Wenhao Huang, Noura Al Moubayed, Jie Fu, Chenghua Lin
| Challenge: | Multi-modal information retrieval (MMIR) is a rapidly evolving field . current benchmarks for image-text pairings overlook the scientific domain . |
| Approach: | They develop a scientific domain-specific MMIR benchmark to evaluate image-text pairings using open-access research paper corpora. |
| Outcome: | The proposed benchmarks are based on 530K image-text pairs extracted from scientific documents with detailed captions. |
Exploring Dynamic Selection of Branch Expansion Orders for Code Generation (2021.acl-long)
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| Challenge: | Existing code generation models model abstract syntax tree (AST) but not suitable for all multi-branch nodes. |
| Approach: | They propose to equip a Seq2Tree model with a branch selector to determine optimal expansion orders for multi-branch nodes. |
| Outcome: | The proposed model can determine optimal expansion orders of branches for multi-branch nodes. |
OTSeq2Set: An Optimal Transport Enhanced Sequence-to-Set Model for Extreme Multi-label Text Classification (2022.emnlp-main)
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| Challenge: | Extreme multi-label text classification (XMTC) is a task of finding the most relevant subset labels from an extremely large label set. |
| Approach: | They propose an autoregressive sequence-to-set model for XMTC tasks named OTSeq2Set. |
| Outcome: | The proposed model outperforms other competitive baselines on 4 benchmark datasets. |
TasTe: Teaching Large Language Models to Translate through Self-Reflection (2024.acl-long)
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| Challenge: | Existing approaches to enhance LLMs' performance in machine translation are unable to fully exploit their instruction-following capabilities. |
| Approach: | They propose a framework for translating through self-reflection that involves two stages of inference . they propose to use the framework to refine LLMs' preliminary translations . |
| Outcome: | The proposed framework can produce translation outputs that match the quality of NMT systems. |
Joint Optimization of Training Data and Policy in RLHF (2026.findings-acl)
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Zhuohao Yu, Jiali Zeng, Weizheng Gu, Mengyuan Sun, Yidong Wang, Fandong Meng, Jie Zhou, Shikun Zhang, Wei Ye
| Challenge: | JODP optimizes policies on fixed training inputs, limiting the diversity of learning signals. |
| Approach: | They propose a framework where policy generates improved variants of training problems to enhance its own learning. |
| Outcome: | The proposed framework improves on safety alignment tasks by allowing 4B models to reach 8B model performance with less than 1% additional computational overhead. |
GTM: A Generative Triple-wise Model for Conversational Question Generation (2021.acl-long)
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| Challenge: | Experimental results show that opendomain conversational question generation improves the quality of questions in terms of fluency, coherence and diversity over competitive baselines. |
| Approach: | They propose a triple-wise model with hierarchical variations for open-domain conversational question generation using a post-question-answer triple and one-to-many semantic mappings. |
| Outcome: | The proposed model significantly improves the quality of questions in terms of fluency, coherence and diversity over baselines. |
Mitigating the Privacy Issues in Retrieval-Augmented Generation (RAG) via Pure Synthetic Data (2025.emnlp-main)
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Shenglai Zeng, Jiankun Zhang, Pengfei He, Jie Ren, Tianqi Zheng, Hanqing Lu, Han Xu, Hui Liu, Yue Xing, Jiliang Tang
| Challenge: | Existing literature suggests that RAG systems may face privacy issues when the retrieval process involves private data. |
| Approach: | They propose a two-stage synthetic data generation paradigm that uses attributes to preserve contextual information from the original data. |
| Outcome: | The proposed approach preserves key contextual information from the original data while reducing privacy risks. |
Coherent Entity Disambiguation via Modeling Topic and Categorical Dependency (2023.findings-emnlp)
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| Challenge: | Existing entity disambiguation methods struggle to capture explicit discourse-level dependencies, resulting in incoherent predictions at the abstract level. |
| Approach: | They propose an unsupervised variational autoencoder to extract latent topic vectors of context sentences to enhance coherence of entity predictions. |
| Outcome: | The proposed system achieves state-of-the-art on popular ED benchmarks with an average improvement of 1.3 F1 points. |
Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation (2021.acl-short)
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| Challenge: | Existing approaches to token-level adaptive training only use static word frequency information without considering the source language. |
| Approach: | They propose a bilingual mutual information based adaptive objective that assigns weights to target tokens with higher BMI . they propose to use this approach to improve token-level adaptive training . |
| Outcome: | The proposed method improves token-level adaptive training on two languages. |
Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model Evaluation (2024.acl-long)
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| Challenge: | Recent advances in large language models have improved performance across tasks . however, the sensitivity of LLMs to prompt leads to unreliability of evaluation results . |
| Approach: | They propose a new concept to evaluate language models with a fixed question or limited paraphrases as the query. |
| Outcome: | The proposed method outperforms existing benchmarks on multiple language models . it avoids prompt sensitivity, rendering models more reliable and robust . |
Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation (D19-1)
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| Challenge: | Context modeling is essential to generate coherent and consistent translation for document-level Neural Machine Translations. |
| Approach: | They propose a query-guided capsule network to cluster context information into different perspectives from which the target translation may concern. |
| Outcome: | The proposed model outperforms baseline models on multiple datasets of different domains. |
Simulating Classroom Education with LLM-Empowered Agents (2025.naacl-long)
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Zheyuan Zhang, Daniel Zhang-Li, Jifan Yu, Linlu Gong, Jinchang Zhou, Zhanxin Hao, Jianxiao Jiang, Jie Cao, Huiqin Liu, Zhiyuan Liu, Lei Hou, Juanzi Li
| Challenge: | Initial studies have focused on task-specific, independent LLM-empowered agents, but the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored. |
| Approach: | They propose a multi-agent classroom simulation teaching framework that recognizes representative class roles and introduces a novel class control mechanism for automatic classroom teaching. |
| Outcome: | The proposed framework can simulate dynamic learning environment for users with active teacher-student and student-studente interactions. |
RoleLLM: Benchmarking, Eliciting, and Enhancing Role-Playing Abilities of Large Language Models (2024.findings-acl)
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Noah Wang, Z.y. Peng, Haoran Que, Jiaheng Liu, Wangchunshu Zhou, Yuhan Wu, Hongcheng Guo, Ruitong Gan, Zehao Ni, Jian Yang, Man Zhang, Zhaoxiang Zhang, Wanli Ouyang, Ke Xu, Wenhao Huang, Jie Fu, Junran Peng
| Challenge: | Large Language Models (LLMs) have paved the way for complex tasks such as role-playing. |
| Approach: | They propose a framework to benchmark, elicit, and enhance role-playing abilities in Large Language Models. |
| Outcome: | The proposed framework improves role-playing abilities with 168,093 samples. |
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. |
TableLLM: Enabling Tabular Data Manipulation by LLMs in Real Office Usage Scenarios (2025.findings-acl)
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Xiaokang Zhang, Sijia Luo, Bohan Zhang, Zeyao Ma, Jing Zhang, Yang Li, Guanlin Li, Zijun Yao, Kangli Xu, Jinchang Zhou, Daniel Zhang-Li, Jifan Yu, Shu Zhao, Juanzi Li, Jie Tang
| Challenge: | TableLLM is a robust large language model capable of handling tabular data manipulation tasks. |
| Approach: | They propose a distant supervision method for training which includes a reasoning process extension strategy and a cross-way validation strategy. |
| Outcome: | The proposed model has 8 billion parameters and is capable of handling tabular data tasks. |
Translatotron-V(ison): An End-to-End Model for In-Image Machine Translation (2024.findings-acl)
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| Challenge: | In-image machine translation (IIMT) aims to translate an image containing texts in source language into an image with translations in target language. |
| Approach: | They propose an end-to-end IIMT model with four modules that translate images . they propose a two-stage training framework to assist the model in learning alignment across languages . |
| Outcome: | The proposed model outperforms cascaded models with only 70.9% of parameters and is highly accurate. |
Efficient Domain Continual pretraining by Mitigating the Stability Gap (2025.acl-long)
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| Challenge: | Continual pretraining is an important approach for Large Language Models to improve their performance in target domains, learn new topics and languages, and even boost their general capabilities. |
| Approach: | They propose a training strategy that mitigates instability by increasing the number of epochs, along with two data sampling strategies targeting data domain relevance and corpus distribution. |
| Outcome: | The proposed training strategy improves the average medical task performance of the OpenLlama-3B model from 36.2% to 40.7% using only 40% of the original training budget, while also enhancing general task performance without causing forgetting. |
AutoRE: Document-Level Relation Extraction with Large Language Models (2024.acl-demos)
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| Challenge: | Existing methods for relation extraction are limited to Sentence-level Relation Extraction (SentRE) tasks. |
| Approach: | They propose an end-to-end DocRE model that adopts a novel RE extraction paradigm named RHF (Relation-Head-Facts) Unlike existing approaches, AutoRE does not rely on the assumption of known relation options, making it more reflective of real-world scenarios. |
| Outcome: | The proposed model surpasses TAG by 10.03% and 9.03% on the dev and test set. |
YEDDA: A Lightweight Collaborative Text Span Annotation Tool (P18-4)
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| Challenge: | Existing annotation tools do not consider post-annotation quality analysis due to inter-annotator disagreement. |
| Approach: | They propose a lightweight but efficient open-source tool for text span annotation that can be used for collaborative user annotation and administrator evaluation and analysis. |
| Outcome: | The proposed system reduces the annotation time by half compared with existing tools and the time can be compressed by 16.47% through intelligent recommendation. |
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding (2024.acl-long)
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Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li
| Challenge: | Large language models (LLMs) can only handle texts a few thousand tokens long, limiting their applications on longer sequence inputs, such as books, reports, and codebases. |
| Approach: | They propose a bilingual, multi-task benchmark for long context understanding that extends context windows and more sophisticated memory mechanisms to improve models' long context capabilities. |
| Outcome: | The proposed model outperforms open-source models but struggles on longer contexts. |
OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models (2025.acl-long)
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Siming Huang, Tianhao Cheng, Jason Klein Liu, Weidi Xu, Jiaran Hao, Liuyihan Song, Yang Xu, Jian Yang, Jiaheng Liu, Chenchen Zhang, Linzheng Chai, Ruifeng Yuan, Xianzhen Luo, Qiufeng Wang, YuanTao Fan, Qingfu Zhu, Zhaoxiang Zhang, Yang Gao, Jie Fu, Qian Liu, Houyi Li, Ge Zhang, Yuan Qi, Xu Yinghui, Wei Chu, Zili Wang
| Challenge: | Code LLMs lack reproducible data pipelines and training protocols for reproducible advancements in code intelligence. |
| Approach: | They propose a top-tier code LLM that releases model weights and inference code . reproducible data pipelines, rigorous experimental ablation results and training protocols are included . |
| Outcome: | The proposed model achieves comparable performance to leading models and serves as an "open cookbook" reproducible training data, rigorous experimental ablation results, and detailed training protocols are also included in the model. |
Consistency Regularization Training for Compositional Generalization (2023.acl-long)
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| Challenge: | Existing neural models have difficulty generalizing to unseen combinations of seen components. |
| Approach: | They propose to improve the capability of Transformer on compositional generalization by consistency regularization training without modifying model architectures. |
| Outcome: | The proposed model performs well on semantic parsing and machine translation benchmarks. |
LongAlign: A Recipe for Long Context Alignment of Large Language Models (2024.findings-emnlp)
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| Challenge: | Existing studies to build long context language models focus on context extension and continual training on long text. |
| Approach: | They propose a recipe for instruction fine-tuning on input sequences of similar length . they adopt packing and sorted batching strategies to speed up supervised fine-uning . |
| Outcome: | The proposed model outperforms existing recipes for LLMs in long context tasks by 30% while maintaining proficiency in handling short, generic tasks. |
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis (2021.findings-emnlp)
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| Challenge: | Existing approaches to Aspect-based sentiment analysis do not exploit the interactive relations among subtasks and do not utilize document-level labeled domain/sentiment knowledge, which restricts their performance. |
| Approach: | They propose an iterative multi-knowledge transfer network for end-to-end ABSA that leverages the inter-task interaction between subtasks. |
| Outcome: | The proposed approach improves on three benchmark datasets. |
A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)
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Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang
| Challenge: | Inductive reasoning is an important task for large language models (LLMs). |
| Approach: | They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation. |
| Outcome: | The proposed method improves inductive reasoning in large language models. |
Towards Knowledge-Based Recommender Dialog System (D19-1)
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| Challenge: | Existing frameworks that only provide information about user preferences can be inaccurate in e-commerce recommender systems. |
| Approach: | They propose a framework which integrates the recommender system and dialog generation system by introducing information about users’ preferences. |
| Outcome: | The proposed framework can achieve better performance in both dialog generation and recommendation compared with baselines. |
T2R-BENCH: A Benchmark for Real World Table-to-Report Task (2025.emnlp-main)
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Jie Zhang, Changzai Pan, Sishi Xiong, Kaiwen Wei, Yu Zhao, Xiangyu Li, Jiaxin Peng, Xiaoyan Gu, Jian Yang, Wenhan Chang, Zhenhe Wu, Jiang Zhong, Shuangyong Song, Xuelong Li
| Challenge: | Existing table benchmarks lack the capacity to adequately assess the practical application of table reasoning in industrial applications. |
| Approach: | They propose a bilingual table-to-report task and a table-based benchmark to assess the quality of table reasoning. |
| Outcome: | The proposed task is based on a bilingual benchmark with 457 industrial tables and evaluation criteria to measure the quality of report generation. |
ABC-Bench: Benchmarking Agentic Backend Coding in Real-World Development (2026.findings-acl)
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Jie Yang, Honglin Guo, Li Ji, Jiazheng Zhou, Rui Zheng, Zhikai Lei, Shuo Zhang, Zhiheng Xi, Shichun Liu, Yuxin Wang, Bo Wang, Yining Zheng, Tao Gui, Xipeng Qiu
| Challenge: | Large Language Models (LLMs) have redefined the role of AI in software engineering . current benchmarks focus on localized code generation, but neglect dynamic, full-process requirements of real-world engineering. |
| Approach: | They propose a benchmark to evaluate agentic backend coding within a realistic, executable workflow. |
| Outcome: | The ABC-Bench benchmark evaluates agentic backend coding within a realistic, executable workflow. |
Execution as Verification: Fine-Grained Self-Correcting Reasoning for Complex KBQA (2026.acl-long)
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| Challenge: | Existing knowledge base question answering methods are limited by syntactic constraints and are prone to structural deviations that render queries unexecutable. |
| Approach: | They propose a framework that reframes semantic parsing as an iterative reasoning process driven by execution feedback. |
| Outcome: | The proposed method achieves significant improvements in query executability and answer accuracy on the WebQSP and CWQ datasets. |
GKD: A General Knowledge Distillation Framework for Large-scale Pre-trained Language Model (2023.acl-industry)
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| Challenge: | Existing knowledge distillation frameworks for language models are limited by memory and the use of complex distillation methods on larger-scale PLMs. |
| Approach: | They propose a general knowledge distillation framework that supports distillation on larger-scale PLMs using various distillation methods. |
| Outcome: | The proposed framework can support distillation on larger-scale PLMs and 25 mainstream methods on 8 NVIDIA A100 (40GB) GPUs. |
CoT-based Synthesizer: Enhancing LLM Performance through Answer Synthesis (2025.acl-long)
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| Challenge: | Existing inference scaling methods rely heavily on the quality of candidate responses . however, they are unable to produce correct answers when all candidates are flawed . |
| Approach: | They propose a CoT-based inference scaling strategy that leverages CoT reasoning to synthesize superior answers by analyzing complementary information from multiple candidate responses. |
| Outcome: | The proposed method improves performance on four benchmark datasets with seven policy models. |
CharacterGLM: Customizing Social Characters with Large Language Models (2024.emnlp-industry)
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Jinfeng Zhou, Zhuang Chen, Dazhen Wan, Bosi Wen, Yi Song, Jifan Yu, Yongkang Huang, Pei Ke, Guanqun Bi, Libiao Peng, JiaMing Yang, Xiyao Xiao, Sahand Sabour, Xiaohan Zhang, Wenjing Hou, Yijia Zhang, Yuxiao Dong, Hongning Wang, Jie Tang, Minlie Huang
| Challenge: | Character-based dialogue systems (CharacterDial) allow users to customize social characters for social interactions. |
| Approach: | They will collect a large-scale Chinese corpus of characters with diverse categories and behaviors and develop CharacterGLM models to address these challenges. |
| Outcome: | Experiments show that CharacterGLM outperforms most popular open- and closed-source LLMs and performs comparable to GPT-4. |
Cheems: A Practical Guidance for Building and Evaluating Chinese Reward Models from Scratch (2025.acl-long)
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Xueru Wen, Jie Lou, Zichao Li, Yaojie Lu, XingYu XingYu, Yuqiu Ji, Guohai Xu, Hongyu Lin, Ben He, Xianpei Han, Le Sun, Debing Zhang
| Challenge: | Existing Chinese resources are small in scale and limited to specific domains, making them insufficient for LLM post-training. |
| Approach: | They propose a Chinese-annotated reward model and a preference dataset to address this gap . they evaluate Chinese RMs on CheemsBench and construct an RM that captures human preferences . |
| Outcome: | The proposed RM achieves state-of-the-art performance on CheemsBench and CheeMePreference. |
Mitigating Hallucinations in LM-Based TTS Models via Distribution Alignment Using GFlowNets (2025.emnlp-main)
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| Challenge: | Existing mitigation strategies for Text-to-Speech systems require excessive training resources or inference latency. |
| Approach: | They propose a GFlOwNet-guided distribution AlignmenT framework that mitigates hallucinations without relying on massive resources or inference latency. |
| Outcome: | The proposed framework reduces over 50% character error rates and lowers uncertainty by up to 58% on challenging test cases. |
Advancing MoE Efficiency: A Collaboration-Constrained Routing (C2R) Strategy for Better Expert Parallelism Design (2025.naacl-long)
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| Challenge: | Using Mixture-of-Experts, researchers have found that efficient MoE is difficult to achieve due to two key reasons: imbalanced expert activation and massive communication overhead. |
| Approach: | They propose a collaboration-constrained routing strategy that encourages more specialized expert groups and leverages expert specialization. |
| Outcome: | The proposed approach achieves an average performance improvement of 0.51% and 0.33% on LLaMA-MoE and Qwen-MaE respectively. |
Understanding Translationese in Cross-Lingual Summarization (2023.findings-emnlp)
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| Challenge: | Existing datasets involve translation, but translationese is distinguished from original text . previous studies have shown that translationeses in CLS are not a problem in training sets . |
| Approach: | They propose to use cross-lingual summarization to generate a concise summary in a target language from a document in . existing datasets typically involve translation in their creation, but the translated text is distinguished from the original written in that language. |
| Outcome: | The proposed method systematically investigates how translationese affects CLS model evaluation and performance when it appears in source documents or target summaries. |
Stand on The Shoulders of Giants: Building JailExpert from Previous Attack Experience (2025.emnlp-main)
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Xi Wang, Songlei Jian, Shasha Li, Xiaopeng Li, Bin Ji, Ma Jun, Xiaodong Liu, Jing Wang, Jianfeng Zhang, Jie Yu, Feilong Bao, null Wangbaosheng
| Challenge: | Existing methods to generate human-aligned content with a “jailbreak prompt” are inefficient and repetitive, causing inefficiency and a lack of experience. |
| Approach: | They propose a framework that integrates past attack experiences to aid current jailbreak attempts. |
| Outcome: | The proposed framework improves both attack effectiveness and efficiency compared to the current black-box jailbreak method. |
Mixture of Attention Heads: Selecting Attention Heads Per Token (2022.emnlp-main)
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| Challenge: | Mixture-of-Experts (MoE) networks have been proposed as an efficient way to scale up model capacity and implement conditional computing. |
| Approach: | They propose a new architecture that combines multi-head attention with the MoE mechanism and a sparsely gated architecture that allows for faster computations. |
| Outcome: | The proposed architecture can scale up the number of attention heads and the number parameters while preserving computational efficiency. |
A Self-Denoising Model for Robust Few-Shot Relation Extraction (2025.acl-long)
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| Challenge: | Existing studies assume that the support set contains only accurately labeled instances, but this assumption is often unrealistic. |
| Approach: | They propose a self-denoising model for FSRE which can automatically correct noisy labels of support instances. |
| Outcome: | The proposed model outperforms all baselines on two public datasets showing that it can correct mislabeled support instances. |
Toward Fully Exploiting Heterogeneous Corpus:A Decoupled Named Entity Recognition Model with Two-stage Training (2021.findings-acl)
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| Challenge: | Named Entity Recognition (NER) is a fundamental and widely used task in natural language processing. |
| Approach: | They propose a decoupled NER model with two-stage training to take advantage of heterogeneous corpus, including dictionaries, distantly supervised instances, and human-annotated instances. |
| Outcome: | Empirical results show that the proposed model improves against baselines and can be scaled to a large extent. |
ConceptMath: A Bilingual Concept-wise Benchmark for Measuring Mathematical Reasoning of Large Language Models (2024.findings-acl)
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Yanan Wu, Jie Liu, Xingyuan Bu, Jiaheng Liu, Zhanhui Zhou, Yuanxing Zhang, Chenchen Zhang, ZhiqiBai ZhiqiBai, Haibin Chen, Tiezheng Ge, Wanli Ouyang, Wenbo Su, Bo Zheng
| Challenge: | ConceptMath evaluates concept-wise mathematical reasoning of Large Language Models (LLMs) Existing benchmarks that evaluate general mathematical reasoning with an average accuracy fail to probe the fine-grained failure modes of mathematical reasoning on specific datasets. |
| Approach: | They introduce a bilingual, fine-grained benchmark that evaluates concept-wise mathematical reasoning of Large Language Models. |
| Outcome: | The proposed benchmarks evaluate concept-wise mathematical reasoning of Large Language Models with concept-based accuracies. |
AndroidGen: Building an Android Language Agent under Data Scarcity (2025.acl-long)
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| Challenge: | Existing LLMs lack high-quality data sources and lack robust data filtration strategies. |
| Approach: | They develop a framework to enhance the capabilities of LLM-based agents under data scarcity. |
| Outcome: | The proposed framework improves the capabilities of LLM-based agents under data scarcity. |
Token-level Adaptive Training for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing token imbalance phenomenon in natural language as different tokens appear with different frequencies, which leads to different learning difficulties for tokens in Neural machine translation (NMT). |
| Approach: | They propose to assign tokens with different frequencies to target tokens during training to encourage the model to pay more attention to low-frequency tokens. |
| Outcome: | The proposed model yields consistent improvements on ZH-EN, EN-RO, and EN-DE translation tasks, especially on sentences that contain more low-frequency tokens. |
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)
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Yuelin Bai, Xeron Du, Yiming Liang, Leo Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Moore Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang
| Challenge: | Existing datasets for Chinese instruction tuning are not well-aligned with Chinese users’ interaction patterns. |
| Approach: | They propose to use Chinese instruction tuning datasets to improve instruction fine-tuning for Chinese users. |
| Outcome: | The proposed dataset shows that Chinese models achieve competitive performance in diverse benchmarks. |
Hiring Now: A Skill-Aware Multi-Attention Model for Job Posting Generation (2020.acl-main)
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| Challenge: | Creating job requirements is a crucial step in the recruiting process, but it is difficult to specify the level of education, experience, relevant skills per the job description. |
| Approach: | They propose a conditional text generation task to generate job requirements based on job descriptions . they use a hierarchical decoder to label the job description with multiple skills . a skill knowledge graph is constructed to capture the global prior knowledge about skills based upon the model . |
| Outcome: | The proposed method is evaluated on real-world job posting data. |
TeachMaster: Generative Teaching via Code (2026.acl-industry)
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Yuheng Wang, Runde Yang, Lin Wu, Jie Zhang, Jingru Fan, Tianle Zhou, Ruoyu Fu, Huatao Li, Ruijie Shi, Siheng Chen, Weinan E, Chen Qian
| Challenge: | Existing methods for creating video content are limited by high costs and slow update cycles. |
| Approach: | They propose a paradigm shifting educators from manual creators to high-level directors who focus on pedagogical intents while agents handle execution. |
| Outcome: | The proposed framework reduces production costs to 0.3% of traditional course videos and provides a robust solution for scalable education. |
Chinese NER Using Lattice LSTM (P18-1)
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| Challenge: | Chinese named entity recognition (NER) is a fundamental task in information extraction. |
| Approach: | They propose a lattice-structured LSTM model for Chinese named entity recognition (NER) model leverages word and word sequence information to encode a sequence of input characters and all potential words that match a lexicon. |
| Outcome: | The proposed model outperforms word-based and character-based models on Chinese NER datasets. |
ELLE: Efficient Lifelong Pre-training for Emerging Data (2022.findings-acl)
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| Challenge: | Existing pre-trained language models are typically trained with static data, ignoring that streaming data of various sources may continuously grow. |
| Approach: | They propose to use function preserved model expansion to expand existing PLM's width and depth to improve efficiency of knowledge acquisition. |
| Outcome: | The proposed model improves pre-training efficiency and performance over existing models on BERT and GPT. |
Multi-Granularity Self-Attention for Neural Machine Translation (D19-1)
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| Challenge: | Existing neural machine translation models use a deep multi-head self-attention network with no explicit phrase information. |
| Approach: | They propose a neural network that combines multi-head self-attention and phrase modeling to train attention heads to attend to phrases in either n-gram or syntactic formalisms. |
| Outcome: | The proposed approach improves on English-to-German and NIST Chinese-to English translation tasks. |
STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training (2026.acl-long)
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Qiuyi Qi, Tian Liang, Mutian Bao, Jinjian Zhang, Dongnan Liu, Wei Zhou, Linjian Mo, Ming Kong, Jie Liu, Feng Zhang, Qiang Zhu
| Challenge: | Prior work has explored step-level supervision using Shannon-entropy-based uncertainty signals, which conflate inherent state complexity with agent confidence. |
| Approach: | They propose a hierarchical group-based RL framework that leverages normalized entropy to locate outlier steps associated with trajectory neglect and optimizes them via a mechanism of trajectory-aware reward and trajectory-independent penalty. |
| Outcome: | Experiments on ALFWorld, WebShop, and Search-Augmented QA show that STAPO achieves state-of-the-art performance while substantially alleviating trajectory neglect. |
One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment (2020.coling-main)
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| Challenge: | Existing methods for automatic comment generation generate common and meaningless comments for music. |
| Approach: | They propose a multi-perspective strategy to enhance automatic music comment generation by combining different perspectives on a music comment dataset. |
| Outcome: | The proposed model outperforms state-of-the-art models on two music comment datasets and outperformed existing models by a substantial margin. |
LazyEviction: Lagged KV Eviction with Attention Pattern Observation for Efficient Long Reasoning (2026.acl-long)
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| Challenge: | Existing KV cache compression methods mitigate memory bottlenecks but struggle in long reasoning tasks. |
| Approach: | They propose a lagged eviction framework that prioritizes evicts based on tokens’ recurrence patterns to reduce KV cache by 50% and maintain comparable accuracy. |
| Outcome: | The proposed framework reduces KV cache by 50% 70% while maintaining comparable accuracy, outperforming existing KV baselines. |
Asclepius: A Spectrum Evaluation Benchmark for Medical Multi-Modal Large Language Models (2025.acl-long)
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Jie Liu, Wenxuan Wang, Su Yihang, Jingyuan Huang, Yudi Zhang, Cheng-Yi Li, Wenting Chen, Xiaohan Xing, Kao-Jung Chang, Linlin Shen, Michael R. Lyu
| Challenge: | Medical Multi-Modal Large Language Models (Med-MLLMs) are a promising new form of artificial general intelligence due to their ability to tackle complex tasks. |
| Approach: | They propose a new benchmark that comprehensively assesses medical multi-modal large language models in terms of distinct medical specialties and different diagnostic capacities. |
| Outcome: | The proposed model covers 15 medical specialties and different diagnostic capacities, and excludes overlap with existing VQA dataset. |
Thinking Beyond the Local: Multi-View Instructed Adaptive Reasoning in KG-Enhanced LLMs (2026.findings-eacl)
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| Challenge: | Existing methods for large language models adopt query-driven iterative reasoning from a local perspective, limiting efficiency and accuracy for complex multi-hop tasks. |
| Approach: | They propose a multi-view instructed adaptive reasoning of LLM on Knowledge Graphs that allows LLMs to plan, evaluate, and adapt reasoning paths from a global perspective. |
| Outcome: | The proposed model overcomes the limitations of local exploration by enabling LLMs to plan, evaluate, and adapt reasoning paths from a global perspective. |
AutoCAD: Automatically Generate Counterfactuals for Mitigating Shortcut Learning (2022.findings-emnlp)
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| Challenge: | Existing methods for generating counterfactuals rely on human efforts or task-specific designs. |
| Approach: | They propose to use a fully automatic and task-agnostic CAD generation framework to generate diverse counterfactuals. |
| Outcome: | The proposed framework outperforms human-in-the-loop and task-specific CAD methods on multiple out-of-domain and challenge benchmarks. |
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling (P19-1)
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| Challenge: | Existing systems for sequence labeling are limited by shallow connections between consecutive hidden states and insufficient modeling of global information. |
| Approach: | They propose a global context enhanced deep transition architecture for sequence labeling . they deepen the state transition path at each position in a sentence and assign tokens with global representations . |
| Outcome: | The proposed architecture outperforms the best reported results on two standard sequence labeling tasks. |
Competence-based Curriculum Learning for Multilingual Machine Translation (2021.findings-emnlp)
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| Challenge: | Existing multilingual machine translation models face an imbalance problem due to the different learning competencies of different languages. |
| Approach: | They propose Competence-based Curriculum Learning for Multilingual Machine Translation, named CCL-M, to help schedule the high resource languages and low resource languages. |
| Outcome: | The proposed approach achieves a steady and significant performance gain compared to the previous state-of-the-art approach on the TED talks dataset. |
ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring (2026.acl-industry)
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Deyi Ji, Junyu Lu, Xuanyi Liu, Liqun Liu, Hailong Zhang, Peng Shu, Huan Yu, Jie Jiang, Tianrun Chen, Lanyun Zhu
| Challenge: | Existing regulatory policies create label inconsistencies and reasoning ambiguities in historical datasets. |
| Approach: | They propose a policy-adaptive governance system that enables evolving reinforcement through multi-agent adversarial umpiring. |
| Outcome: | The proposed system outperforms fine-tuning baselines on industrial and public datasets . it enables evolving reinforcement through multi-agent adversarial umpiring . |
Deconstruct, Diagnose, and Deliberate: A Protocol-Adaptive Role-Specific Multi-Agent Framework for Fake News Detection (2026.findings-acl)
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| Challenge: | Existing methods for fake news detection rely on monolithic verification methods . Existing approaches often yield ambiguous verdicts due to superficial processing . |
| Approach: | They propose a protocol-adaptive role-specific multi-agent framework that decomposes verification into factual, logical, and contextual dimensions. |
| Outcome: | The proposed framework outperforms baseline methods in both predictive accuracy and explanatory quality. |
ENT-DESC: Entity Description Generation by Exploring Knowledge Graph (2020.emnlp-main)
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| Challenge: | Existing models for knowledge-to-text generation use RDF triples or key-value pairs to generate a natural language description. |
| Approach: | They propose a large-scale dataset to facilitate the study of KG-to-text . they propose MGCN model architecture that incorporates aggregation methods to extract the rich graph information. |
| Outcome: | The proposed model can represent the original graph information more comprehensively and integrates multiple aggregation methods to extract the rich graph information. |
A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code (2026.findings-acl)
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Keke Lian, Wang Bin, Lei Zhang, Libo Chen, Junjie Wang, Ziming Zhao, Yujiu Yang, Miaoqian Lin, Haotong Duan, Haoran Zhao, Shuang Liao, Mingda Guo, Quan Jiazheng, Yilu Zhong, Chenhao He, Chen Zichuan, Jie Wu, Haoling Li, Zhaoxuan Li, Jiongchi Yu, Hui LI, Dong Zhang
| Challenge: | Existing security evaluation benchmarks lack relevance to real-world AI programming tasks . current LLMs struggle with secure coding, research shows . |
| Approach: | They propose a repository-level evaluation benchmark to assess security of AI-generated code. |
| Outcome: | The proposed framework mirrors real-world AI programming tasks and offers valuable insights into the state of AI code generation. |
ToolDNA: Autonomous Evolution of Tool Metadata for Robust Dialogue Agents (2026.findings-acl)
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| Challenge: | Task-oriented dialogue systems face labor-intensive manual metadata tuning and sparse reinforcement learning (RL) rewards that fail to diagnose invocation errors. |
| Approach: | They propose a framework that enables auto-evolution of policy networks and tool metadata via RL . a tool metadata loop coordinates metadata through policy-generated candidates during rollouts . |
| Outcome: | The proposed framework achieves +11% problem resolution and +54% accuracy over commercial LLMs with prompt engineering and +25%/+35% over supervised fine-tuning. |
TSAM: A Two-Stream Attention Model for Causal Emotion Entailment (2022.coling-1)
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| Challenge: | Existing studies on EAC focus on Emotion Recognition in Conversations (ERC), i.e., recognizing emotion labels of utterances. |
| Approach: | They propose a two-stream attention model to capture correlations between utterances in a global view and classify multiple utterrances synchronously to capture emotion and speaker information in parallel. |
| Outcome: | The proposed model outperforms baselines and achieves new State-Of-The-Art (SOTA) performance. |
TiMem: Temporal-Hierarchical Memory Consolidation for Long-Horizon Conversational Agents (2026.findings-acl)
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Kai Li, Xuanqing Yu, Ziyi Ni, Yi Zeng, Yao Xu, Zheqing Zhang, Xin Li, Jitao Sang, Xiaogang Duan, Xuelei Wang, Chengbao Liu, Jie Tan
| Challenge: | Existing memory frameworks provide limited support for temporally structured information across hierarchical levels, leading to fragmented memories and unstable long-horizon personalization. |
| Approach: | They propose a temporal–hierarchical memory framework that organizes conversations through a Temporal Memory Tree. |
| Outcome: | The proposed framework outperforms baselines while reducing the recalled memory length by 52.20%. |
Subword Encoding in Lattice LSTM for Chinese Word Segmentation (N19-1)
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| Challenge: | Using subwords, Chinese word segmentation models use character combination information to disambiguate characters. |
| Approach: | They propose a subword-based neural word segmentor that integrates character embeddings into a Lattice LSTM network over a character sequence. |
| Outcome: | The proposed model can utilize abundant character combination information, which is effective to disambiguate characters. |
OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement (2024.findings-acl)
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| Challenge: | OpenCodeInterpreter-33B provides a high level of performance for code generation, executing, and iterative refinement. |
| Approach: | They propose a family of open-source code systems for generating, executing, and iteratively refining code. |
| Outcome: | The OpenCodeInterpreter-33B performs well on humanEval, MBPP, and EvalPlus benchmarks. |
Instructed Language Models with Retrievers Are Powerful Entity Linkers (2023.emnlp-main)
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| Challenge: | Generative approaches powered by large language models have demonstrated emergent abilities in tasks that require complex reasoning abilities. |
| Approach: | They propose a sequence-to-sequence training objective with instruction-tuning that enables casual language models to perform entity linking over knowledge bases. |
| Outcome: | The proposed framework outperforms existing approaches with +6.8 F1 points gain on average and huge advantage in training data efficiency and compute consumption. |
CORBA: Contagious Recursive Blocking Attacks on Multi-Agent Systems Based on Large Language Models (2026.findings-acl)
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| Challenge: | Existing security models rely on open-ended communication, but the collaborative process itself can be exploited and disrupted. |
| Approach: | They propose a new threat class, called Denial-of-Collaboration, which corrupts collaborative structure and transforms communication topology into self-sabotage. |
| Outcome: | The proposed attacks bypass conventional safety alignments that are not designed to detect behavioral or systemic attacks. |
Parallel Interactive Networks for Multi-Domain Dialogue State Generation (2020.emnlp-main)
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| Challenge: | Existing models do not account for the dependencies between system and user utterances in the same turn and across different turns. |
| Approach: | They propose to integrate an interactive encoder to jointly model in-turn dependencies and cross-turn dependents. |
| Outcome: | The proposed model is superior to existing models and can be used to selectively copy words from historical system utterances or historical user utterrances. |