Papers by Wei Huang
Snapshot-Guided Domain Adaptation for ELECTRA (2022.findings-emnlp)
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| Challenge: | Existing domain-specific knowledge of domain-related tasks is lacking in pre-trained language models. |
| Approach: | They propose a domain-adaptation method which can dynamically select domain-specific tokens and guide the discriminator to emphasize them, without introducing new training parameters. |
| Outcome: | The proposed method can capture domain-specific knowledge of domain-related tasks without introducing new training parameters. |
Math Word Problem Solving with Explicit Numerical Values (2021.acl-long)
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| Challenge: | Existing methods for solving math word problems ignore numerical values in solving problems. |
| Approach: | They propose a numerically-based approach that explicitly incorporates numerical values into a sequence-to-tree network and uses a mathematical properties prediction mechanism to capture category and comparison information of numerals. |
| Outcome: | The proposed model outperforms existing state-of-the-art models on the Math23K and APE datasets. |
HAF-RM: A Hybrid Alignment Framework for Reward Model Training (2025.acl-long)
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Shujun Liu, Xiaoyu Shen, Yuhang Lai, Siyuan Wang, Shengbin Yue, Zengfeng Huang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Recent studies have focused on enhancing reward models through data improvements, following the conventional training framework for reward models that directly optimizes the predicted rewards. |
| Approach: | They propose a hybrid alignment framework **HAF-RM** that incorporates additional constraint on token-level policy probabilities in addition to the reward score. |
| Outcome: | The proposed framework can supervise the internal preference model at the token level and optimize the mapping layer of the reward model at sequence level. |
From Isolated Scoring to Collaborative Ranking: A Comparison-Native Framework for LLM-Based Paper Evaluation (2026.findings-acl)
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Pujun Zheng, Jiacheng Yao, Jinquan Zheng, Chenyang Gu, Guoxiu He, Jiawei Liu, Yong Huang, Tianrui Guo, Wei Lu
| Challenge: | Large language models (LLMs) are currently used to evaluate scientific papers by assigning an absolute score to each paper independently. |
| Approach: | They propose a comparison-native framework for paper evaluation that integrates comparison into both data construction and model learning. |
| Outcome: | The proposed framework achieves an average relative improvement of 21.8% over the strong baseline DeepReview-14B, while exhibiting robust generalization to five previously unseen datasets. |
PTCSpell: Pre-trained Corrector Based on Character Shape and Pinyin for Chinese Spelling Correction (2023.findings-acl)
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| Challenge: | Chinese spelling correction (CSC) is a task which detects incorrect characters in Chinese text and corrects them. |
| Approach: | They propose to pre-train a Chinese spelling correction corrector under the detector-corrector architecture and propose to capture pronunciation and shape information in Chinese characters. |
| Outcome: | The proposed corrector achieves an average of 5.8% F1 improvements over state-of-the-art methods, verifying its effectiveness. |
Better Process Supervision with Bi-directional Rewarding Signals (2025.findings-acl)
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Wenxiang Chen, Wei He, Zhiheng Xi, Honglin Guo, Boyang Hong, Jiazheng Zhang, Nijun Li, Tao Gui, Yun Li, Qi Zhang, Xuanjing Huang
| Challenge: | Existing processes that reward for each step are one-directional and lack a mechanism to model the distance to the final target. |
| Approach: | They propose a process supervision model that evaluates the correctness of previous steps and the probability of future success. |
| Outcome: | The proposed model outperforms existing supervision models like ORM and PRM on reasoning tasks and improves solution re-design. |
When Evolution Strategy Meets Language Models Tuning (2025.coling-main)
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| Challenge: | Autoregressive language models with pretraining often display limited capability in effectively following instructions. |
| Approach: | They propose an on-policy approach to optimize models by harnessing the principle of biological evolution, namely survival of the fittest. |
| Outcome: | The proposed method can achieve superior performance in various tasks and comparable performance in the human alignment task. |
UnifiedMLLM: Enabling Unified Representation for Multi-modal Multi-tasks With Large Language Model (2025.findings-naacl)
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Zhaowei Li, Wei Wang, YiQing Cai, Qi Xu, Pengyu Wang, Dong Zhang, Hang Song, Botian Jiang, Zhida Huang, Tao Wang
| Challenge: | Representative models like LLaVA and MiniGPT-4 have great capabilities in various tasks. |
| Approach: | They propose a unified model to represent various multi-modal tasks using a single representation. |
| Outcome: | The proposed model outperforms existing models in a variety of tasks while maintaining generality and scalability. |
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. |
Symbol tuning improves in-context learning in language models (2023.emnlp-main)
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Jerry Wei, Le Hou, Andrew Lampinen, Xiangning Chen, Da Huang, Yi Tay, Xinyun Chen, Yifeng Lu, Denny Zhou, Tengyu Ma, Quoc Le
| Challenge: | Language models are sensitive to the way that prompts are given, indicating that they are not reasoning in a robust manner. |
| Approach: | They propose to fine tune language models on in-context input-label pairs where natural language labels are replaced with arbitrary symbols. |
| Outcome: | The proposed model is much stronger at reasoning tasks and more robust to underspecified prompts than the standard model. |
Knowledge Verification to Nip Hallucination in the Bud (2024.emnlp-main)
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| Challenge: | Recent studies have shown that large language models generate responses that sound plausible but contradict factual knowledge, a phenomenon known as hallucination. |
| Approach: | They propose a novel approach to align large language models to evaluate knowledge boundaries based on external knowledge to reduce hallucinations . |
| Outcome: | The proposed approach reduces hallucinations across six benchmarks using foundation LLMs of varying backbones and scales. |
Optimizing Deeper Transformers on Small Datasets (2021.acl-long)
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Peng Xu, Dhruv Kumar, Wei Yang, Wenjie Zi, Keyi Tang, Chenyang Huang, Jackie Chi Kit Cheung, Simon J.D. Prince, Yanshuai Cao
| Challenge: | a common belief that training deep transformers from scratch requires large datasets is wrong . however, with proper initialization and optimization, the benefits of very deep transformer can carry over to challenging tasks with small datasets. |
| Approach: | They train 48 layers of transformers from pre-trained RoBERTa and 24 relation-aware layers from scratch. |
| Outcome: | The proposed scheme achieves state-of-the-art performance on a text-to-sql parsing benchmark . it uses 24 fine-tuned layers from pre-trained RoBERTa and 24 relation-aware layers from scratch . |
Adaptive Policy with Wait-k Model for Simultaneous Translation (2023.emnlp-main)
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| Challenge: | Existing approaches to simultaneous machine translation require a robust read/write policy . a standalone multi-path wait-k model performs competitively with adaptive policies . |
| Approach: | They propose a more flexible approach by decoupling the adaptive policy model from the translation model. |
| Outcome: | The proposed approach outperforms baseline approaches in translation tasks. |
Cross-Lingual Phrase Retrieval (2022.acl-long)
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| Challenge: | Existing approaches to cross-lingual phrase retrieval learn word or sentence representations in word or sentences. |
| Approach: | They propose a cross-lingual phrase retrieval model that extracts phrase representations from unlabeled example sentences. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a large-scale cross-lingual phrase retrieval dataset, showing it can perform in an unseen language pair during training. |
CROP: Zero-shot Cross-lingual Named Entity Recognition with Multilingual Labeled Sequence Translation (2022.findings-emnlp)
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Jian Yang, Shaohan Huang, Shuming Ma, Yuwei Yin, Li Dong, Dongdong Zhang, Hongcheng Guo, Zhoujun Li, Furu Wei
| Challenge: | Named entity recognition (NER) suffers from the scarcity of annotated training data, especially for low-resource languages without labeled data. |
| Approach: | They propose a cross-lingual entity projection framework to enable zero-shot cross-linguistic NER with the help of a multilingual labeled sequence translation model. |
| Outcome: | The proposed method outperforms the baseline method on two benchmarks by a large margin of +3 7 F1 scores and achieves state-of-the-art performance. |
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA (2022.acl-long)
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Zewen Chi, Shaohan Huang, Li Dong, Shuming Ma, Bo Zheng, Saksham Singhal, Payal Bajaj, Xia Song, Xian-Ling Mao, Heyan Huang, Furu Wei
| Challenge: | ELECTRA-style tasks are used to pretrain cross-lingual models for NLP tasks . masked language modeling tasks require massive computation resources, rendering such models quite expensive . |
| Approach: | They propose to use ELECTRA-style tasks to pre-train a cross-lingual language model . they propose to pretrain the model on multilingual and parallel corpora . |
| Outcome: | The proposed model outperforms baseline models on cross-lingual understanding tasks with much less computation cost. |
LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin (2024.acl-long)
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Shihan Dou, Enyu Zhou, Yan Liu, Songyang Gao, Wei Shen, Limao Xiong, Yuhao Zhou, Xiao Wang, Zhiheng Xi, Xiaoran Fan, Shiliang Pu, Jiang Zhu, Rui Zheng, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Experimental results show that, as the instruction data increases, LoRAMoE can significantly improve the ability to process downstream tasks, while maintaining the world knowledge stored in the LLM. |
| Approach: | They propose a framework that introduces several low-rank adapters and integrates them by using a router network to freeze the backbone model and force a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks. |
| Outcome: | The proposed framework freezes the backbone model and forces a portion of LoRAs to focus on leveraging world knowledge to solve downstream tasks, to alleviate world knowledge forgetting. |
MentalSeek-Dx: Towards Progressive Hypothetico-Deductive Reasoning for Real-world Psychiatric Diagnosis (2026.acl-long)
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Xiao Sun, null Ymyang, Xinyi Jiang, Yu Tian, Junnan Zhu, Jiang Zhong, Qin Lei, Jingwang Huang, Haoyang Zeng, Xinyu Zhou, Xin Xiao, Kaiwen Wei
| Challenge: | Mental health disorders represent a burgeoning global public health challenge . lack of ecological validity and fine-grained diagnostic supervision limits their utility . |
| Approach: | They propose a medical-specialized LLM trained to internalize clinical reasoning process through supervised trajectory construction and curriculum-based reinforcement learning. |
| Outcome: | The proposed model achieves state-of-the-art with only 14B parameters, establishing a clinically grounded framework for reliable psychiatric diagnosis. |
Argue with Me Tersely: Towards Sentence-Level Counter-Argument Generation (2023.emnlp-main)
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Jiayu Lin, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei
| Challenge: | Existing work describes paragraph-level counter-argument generation task as paragraph-based . however, sentence-level generation can be quite different due to its unique constraints and brevity-focused challenges. |
| Approach: | They propose a benchmark framework for sentence-level counter-argument generation . they use an annotated debate forum dataset to generate high-quality counter-argments . |
| Outcome: | The proposed framework and evaluator are competitive in counter-argument generation tasks. |
Towards Fast and Accurate Neural Chinese Word Segmentation with Multi-Criteria Learning (2020.coling-main)
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| Challenge: | Chinese word segmentation datasets have ambiguous annotation criteria resulting in multi-grained compounds. |
| Approach: | They propose a domain adaptive segmenter to exploit diverse annotation criteria of datasets . they use bidirectional encoder representations from transformers to introduce open-domain knowledge . |
| Outcome: | The proposed model outperforms the state-of-the-art models on 10 Chinese word datasets with superior efficiency. |
Chart-MRAG: Benchmarking Multimodal Retrieval Augmented Generation on Chart-based Documents (2026.acl-long)
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null Ymyang, Jiang Zhong, Li Jin, Xiao Sun, Jingwang Huang, null Gaojinpeng, Qing Liu, Yang Bai, Jingyuan Zhang, Rui Jiang, Qin Lei, Kaiwen Wei
| Challenge: | Existing benchmarks focus on simple image-text interactions, overlooking complex visual formats like charts. |
| Approach: | They propose a semi-automatic framework for generating evaluation samples through multi-modal keypoint extraction, knowledge graph construction, and qa pair synthesis. |
| Outcome: | The proposed framework generates 4,738 question-answering pairs across 8 domains from real-world documents. |
Don’t Reinvent the Wheel: Efficient Instruction-Following Text Embedding based on Guided Space Transformation (2025.acl-long)
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| Challenge: | Existing methods for text embedding require re-encoding the entire corpus for each instruction. |
| Approach: | They propose a framework that generates dynamic text embeddings that adapt to user instructions, highlighting specific attributes of text. |
| Outcome: | The proposed framework improves instruction-following text embedding quality over state-of-the-art methods while speeding up processing on large datasets. |
From Lists to Emojis: How Format Bias Affects Model Alignment (2025.acl-long)
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| Challenge: | Format biases in reinforcement learning from human feedback are underexplored . despite its effectiveness, RLHF faces challenges, including policy and regulatory constraints . |
| Approach: | They extend the study of preference biases beyond verbosity bias to a wider range of format biase . they show that with a small amount of biased data, they can inject significant bias into the reward model . |
| Outcome: | The proposed approach can be easily exploited by large language models to achieve higher rankings on popular benchmarks like AlpacaEval and LMSYS Chatbot Arena. |
CLOMO: Counterfactual Logical Modification with Large Language Models (2024.acl-long)
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Yinya Huang, Ruixin Hong, Hongming Zhang, Wei Shao, Zhicheng Yang, Dong Yu, Changshui Zhang, Xiaodan Liang, Linqi Song
| Challenge: | Existing studies on evaluating model reasoning are limited in both form and content. |
| Approach: | They propose a task to cultivate counterfactual thought processes within large language models and an evaluation metric to evaluate their natural language output instead of modeling the task as a multiple-choice problem. |
| Outcome: | The proposed evaluation metric aligns well with human preference. |
Context-DPO: Aligning Language Models for Context-Faithfulness (2025.findings-acl)
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Baolong Bi, Shaohan Huang, Yiwei Wang, Tianchi Yang, Zihan Zhang, Haizhen Huang, Lingrui Mei, Junfeng Fang, Zehao Li, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang, Shenghua Liu
| Challenge: | Context-DPO is the first alignment method specifically designed to enhance contextfaithfulness for large language models. |
| Approach: | They propose a benchmark that simulates Retrieval-Augmented Generation scenarios with knowledge conflicts to evaluate context-faithfulness. |
| Outcome: | The proposed method improves LLMs' context-faithfulness by 35% to 280% over open-source models. |
MergeME: Model Merging Techniques for Homogeneous and Heterogeneous MoEs (2025.naacl-long)
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Yuhang Zhou, Giannis Karamanolakis, Victor Soto, Anna Rumshisky, Mayank Kulkarni, Furong Huang, Wei Ai, Jianhua Lu
| Challenge: | State-of-the-art methods for merging expert models with different architectures do not address parameter interference and require extensive fine-tuning to restore performance. |
| Approach: | They propose a method for merging experts with different architectures into a unified Mixture-of-Experts model with a goal of enhancing performance in each domain while retaining effectiveness on general tasks. |
| Outcome: | Experiments across multiple domains show that the proposed methods reduce fine-tuning costs and improve performance over state-of-the-art methods. |
An Unsupervised Framework for Adaptive Context-aware Simplified-Traditional Chinese Character Conversion (2024.lrec-main)
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| Challenge: | Traditional Chinese characters are still widely used in many areas of China . traditional methods to convert between simplified characters are ineffective . |
| Approach: | They propose an unsupervised adaptive context-aware conversion model that learns to convert between simplified and traditional Chinese characters under a denoising auto-encoder framework. |
| Outcome: | The proposed model outperforms strong unsupervised baselines and yields better conversion result for one-to-many cases. |
Mixture of Ordered Scoring Experts for Cross-prompt Essay Trait Scoring (2025.acl-long)
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| Challenge: | Existing approaches to automate essay scoring overlook critical information, authors say . evaluators often limit their performance to unseen topics, resulting in incomplete assessment perspectives. |
| Approach: | They propose a framework that integrates information from prompts and essays into an AES framework. |
| Outcome: | The proposed framework achieves state-of-the-art in cross-prompt scoring and multi-trait scoring on the ASAP++ dataset. |
MAKAR: a Multi-Agent framework based Knowledge-Augmented Reasoning for Grounded Multimodal Named Entity Recognition (2025.emnlp-main)
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Xinkui Lin, Yuhui Zhang, Yongxiu Xu, Kun Huang, Hongzhang Mu, Yubin Wang, Gaopeng Gou, Li Qian, Li Peng, Wei Liu, Jian Luan, Hongbo Xu
| Challenge: | Existing methods for GMNER fail to address semantic ambiguity caused by polysemy and long-tail distribution of datasets. |
| Approach: | They propose a framework for Grounded Multimodal Named Entity Recognition that leverages a Multimodal Large Language Model to address semantic ambiguity. |
| Outcome: | Extensive experiments show that the proposed framework outperforms existing methods on two benchmark datasets. |
Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning (2023.findings-emnlp)
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Zhuolin Yang, Wei Ping, Zihan Liu, Vijay Korthikanti, Weili Nie, De-An Huang, Linxi Fan, Zhiding Yu, Shiyi Lan, Bo Li, Mohammad Shoeybi, Ming-Yu Liu, Yuke Zhu, Bryan Catanzaro, Chaowei Xiao, Anima Anandkumar
| Challenge: | Existing methods for image-to-text generation store all knowledge within parameters, thus requiring computational-expensive fine-tuning. |
| Approach: | They propose a Retrieval-augmented Visual Language Model that stores all the knowledge within parameters and can be used to retrieve it from the external database. |
| Outcome: | The proposed model significantly boosts performance for image-to-text generation tasks with 4x less parameters compared with baseline methods. |
Mitigating Catastrophic Forgetting in Large Language Models with Forgetting-aware Pruning (2025.emnlp-main)
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| Challenge: | Recent advances in large language models (LLMs) have shown impressive capabilities in various downstream tasks but typically face Catastrophic Forgetting (CF) during fine-tuning. |
| Approach: | They propose a pruning-based approach to balance CF and downstream task performance by integrating the ratio of the task vector to pre-trained model parameters into the pruning criteria. |
| Outcome: | The proposed pruning-based approach limits CF to just 0.25% while maintaining 99.67% accuracy on downstream tasks. |
ALaRM: Align Language Models via Hierarchical Rewards Modeling (2024.findings-acl)
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| Challenge: | Current alignment approaches struggle with inconsistency and sparsity of human supervision signals. |
| Approach: | They propose a framework modeling hierarchical rewards in reinforcement learning from human feedback (RLHF) it integrates holistic rewards with aspect-specific rewards to enhance alignment of large language models with human preferences. |
| Outcome: | The proposed framework improves the alignment of large language models with human preferences by integrating holistic rewards with aspect-specific rewards. |
A Lexicon-Based Graph Neural Network for Chinese NER (D19-1)
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| Challenge: | Chinese named entity recognition models are vulnerable to word ambiguities due to the lack of global semantics and chain structure. |
| Approach: | They propose a lexicon-based graph neural network with global semantics to solve word ambiguities in Chinese named entity recognition (NER) Lexicons are used to construct the graph and provide word-level features. |
| Outcome: | The proposed model improves on four NER datasets on Chinese characters, potential words, and the whole-sentence semantics. |
Unleashing the Power of Language Models in Text-Attributed Graph (2023.findings-emnlp)
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| Challenge: | Existing studies on graph learning on text-attributed graphs have been limited by memory cost and underutilization of relationships between nodes and words. |
| Approach: | They propose a Node Representation Update Pre-training Architecture based on Co-modeling text and graph to learn representations of papers and words simultaneously. |
| Outcome: | The proposed model outperforms baselines on the ogbn-arxiv benchmark dataset. |
Unveiling the Truth and Facilitating Change: Towards Agent-based Large-scale Social Movement Simulation (2024.findings-acl)
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| Challenge: | Existing methods for simulating social movements encounter challenges in capturing behavior of participants. |
| Approach: | They propose a hybrid framework for social media user simulation wherein users are categorized into two types: core and ordinary users. |
| Outcome: | The proposed framework is able to simulate the behavior of social media users across real-world datasets and demonstrate its effectiveness and flexibility. |
Attention-Guided Answer Distillation for Machine Reading Comprehension (D18-1)
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| Challenge: | Existing approaches to reading comprehension systems are vulnerable to adversarial attacks. |
| Approach: | They propose to use knowledge distillation to transfer knowledge from an ensemble to a single model. |
| Outcome: | The proposed methods outperform the teacher on adversarial datasets and NarrativeQA benchmarks. |
Vision Language Pre-training by Contrastive Learning with Cross-Modal Similarity Regulation (2023.acl-long)
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| Challenge: | Large-scale pre-trained vision-language models have recently achieved tremendous success on a wide range of cross-modal tasks. |
| Approach: | They propose a new framework for a semantically-aware contrastive learning that minimizes the MI between false negative and positive samples . |
| Outcome: | The proposed framework minimizes the MI between false negative samples and positive samples even though they share similar semantics. |
Generalized Intent Discovery: Learning from Open World Dialogue System (2022.coling-1)
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| Challenge: | Existing intent classification models rely on a pre-defined intent set and supervised labels, which is limited in some practical scenarios. |
| Approach: | They propose to extend an IND intent classifier to an open-world intent set including IND and OOD intents. |
| Outcome: | The proposed task can classify IND and OOD intents while discovering new unlabeled OOD types incrementally. |
Respond in my Language: Mitigating Language Inconsistency in Response Generation based on Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) show strong instruction understanding ability across multiple languages, but are easily biased towards English in instruction tuning. |
| Approach: | They propose to use a model with Pseudo-Inconsistent Penalization to prevent the model from generating English responses when given non-English language prompts during training and prior Enhanced decoding to improve the language consistency of the model. |
| Outcome: | The proposed methods significantly improve the language consistency of the model without multilingual data. |
Learning by Analogy: Diverse Questions Generation in Math Word Problem (2023.findings-acl)
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| Challenge: | Existing methods for solving math word problem (MWP) use shortcut learning to train solvers based on samples with a single question. |
| Approach: | They propose to generate diverse yet consistent questions from a common scenario . they then feed the equations to a question generator to obtain the diverse questions . their method leads to performance improvement on the current benchmark Math23K . |
| Outcome: | The proposed method generates diverse yet consistent questions with a variety of equations and questions . it improves on the current benchmark, which is based on the proposed method . |
Multilingual Agreement for Multilingual Neural Machine Translation (2021.acl-short)
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| Challenge: | Existing models that only use auxiliary languages to encourage multilingual agreement ignore the relationships between different language pairs. |
| Approach: | They propose a multilingual agreement-based method which explicitly models the agreement between different translation directions by randomly substituting some fragments of the source language with their counterpart translations of auxiliary languages. |
| Outcome: | The proposed method improves on the multilingual translation task of 10 language pairs. |
NUT-RC: Noisy User-generated Text-oriented Reading Comprehension (2020.coling-main)
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| Challenge: | Existing RC models focus on extractive or generative, but ignore integration of them. |
| Approach: | They propose a noisy user-generated text-oriented RC model that integrates extractive and generative RC models by a multi-task learning mechanism and an answer selection module. |
| Outcome: | The proposed model outperforms state-of-the-art models on Twitter. |
Pun2Pun: Benchmarking LLMs on Textual-Visual Chinese-English Pun Translation via Pragmatics Model and Linguistic Reasoning (2025.acl-srw)
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| Challenge: | Current approaches resort to suboptimal compromises and computational methods remain inadequate for translation. |
| Approach: | They propose a Constant-Variable Optimization (CVO) model for translation strategy and an Ovl metric for translation quality assessment that adapts to Chinese and English. |
| Outcome: | The proposed model improves performance on textual and visual puns while maintaining linguistic mechanisms and humorous effects. |
Temporal Scaling Law for Large Language Models (2025.emnlp-main)
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Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Zhenpeng Su, Wei Huang, Jianwei Niu, Jungong Han, Guiguang Ding
| Challenge: | Existing studies have found that the test loss of LLMs scales as power-laws with model size, computational budget, and dataset size. |
| Approach: | They propose a concept of Temporal Scaling Law to study test loss of LLMs . they break down test loss into fine-grained token positions and develop a dynamic hyperbolic-law . |
| Outcome: | The proposed model predicts the test loss of LLMs as the training steps scale up. |
TableBank: Table Benchmark for Image-based Table Detection and Recognition (2020.lrec-1)
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| Challenge: | Existing techniques for table detection and recognition are limited to document types and layouts. |
| Approach: | They propose to build a table detection and recognition dataset with weak supervision from Word and Latex documents on the internet. |
| Outcome: | The proposed dataset contains 417K high quality labeled tables and is publicly available. |
Allocating Large Vocabulary Capacity for Cross-Lingual Language Model Pre-Training (2021.emnlp-main)
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| Challenge: | Existing models require a more expressive vocabulary to represent all languages . however, increasing the vocabulary size significantly slows down the pre-training speed . |
| Approach: | They propose an algorithm VoCap to determine the desired vocabulary capacity of each language. |
| Outcome: | The proposed algorithm improves cross-lingual model pre-training while reducing side effects of increasing vocabulary size. |
Unveiling Selection Biases: Exploring Order and Token Sensitivity in Large Language Models (2024.findings-acl)
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| Challenge: | Using zero-shot or few-shot prompting, Large Language Models have been widely adopted in downstream applications. |
| Approach: | They propose to quantify the impact of option order and token usage on LLMs and propose mitigation strategies to enhance model performance. |
| Outcome: | The proposed mitigation strategies improve model performance and reduce the impact of token and order sensitivity on LLMs. |
Interpretable Composition Attribution Enhancement for Visio-linguistic Compositional Understanding (2024.emnlp-main)
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| Challenge: | Despite promising progress, vision-language models still exhibit significant challenges in understanding visio-linguistic concepts beyond object terms. |
| Approach: | They propose a framework that encourages the model to pay greater attention to composition words denoting relationships and attributes within the text. |
| Outcome: | The proposed framework improves the ability to discern intricate details and construct more sophisticated interpretations of combined visual and linguistic elements. |
Can Monolingual Pretrained Models Help Cross-Lingual Classification? (2020.aacl-main)
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| Challenge: | Multilingual pretrained language models have shown impressive results for cross-lingual transfer, but due to the constant model capacity, multilingual pre-training usually lags behind the monolingual competitors. |
| Approach: | They propose to transfer the knowledge from monolingual pretrained models to multilingual ones to improve zero-shot cross-lingual classification by using machine translation systems. |
| Outcome: | The proposed methods outperform vanilla multilingual fine-tuning on two cross-lingual classification benchmarks. |
Revamping Multilingual Agreement Bidirectionally via Switched Back-translation for Multilingual Neural Machine Translation (2024.findings-eacl)
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| Challenge: | Current multilingual agreement (MA) methods require parallel data between multiple language pairs, which is not always realistic and optimize the agreement in an ambiguous direction, which hampers the translation performance. |
| Approach: | They propose a novel multilingual agreement framework that optimizes agreement bidirectionally with the Kullback-Leibler Divergence loss. |
| Outcome: | The proposed method improves strong baselines on the task of multilingual neural machine translation with three benchmarks: TED Talks, News, and Europarl. |
Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) demonstrate impressive multilingual capability, but their performance varies substantially across different languages. |
| Approach: | They propose a generic template prompt that stimulates cross-lingual and logical reasoning skills to enhance task performance across languages. |
| Outcome: | The proposed method improves multilingual capability across languages and covers high-resource and low-resourced languages. |
HyKnow: End-to-End Task-Oriented Dialog Modeling with Hybrid Knowledge Management (2021.findings-acl)
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| Challenge: | Task-oriented dialog systems typically manage structured knowledge to guide goal-oriented conversations. |
| Approach: | They propose a TOD system with hybrid knowledge management, HyKnow, which extends the belief state to manage both structured and unstructured knowledge. |
| Outcome: | The proposed model outperforms existing TOD systems in the evaluation of a multiWOZ dataset on unstructured knowledge with strong end-to-end performance. |
HD-Eval: Aligning Large Language Model Evaluators Through Hierarchical Criteria Decomposition (2024.acl-long)
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Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Large language models (LLMs) are a promising alternative to expensive human evaluations. |
| Approach: | They propose a framework that iteratively aligns LLM-based evaluators with human preference . they decompose a given evaluation task into finer-grained criteria . |
| Outcome: | The proposed framework iteratively aligns LLM-based evaluators with human preference . it decomposes a given evaluation task into finer-grained criteria . the framework is efficient to train and more explainable than relying solely on prompts . |
A Data-Efficient Path to Multilingual LLMs: Language Expansion via Post-training PARAM𝛥 Integration into Upcycled MoE (2026.acl-long)
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Hao Zhou, Tianhao Li, Zhijun Wang, Shuaijie She, Linjuan Wu, Hao-Ran Wei, Baosong Yang, Jiajun Chen, Shujian Huang
| Challenge: | Large Language Models (LLMs) are expensive and require extensive Continued Pre-Training and data-intensive alignment to expand. |
| Approach: | They propose a method which upcycles a dense model into a Mixture-of-Experts architecture, allocating different experts to different languages. |
| Outcome: | Experiments show that the proposed model upcycles a dense model into a Mixture-of-Experts(MoE) architecture, allocating different experts to different languages. |
Explore Spurious Correlations at the Concept Level in Language Models for Text Classification (2024.acl-long)
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| Challenge: | Language models have demonstrated remarkable performance in numerous NLP tasks, employing both fine-tuning and in-context learning (ICL) methods. |
| Approach: | They propose a method to assess concept bias in models during fine-tuning and in-context learning using ChatGPT. |
| Outcome: | The proposed method outperforms token removal approaches and is validated through extensive testing. |
Visual Evidence Prompting Mitigates Hallucinations in Large Vision-Language Models (2025.acl-long)
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| Challenge: | LVLMs have shown impressive progress by integrating visual perception with linguistic understanding to produce contextually grounded outputs. |
| Approach: | They propose a visual evidence prompting method to mitigate hallucinations in large vision-language models by using small visual models to complement them. |
| Outcome: | The proposed method reduces hallucinations by reducing false activation and enhancing correct ones. |
Negative Sample is Negative in Its Own Way: Tailoring Negative Sentences for Image-Text Retrieval (2022.findings-naacl)
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| Challenge: | Existing approaches to retrieve hard negative sentences are limited in the scale of the dataset thus fail to identify negative samples of high difficulty for every image. |
| Approach: | They propose to use a model to generate synthetic negative sentences with higher difficulty by masking and refilling the images and performing word discrimination and word correction tasks to improve retrieval and generation. |
| Outcome: | The proposed model generates synthetic negative sentences with higher difficulty on MS-COCO and Flickr30K and is robust and faithful to state-of-the-art training. |
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment (2022.acl-long)
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Zijie Huang, Zheng Li, Haoming Jiang, Tianyu Cao, Hanqing Lu, Bing Yin, Karthik Subbian, Yizhou Sun, Wei Wang
| Challenge: | Existing methods to predict missing facts in knowledge graphs are limited in language alignment . SS-AGA uses seed alignment as an edge type to fuses all KGs as a whole graph . |
| Approach: | They propose a self-supervised adaptive graph alignment method that fuses all KGs as a whole graph by regarding alignment as 'a new edge type' they propose SS-AGA method that uses relation-aware attention weights to capture potential alignment pairs in a new paradigm. |
| Outcome: | The proposed method can predict missing facts in a knowledge graph (KG) but language alignment is scarce and new alignment identification is noisy. |
mT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs (2021.emnlp-main)
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Zewen Chi, Li Dong, Shuming Ma, Shaohan Huang, Saksham Singhal, Xian-Ling Mao, Heyan Huang, Xia Song, Furu Wei
| Challenge: | Multilingual T5 pretrains a sequence-to-sequence model on monolingual texts, but it has shown promising results on many cross-lingual tasks. |
| Approach: | They propose a partially non-autoregressive objective for text-to-text pre-training and propose mT6 to improve cross-lingual transferability over multilingual T5. |
| Outcome: | The proposed model improves cross-lingual transferability over existing models. |
MAssistant: A Personal Knowledge Assistant for MOOC Learners (D19-3)
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| Challenge: | Massive Open Online Courses (MOOCs) have experienced a rapid development since 2012 . many MOOC platforms have been launched, including Coursera1 , edX2 , and Udacity3 etc. |
| Approach: | They present a personal knowledge assistant system called MAssistant for MOOC learners . MAsistants has a large-scale concept graph built from open data . it also provides a browser extension which interacts with users during video lectures . |
| Outcome: | The proposed system helps users trace the concepts they have learned in MOOCs, and to build their own concept graphs. |
Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset (2026.findings-acl)
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Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu, Yuan Qi
| Challenge: | Existing supervised fine-tuning methods struggle to generalize across document types, leading to poor performance. |
| Approach: | They propose layoutRL, a reinforcement learning framework that optimizes layout understanding through composite rewards integrating normalized edit distance, paragraph count accuracy, and reading order preservation. |
| Outcome: | The proposed model outperforms specialized document parsing systems and general-purpose vision-language models on a broad range of document types, languages, and structural complexities. |
AgentV-RL: Scaling Reward Modeling with Agentic Verifier (2026.findings-acl)
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Jiazheng Zhang, Ziche Fu, Zhiheng Xi, Wenqing Jing, Mingxu Chai, Wei He, Guoqiang Zhang, Chenghao Fan, Chenxin An, Wenxiang Chen, Zhicheng Liu, Haojie Pan, Dingwei Zhu, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to improve LLM reasoning are limited in complex domains and lack external grounding makes verifiers unreliable on computation-intensive tasks. |
| Approach: | They propose a framework that transforms reward modeling into a multi-turn, tool-augmented deliberative process. |
| Outcome: | The proposed framework surpasses state-of-the-art ORMs by 25.2% under parallel and sequential TTS. |
Unveiling and Consulting Core Experts in Retrieval-Augmented MoE-based LLMs (2024.emnlp-main)
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Xin Zhou, Ping Nie, Yiwen Guo, Haojie Wei, Zhanqiu Zhang, Pasquale Minervini, Ruotian Ma, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing research seeks to enhance RAG performance by retrieving higher-quality documents or designing RAG-specific LLMs, but internal mechanisms that contribute to RAG’s effectiveness remain underexplored. |
| Approach: | They propose to examine the internal mechanisms within the popular Mixture-of-Expert (MoE)-based LLMs and examine their ability to improve RAG by examining expert activations. |
| Outcome: | The proposed method significantly improved the ability of Large Language Models (LLMs) to solve knowledge-intensive tasks. |
MTVQA: Benchmarking Multilingual Text-Centric Visual Question Answering (2025.findings-acl)
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Jingqun Tang, Qi Liu, Yongjie Ye, Jinghui Lu, Shu Wei, An-Lan Wang, Chunhui Lin, Hao Feng, Zhen Zhao, Yanjie Wang, Yuliang Liu, Hao Liu, Xiang Bai, Can Huang
| Challenge: | Text-Centric Visual Question Answering (TEC-VQA) is a text-centric visual task understanding tool. |
| Approach: | They introduce a benchmark that features human expert annotations across 9 languages . they prioritize the text in question-answer pairs while disregarding visual text in images . |
| Outcome: | The proposed benchmarks prioritize the text in question-answer pairs while disregarding visual text in images. |
On Sample Based Explanation Methods for NLP: Faithfulness, Efficiency and Semantic Evaluation (2021.acl-long)
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| Challenge: | Existing methods for explaining "black-box" models such as Influence Functions are becoming more popular. |
| Approach: | They propose a semantic-based evaluation metric that can better align with humans’ judgment of explanations than the widely adopted diagnostic or re-training measures. |
| Outcome: | The proposed method can better align with humans’ judgment of explanations than diagnostic or re-training measures. |
“I’ve Decided to Leak”: Probing Internals Behind Prompt Leakage Intents (2025.emnlp-main)
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Jianshuo Dong, Yutong Zhang, Liu Yan, Zhenyu Zhong, Tao Wei, Ke Xu, Minlie Huang, Chao Zhang, Han Qiu
| Challenge: | Large language models (LLMs) exhibit prompt leakage vulnerabilities, raising intellectual property and confidentiality concerns. |
| Approach: | They use probing techniques to capture LLMs’ intent-related internal representations and show that they internalize prompt leakage intents in their hidden states before generating tokens. |
| Outcome: | The proposed probes achieve 90%+ AUROC across all tested models, even when applied to new system prompts and attacks. |
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs (2023.findings-acl)
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Zijie Huang, Daheng Wang, Binxuan Huang, Chenwei Zhang, Jingbo Shang, Yan Liang, Zhengyang Wang, Xian Li, Christos Faloutsos, Yizhou Sun, Wei Wang
| Challenge: | Existing methods to embed knowledge graphs have ignored the fact that they contain two fundamentally different views: high-level ontology-view concepts and fine-grained instance-view entities. |
| Approach: | They propose a novel geometric representation that jointly embeds the two views of a KG using dual geometric representations. |
| Outcome: | Experiments on the public DBpedia KG and a newly-created industrial KG show the proposed method works well. |
A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extraction (2026.findings-acl)
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Chengguang Gan, Sunbowen Lee, Qingyu Yin, Yunhao Liang, Xinyang He, Hanjun Wei, Younghun Lim, Shijian Wang, Hexiang Huang, QingHao Zhang, Shiwen Ni, Tatsunori Mori
| Challenge: | Existing work on the Mutual Reinforcement Effect in information extraction has not been empirically validated . 76 percent of the 21 sub-datasets exhibit the Mutual Reforcement effect across languages . |
| Approach: | They propose a multilingual MRE mix dataset that integrates 21 sub-datasets covering English, Japanese, and Chinese. |
| Outcome: | The proposed framework reduces manual annotation effort while preserving structural requirements of MRE tasks. |
PALM: Pre-training an Autoencoding&Autoregressive Language Model for Context-conditioned Generation (2020.emnlp-main)
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| Challenge: | Existing techniques for natural language understanding and generation use autoencoding and/or autoregressive objectives to train models. |
| Approach: | They propose a self-supervised pre-training scheme that pre-trains an autoencoding and autoregressive language model on a large unlabeled corpus for generating new text conditioned on context. |
| Outcome: | The proposed scheme achieves state-of-the-art results on a variety of language generation benchmarks covering generative question answering, abstractive summarization and conversational response generation. |
VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models (2025.naacl-long)
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| Challenge: | Despite the impressive capabilities of large multi-modal models, their effectiveness in handling complex tasks has been limited by the prevailing singlestep reasoning paradigm. |
| Approach: | They propose a visuallygrounded object-centric Chain-of-Thought reasoning framework for LMMs that is based on a multi-modal interleaved and aligned representation of object concepts. |
| Outcome: | The proposed model outperforms SOTA models in CLEVR and EmbSpatial benchmarks. |
Better Simultaneous Translation with Monotonic Knowledge Distillation (2023.acl-long)
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| Challenge: | Existing methods to train offline MT models require generating target tokens before source sentence is fully consumed. |
| Approach: | They propose a method that leverages traditional translation models as teachers to generate monotonic yet accurate reference translations for sequence-level knowledge distillation. |
| Outcome: | The proposed approach improves on strong baselines and on a monotonic version of the WMT15 De-En test set. |
GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator (2023.acl-long)
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Jian Yang, Shuming Ma, Li Dong, Shaohan Huang, Haoyang Huang, Yuwei Yin, Dongdong Zhang, Liqun Yang, Furu Wei, Zhoujun Li
| Challenge: | Existing pre-training methods underutilize the benefits of language understanding for generation. |
| Approach: | They propose a GAN-style model for encoder-decoder pre-training with an auxiliary discriminator. |
| Outcome: | The proposed model outperforms existing pre-trained models and achieves state-of-the-art performance. |
DialogGen: Multi-modal Interactive Dialogue System with Multi-turn Text-Image Generation (2025.findings-naacl)
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Minbin Huang, Yanxin Long, Xinchi Deng, Ruihang Chu, Jiangfeng Xiong, Xiaodan Liang, Hong Cheng, Qinglin Lu, Wei Liu
| Challenge: | Text-to-image (T2I) generation models have advanced in recent years, but effective interaction with these models is challenging for average users due to the need for specialized prompt engineering knowledge and the inability to perform multi-turn image generation. |
| Approach: | They propose to use off-the-shelf MLLMs and T2I models to build a multi-modal interactive dialogue system (MIDS) that can generate correct output modalities and coherence of output images. |
| Outcome: | The proposed pipeline can generate correct output modalities and coherent multi-modal outputs compared with other state-of-the-art models. |
Query Structure Modeling for Inductive Logical Reasoning Over Knowledge Graphs (2023.acl-long)
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| Challenge: | Existing methods for inductive reasoning over knowledge graphs lack the ability to model the logical structures of complex queries. |
| Approach: | They propose a structure-modeled textual encoding framework for inductive logical reasoning over KGs that encodes linearized query structures and entities using pre-trained language models to find answers. |
| Outcome: | The proposed framework encodes query structures and entities using pre-trained language models to find answers. |
Through the Valley: Path to Effective Long CoT Training for Small Language Models (2025.emnlp-main)
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| Challenge: | Long chain-of-thought (CoT) supervision is effective for large language models . but small models trained on limited long CoT data experience performance degradation . |
| Approach: | They identify a phenomenon called Long CoT Degradation in small language models . long CoT data can be used to generate long chain-of-thought (CoT) responses . |
| Outcome: | The results show that models trained on 8k long CoT examples lose up to 75% of their original performance before fine-tuning. |
Mitigating Tail Narrowing in LLM Self-Improvement via Socratic-Guided Sampling (2025.naacl-long)
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Yiwen Ding, Zhiheng Xi, Wei He, Lizhuoyuan Lizhuoyuan, Yitao Zhai, Shi Xiaowei, Xunliang Cai, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Large language models (LLMs) generate solutions themselves and iteratively train on filtered, high-quality rationales, but performance reaches a ceiling after a few iterations. |
| Approach: | They propose a strategy to improve the efficiency of sampling heavy-tailed data by using Socratic-style guidance signals to help LLMs reasoning with complex queries. |
| Outcome: | The proposed approach is effective on difficult queries and on held-out tasks, while requiring human supervision. |
Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules (2024.emnlp-main)
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| Challenge: | Empirical results show that MoMs consistently outperform vanilla transformers . |
| Approach: | They propose an architecture that allows for a mixture-of-modules computation that uses a finite set of modules defined by multi-head attention and feed-forward networks. |
| Outcome: | The proposed architecture outperforms vanilla Transformers and their variants in multiple ways. |
AttenWalker: Unsupervised Long-Document Question Answering via Attention-based Graph Walking (2023.findings-acl)
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| Challenge: | Existing methods for annotating long-document question answering are based on short documents and can hardly incorporate long-range information. |
| Approach: | They propose an unsupervised method to generate long-document question answering pairs . they propose a method to aggregate and generate answers with long-range dependency . |
| Outcome: | The proposed method outperforms existing methods on NarrativeQA and Qasper. |
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)
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Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Large language models are increasingly employed to empower autonomous agents to simulate human behavior. |
| Approach: | They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts. |
| Outcome: | The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning. |
EARL: Informative Knowledge-Grounded Conversation Generation with Entity-Agnostic Representation Learning (2021.emnlp-main)
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| Challenge: | Existing knowledge-grounded conversation models lack knowledge that occurs in training data, resulting in incomplete knowledge generation. |
| Approach: | They propose an Entity-Agnostic Representation Learning method to introduce knowledge graphs to informative conversation generation using context of conversations and relational structure of knowledge graph. |
| Outcome: | The proposed model generates more informative, coherent, and natural responses than baseline models. |
Learning to Refine: Self-Refinement of Parallel Reasoning in LLMs (2026.findings-acl)
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Qibin Wang, Pu Zhao, Shaohan Huang, Fangkai Yang, Lu Wang, Furu Wei, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing approaches to test-time scaling are limited due to the quality of candidate responses. |
| Approach: | They propose a new metric to quantify the relative improvement of self-refinement beyond majority voting. |
| Outcome: | The proposed method achieves state-of-the-art performance across five benchmarks over other methods. |
AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)
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Zhiheng Xi, Yiwen Ding, Wenxiang Chen, Boyang Hong, Honglin Guo, Junzhe Wang, Xin Guo, Dingwen Yang, Chenyang Liao, Wei He, Songyang Gao, Lu Chen, Rui Zheng, Yicheng Zou, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, Yu-Gang Jiang
| Challenge: | Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents. |
| Approach: | They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
| Outcome: | The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction. |
MCS: An In-battle Commentary System for MOBA Games (2022.coling-1)
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Xiaofeng Qi, Chao Li, Zhongping Liang, Jigang Liu, Cheng Zhang, Yuanxin Wei, Lin Yuan, Guang Yang, Lanxiao Huang, Min Li
| Challenge: | In-battle commentary is an important component of live streaming of e-sports competitions and is applicable to a wide range of scenarios like combat information analysis and live streaming. |
| Approach: | They propose a generative system for in-battle real-time commentary in mobile MOBA games and propose 'transform' method to convert match statistics and utterances into consistent encoding space. |
| Outcome: | The proposed system is based on real-time match statistics and events and can be used for live streaming, e-sports commentary and combat information analysis. |
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models (2024.lrec-main)
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| Challenge: | Existing methods for incorporating external knowledge into language models do not prioritize learning embeddings for entity-related tokens. |
| Approach: | They propose a framework for incorporating external knowledge into pre-training models that utilize entity-related tokens. |
| Outcome: | The proposed framework reduces pre-training time by 50% and outperforms other KEPLMs in knowledge probing tasks and multiple knowledge-aware language understanding tasks. |
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. |
Se2: Sequential Example Selection for In-Context Learning (2024.findings-acl)
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| Challenge: | Prior work has explored the selection of examples for in-context learning, neglecting the internal relationships between examples and exist an inconsistency between training and inference. |
| Approach: | They propose a sequential-aware method that leverages the LLM’s feedback on varying context, aiding in capturing inter-relationships and sequential information among examples. |
| Outcome: | Experiments on 23 NLP tasks show that Se2 surpasses baselines and achieves 42% relative improvement over random selection. |
Modeling Evolution of Message Interaction for Rumor Resolution (2020.coling-main)
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| Challenge: | Existing methods for rumor resolution ignore local interactions during the message diffusion which is important for the identification of rumors. |
| Approach: | They propose to model confrontation and reciprocity between message pairs via discrete variational autoencoders which effectively reflects the diversified opinion interactivity. |
| Outcome: | Experiments on a PHEME dataset show that the proposed model achieves higher accuracy than existing methods. |
Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)
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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan
| Challenge: | Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems. |
| Approach: | They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations. |
| Outcome: | The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods. |
MMR-GRPO: Accelerating GRPO-Style Training through Diversity-Aware Reward Reweighting (2026.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in mathematical reasoning tasks. |
| Approach: | They propose to use Maximal Marginal Relevance to reweigh rewards of multiple rollouts by balancing rollout quality with diversity to reduce rollout redundancy. |
| Outcome: | The proposed approach reduces training time and costs by 47.9% . evaluations across three model sizes, three GRPO variants, and five mathematical reasoning benchmarks show that it achieves comparable peak performance while requiring on average 70.2% less wall-clock time. |
A Comprehensive Evaluation of Quantization Strategies for Large Language Models (2024.findings-acl)
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| Challenge: | Quantization studies have focused on instruction-tuned LLMs, leaving their performance on other benchmarks unclear. |
| Approach: | They propose a framework to evaluate quantized large language models using four dimensions . they propose to reduce the bits needed for model weights or activations with minimal performance loss . |
| Outcome: | The proposed framework can retain comparable performance to non-quantized LLMs on most benchmarks. |
Text Augmentation in a Multi-Task View (2021.eacl-main)
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| Challenge: | a multi-task view of data augmentation allows for a more robust performance than traditional augmentation. |
| Approach: | They propose a multi-task view of data augmentation where original and augmented samples are weighted substantively during training. |
| Outcome: | The proposed model improves on three benchmark text classification datasets. |
Neural Response Generation with Meta-words (P19-1)
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| Challenge: | Experimental results show that meta-words can be used to generate open domain dialogues . human-machine conversation is a fundamental problem in NLP . |
| Approach: | They propose a goal-tracking memory network that formalizes meta-word expression as a target in response generation and manages the generation process with a state memory panel and a controller. |
| Outcome: | The proposed model outperforms state-of-the-art generation models in response relevance, response diversity, and accuracy. |
Align Voting Behavior with Public Statements for Legislator Representation Learning (2021.acl-long)
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| Challenge: | Existing studies rely on roll call data to estimate political preference of legislators. |
| Approach: | They propose to integrate voting behavior and public statements on Twitter to jointly model legislators. |
| Outcome: | The proposed model improves on the task of roll call vote prediction . it also shows that the model captures nuances in statements . |
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)
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| Challenge: | Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems. |
| Approach: | They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance. |
| Outcome: | The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist. |
PathQG: Neural Question Generation from Facts (2020.emnlp-main)
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| Challenge: | Existing research for question generation encodes text as a sequence of tokens without explicitly modeling fact information. |
| Approach: | They propose to incorporate facts in the input text for question generation in a comprehensive way. |
| Outcome: | The proposed model outperforms state-of-the-art models and human evaluation shows it generates relevant and informative questions. |
Multi-level Association Refinement Network for Dialogue Aspect-based Sentiment Quadruple Analysis (2025.acl-long)
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| Challenge: | Existing methods for identifying quadruples rely on predefined dialogue structure and word semantics to achieve accurate and comprehensive sentiment associations between utterances and words. |
| Approach: | They propose a multi-level association refinement network to achieve more accurate sentiment associations between utterances and words. |
| Outcome: | The proposed framework achieves state-of-the-art performance under low-resource conditions. |
GeAR: Generation Augmented Retrieval (2025.findings-acl)
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Haoyu Liu, Shaohan Huang, Jianfeng Liu, Yuefeng Zhan, Hao Sun, Weiwei Deng, Feng Sun, Furu Wei, Qi Zhang
| Challenge: | Document retrieval techniques are used to compute semantic similarity between a query and documents, but the scalar similarity fails to reflect enough information, hindering the interpretation of retrieval results. |
| Approach: | They propose a method which improves the global document-query similarity through contrastive learning and integrates well-designed fusion and decoding modules. |
| Outcome: | The proposed method improves the global document-query similarity through contrastive learning and integrates well-designed fusion and decoding modules. |
Bias in the Ear of the Listener: Assessing Sensitivity in Audio Language Models Across Linguistic, Demographic, and Positional Variations (2026.findings-eacl)
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| Challenge: | Recent advances extend language understanding beyond text to speech, enabling unified reasoning across modalities. |
| Approach: | They construct and release a speech-augmented benchmark based on Global MMLU Lite and a data set spanning English, Chinese, and Korean. |
| Outcome: | The proposed model is robust to demographic factors but sensitive to language and option order, suggesting that speech can amplify structural biases. |
Multi-Objective Forward Reasoning and Multi-Reward Backward Refinement for Product Review Summarization (2024.lrec-main)
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| Challenge: | Product review summarization aims to generate a concise summary based on product reviews . factual accuracy, aspect comprehensiveness, and content relevance are challenges . |
| Approach: | They propose an FB-Thinker framework to improve product review summarization ability . they propose two Chinese product review summary datasets for instruction-tuning and evaluation . |
| Outcome: | The proposed framework improves product review summarization with forward reasoning and backward refinement. |
MAPO: Advancing Multilingual Reasoning through Multilingual-Alignment-as-Preference Optimization (2024.acl-long)
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| Challenge: | Existing models exhibit inconsistent reasoning abilities across different languages . existing models lack consistency across languages due to imbalance of training data . |
| Approach: | They propose a multilingual alignment-as-preference optimization framework to align reasoning processes in other languages with the dominant language. |
| Outcome: | The proposed framework improves multilingual reasoning across languages on three benchmarks. |
Modeling Historical Relevant and Local Frequency Context for Representation-Based Temporal Knowledge Graph Forecasting (2024.findings-emnlp)
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| Challenge: | Existing representation-based approaches neglect candidate-specific temporal context, resulting in serious information loss or homogeneous prediction. |
| Approach: | They propose a temporal representation learning model that incorporates temporal contexts of candidates and models temporal contextual information from historiCal Relevant context and locAl Frequency contexT. |
| Outcome: | The proposed model can leverage temporal contextual information to achieve differential predictions on six benchmark datasets. |
IgSEG: Image-guided Story Ending Generation (2021.findings-acl)
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| Challenge: | Existing tasks such as story ending generation generate text-based story endings, but visual storytelling generates photo-streams-based stories. |
| Approach: | They propose a task called Image-guided Story Ending Generation (IgSEG) given a multi-sentence story plot and an ending-related image, they propose MGCL to solve these challenges. |
| Outcome: | The proposed model outperforms baselines on automatic and human evaluation. |
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models (2024.findings-acl)
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Xueliang Zhao, Xinting Huang, Tingchen Fu, Qintong Li, Shansan Gong, Lemao Liu, Wei Bi, Lingpeng Kong
| Challenge: | Multimodal reasoning is a key capability for large vision-language models . however, the vanilla Chain-of-Thought method fails to address critical steps in multi-step reasoning tasks. |
| Approach: | They propose a bi-modal Behavioral Alignment method to augment multimodal reasoning . they use domain-specific language to integrate multimodal information into a precise alternative form . |
| Outcome: | The proposed method significantly improves GPT-4V(ision) on geometry problem solving, chess positional advantage prediction and molecular property prediction. |
SFMSS: Service Flow aware Medical Scenario Simulation for Conversational Data Generation (2025.findings-naacl)
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| Challenge: | Medical-specific Large Language Models (LLMs) have demonstrated impressive performance on medical-related exams and tasks. |
| Approach: | They propose a framework for medical conversational data generation that uses Authentic Seed Data to ensure quality of the data. |
| Outcome: | The proposed model outperforms all baselines and human evaluations, and aligns with human preferences and clinical demands. |
FlashAudio: Rectified Flow for Fast and High-Fidelity Text-to-Audio Generation (2025.acl-long)
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| Challenge: | Recent advances in latent diffusion models (LDMs) have markedly enhanced text-to-audio generation, yet their iterative sampling processes impose substantial computational demands, limiting practical deployment. |
| Approach: | They propose to learn straight flow for fast simulation by using flashAudio with rectified flows and immiscible flow to minimize the total distance of data-noise pairs in a batch vias assignment. |
| Outcome: | The proposed method can learn straight flow for fast simulations and reduce noise distribution. |
TRIP: Accelerating Document-level Multilingual Pre-training via Triangular Document-level Pre-training on Parallel Data Triplets (2023.findings-emnlp)
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Hongyuan Lu, Haoyang Huang, Shuming Ma, Dongdong Zhang, Wai Lam, Zhaochuan Gao, Anthony Aue, Arul Menezes, Furu Wei
| Challenge: | Existing approaches to multilingual sequence-to-sequence pre-training rely on monolingual corpora and sometimes synthetic document-level bilingual corporata. |
| Approach: | They propose to leverage document-level trilingual parallel corpora to improve sequence-to-sequence multilingual pre-training by using a novel method called Grafting. |
| Outcome: | The proposed method achieves strong state-of-the-art (SOTA) scores on three multilingual document-level machine translation benchmarks and one cross-lingual abstractive summarization benchmark. |
Democratizing Reasoning Ability: Tailored Learning from Large Language Model (2023.emnlp-main)
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Zhaoyang Wang, Shaohan Huang, Yuxuan Liu, Jiahai Wang, Minghui Song, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Large language models (LLMs) exhibit impressive emergent abilities in natural language processing, but their democratization is hindered due to huge computation requirements and closed-source nature. |
| Approach: | They propose a tailored learning approach to distill the exclusive reasoning ability to smaller LMs to facilitate democratization. |
| Outcome: | The proposed approach enables the democratization of the exclusive reasoning ability by leveraging the black-box model as a reasoning teacher. |
Benchmark Self-Evolving: A Multi-Agent Framework for Dynamic LLM Evaluation (2025.coling-main)
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| Challenge: | Recent advances in Large Language Models have demonstrated remarkable performance across tasks. |
| Approach: | They propose a benchmark self-evolving framework to dynamically evaluate rapidly advancing Large Language Models. |
| Outcome: | The proposed framework extends existing benchmarks to extend models across tasks and tasks. |
AnchorSeg: Language Grounded Query Banks for Reasoning Segmentation (2026.acl-long)
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Rui Qian, Chuanhang Deng, Qiang Huang, Jian Xiong, Mingxuan Li, Yingbo Zhou, Wei Zhai, Jintao Chen, Dejing Dou
| Challenge: | Existing models rely on a single segmentation token whose hidden state implicitly encodes both semantic reasoning and spatial localization . Existing methods rely only on SEG>, which encodes semantic reasoning, limiting the model's ability to explicitly disentangle what to segment from where to segment. |
| Approach: | They propose a method which reformulates reasoning segmentation as a structured conditional generation process over image tokens conditioned on language grounded query banks. |
| Outcome: | The proposed model bridges token-level predictions and pixel-level supervision by decoupling spatial grounding from semantic reasoning through structured language grounded query banks. |
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022.findings-naacl)
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Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, Dan Roth
| Challenge: | Large language models have achieved high performance on various natural language benchmarks, but the explainability of their output remains elusive. |
| Approach: | They propose an architecture called iterative retrieval-generation reasoner that generates an entailment tree that explains a given hypothesis by using premises from C. |
| Outcome: | The proposed model outperforms existing benchmarks on premise retrieval and entailment tree generation with around 300% gain in overall correctness. |
LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning (2026.acl-long)
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Zerui Chen, Qinggang Zhang, Zhishang Xiang, Zhimin Wei, Linfeng Gao, Xiao Huang, Zhihong Zhang, Jinsong Su
| Challenge: | Graph-based Retrieval-Augmented Generation (GraphRAG) is a new approach to document retrieval, but it is not suitable for legal reasoning. |
| Approach: | They propose a framework for reliable legal reasoning that structures knowledge as relational graphs and uses a multi-agent system to verify validity. |
| Outcome: | The proposed framework outperforms existing GraphRAG models in accurate and trustworthy legal analysis. |
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey (2025.findings-naacl)
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Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
| Challenge: | Large Language Models (LLMs) have shown great potential to enhance Natural Language Processing (NLP) models in areas such as predictive accuracy, fairness, robustness, and explainability. |
| Approach: | They evaluate or improve generative Large Language Models from a causal perspective in areas such as reasoning capacity, fairness and safety issues, explainability, and handling multimodality. |
| Outcome: | The proposed models can be used to perform causal relationship discovery and causal effect estimation tasks. |
LongHeads: Multi-Head Attention is Secretly a Long Context Processor (2024.findings-emnlp)
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| Challenge: | Large language models struggle to process lengthy inputs due to limited length generalization and attention’s quadratic computational demands. |
| Approach: | They propose a training-free framework that allows each head to attend to important context chunks instead of allowing each head a full sentence . |
| Outcome: | The proposed framework unlocks multi-head attention's untapped potential by allowing each head to attend to important context chunks instead of the full sentence. |
Textual Aesthetics in Large Language Models (2025.emnlp-main)
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| Challenge: | Existing studies on image aesthetics have focused on content correctness and helpfulness of responses. |
| Approach: | They propose a textual aesthetics-powered fine-tuning method that leverages textual visual aesthetics without compromising content correctness. |
| Outcome: | The proposed method improves aesthetic scores and performs well on general evaluation datasets. |
Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt (2022.emnlp-main)
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| Challenge: | Existing work focuses on monolingual prompts, but we study multilingual prompt for multilingual models. |
| Approach: | They propose a model that uses a unified prompt for all languages, called UniPrompt, to alleviate the effort of designing different prompts for multiple languages. |
| Outcome: | The proposed model outperforms baseline models in the zero-shot cross-lingual setting. |
Beyond Quantity: Trajectory Diversity Scaling for Code Agents (2026.findings-acl)
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Guhong Chen, Chenghao Sun, Cheng Fu, Qiyao Wang, Zhihong Huang, ChaoPeng Wei, Guangxu Chen, Feiteng Fang, Ahmadreza Argha, Bing Zhao, Xander Xu, Qi Han, Hamid Alinejad-Rokny, Qiang Qu, Binhua Li, Shiwen Ni, Min Yang, HU Wei, Yongbin Li
| Challenge: | Code large language models (LLMs) are becoming tool-interactive agents . quantity-centric scaling exhibits an early bottleneck that underutilizes trajectory data . et al.: a new approach to scale trajectory diversity improves tool-use generalization . |
| Approach: | They propose a Trajectory Diversity Scaling-based data synthesis framework for code agents that scales performance through diversity rather than raw volume. |
| Outcome: | Experiments on general tool-use benchmarks and code agent tasks show that TDScaling improves tool-user generalization and inherent coding proficiency. |
Making Parameter-efficient Tuning More Efficient: A Unified Framework for Classification Tasks (2022.coling-1)
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Xin Zhou, Ruotian Ma, Yicheng Zou, Xuanting Chen, Tao Gui, Qi Zhang, Xuanjing Huang, Rui Xie, Wei Wu
| Challenge: | Large pre-trained language models (PLMs) have demonstrated superior performance in industrial applications. |
| Approach: | They propose a framework that re-uses existing parameter-efficient methods with a unified classifier. |
| Outcome: | The proposed framework improves the efficiency of existing parameter-efficient methods with a unified classifier. |
LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have been successful in understanding language and processing text, but their cost prohibits their practical applications. |
| Approach: | They propose a multi-agent collaboration method that breaks down lengthy documents into smaller, more manageable chunks and organizes the member agents to read their assigned chunks. |
| Outcome: | The proposed method achieves 16.42% and 1.63% accuracy gains over existing models on single-hop and multi-hop QA settings. |
Do Before You Judge: Self-Reference as a Pathway to Better LLM Evaluation (2025.findings-emnlp)
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| Challenge: | LLM-as-Judge frameworks are increasingly popular for AI evaluation, yet research findings on the relationship between models’ generation and judgment abilities remain inconsistent. |
| Approach: | They propose a self-reference-guided evaluation strategy that leverages a model’s own answers as references to strengthen the correlation between generation and judgment abilities. |
| Outcome: | The proposed approach strengthens the correlation between model generation and judgment abilities and provides a reliable proxy for model selection in evaluation tasks. |
Multi-Agent Simulator Drives Language Models for Legal Intensive Interaction (2025.findings-naacl)
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ShengbinYue ShengbinYue, Ting Huang, Zheng Jia, Siyuan Wang, Shujun Liu, Yun Song, Xuanjing Huang, Zhongyu Wei
| Challenge: | Large Language Models (LLMs) have advanced legal intelligence, but the scarcity of scenario data impedes the progress toward interactive legal scenarios. |
| Approach: | They propose a Multi-agent Legal Simulation Driver to generate synthetic data by simulating interactive legal scenarios. |
| Outcome: | The proposed framework ensures consistency of legal attributes between participants and introduces a supervisory mechanism to align participants’ characters and behaviors as well as addressing distractions. |
A Reinforced Generation of Adversarial Examples for Neural Machine Translation (2020.acl-main)
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| Challenge: | Neural machine translation systems fail on less decent inputs, which may harm the credibility of these systems. |
| Approach: | They propose a paradigm that generates adversarial examples using reinforcement learning to expose pitfalls for a given performance metric. |
| Outcome: | The proposed paradigm produces stable attacks with meaning-preserving adversarial examples. |
DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data (2025.findings-emnlp)
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Yuhang Zhou, Jing Zhu, Shengyi Qian, Zhuokai Zhao, Xiyao Wang, Xiaoyu Liu, Ming Li, Paiheng Xu, Wei Ai, Furong Huang
| Challenge: | Large Language Models (LLMs) are increasingly aligned with human preferences through Reinforcement Learning from Human Feedback (RLHF). |
| Approach: | a new study proposes a domain-informed self-consistency policy optimization extension to GRPO that addresses inter-group imbalance. |
| Outcome: | a new extension of GRPO addresses inter-group imbalance with two key innovations . the proposed method outperforms existing GR PO variants by 5% on Qwen3 models . |
VFA: Empowering Multilingual MLLMs via Vision-Free Adaptation (2026.acl-long)
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Yixia Li, Yaqing Shi, Zhiwen Ruan, Dongdong Zhang, Lingjie Jiang, Shaohan Huang, Yun Chen, Guanhua Chen, Furu Wei
| Challenge: | Multimodal large language models have advanced rapidly, yet most remain English-centric . scaling multilingual multimodal instruction tuning is limited by the scarcity and high cost of non-English image–text supervision. |
| Approach: | They propose a framework that decouples multilingual language enhancement from visual alignment by composing complementary task vectors over a shared LLM backbone. |
| Outcome: | The proposed framework achieves competitive performance with a fully multimodally trained model using less than 2% of the text data. |
CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation (2022.acl-long)
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| Challenge: | Current models can not ensure the complexity of generated questions, so they may generate shallow questions that can be answered without multi-hop reasoning. |
| Approach: | They propose a controlled framework to generate multi-hop questions that contain key entities in multi- hop reasoning chains and a novel Transformer-based decoder to guarantee that key entities appear in the questions. |
| Outcome: | The proposed model outperforms the state-of-the-art model 25% on HotpotQA. |
Mask Attention Networks: Rethinking and Strengthen Transformer (2021.naacl-main)
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Zhihao Fan, Yeyun Gong, Dayiheng Liu, Zhongyu Wei, Siyuan Wang, Jian Jiao, Nan Duan, Ruofei Zhang, Xuanjing Huang
| Challenge: | Existing research explores to enhance the two sublayers separately to improve the capability of Transformer for text representation. |
| Approach: | They propose to combine SAN and Feed-Forward Networks to create a dynamic mask attention network with a learnable mask matrix which can model localness adaptively. |
| Outcome: | The proposed model outperforms the original Transformer on translation and text summarization tasks. |
Breaking the Evaluation Paradox: Evaluating High-Entropy Search with Computationally Irreducible Constraints (2026.findings-acl)
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| Challenge: | a new framework for evaluation of exhaustive search capabilities is needed . high-entropy enumeration tasks make such ground truth impossible for humans to create . VERITAS is a framework built on the principle of computationally irreducible constraints . |
| Approach: | They propose a framework that uses non-optimizable constraints to create verifiable searches . VERITAS can generate infinite number of test cases with perfect ground truth and precise difficulty control . |
| Outcome: | a new evaluation framework for large language models is based on non-optimizable constraints . the framework can generate infinite number of test cases with perfect ground truth and precise difficulty control . |
Cross-Modal Masked Compositional Concept Modeling for Enhancing Visio-Linguistic Compositionality (2026.acl-long)
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| Challenge: | a contrastive learning approach for vision-language models is needed to capture compositional information. |
| Approach: | They propose a framework that masks compositional concepts in one modality and reconstructs them conditioned on full contextual information from the other . |
| Outcome: | The proposed framework enhances compositionality in visual language models and improves their ability to capture syntactic structure and linguistic information. |
PEER: Pre-training ELECTRA Extended by Ranking (2023.findings-acl)
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| Challenge: | Existing models for pre-training require expensive pre-trainer computation cost . ELECTRA model can perform replaced token detection (RTD) task with reduced pre- training cost compared to current models . |
| Approach: | They propose to extend a discriminator-based replaced token detection task into a ranker-based task . they propose to use a binary classifier to perform a more precise task with negligible additional computation cost. |
| Outcome: | The proposed model outperforms state-of-the-art models with ELECTRA in GLUE tasks given the same cost. |
EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation (2025.findings-emnlp)
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| Challenge: | Large language models (LLMs) have demonstrated impressive ability to role-play humans and replicate complex social dynamics. |
| Approach: | They propose an efficient agent communication language induction for social simulations that reduces token consumption by over 20%. |
| Outcome: | The proposed model reduces token consumption by over 20% while preserving human language. |
Pre-training Language Model as a Multi-perspective Course Learner (2023.findings-acl)
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Beiduo Chen, Shaohan Huang, Zihan Zhang, Wu Guo, Zhenhua Ling, Haizhen Huang, Furu Wei, Weiwei Deng, Qi Zhang
| Challenge: | Experimental results show that our method significantly improves ELECTRA’s average performance by 2.8% and 3.2% absolute points respectively on GLUE and SQuAD 2.0 benchmarks. |
| Approach: | They propose a multi-perspective course learning method to fetch many degrees and visual angles for sample-efficient pre-training and to fully leverage the relationship between generator and discriminator. |
| Outcome: | The proposed method improves ELECTRA's performance on GLUE and SQuAD 2.0 benchmarks and overshadows recent advanced ELECL-style models under the same settings. |
Joint Enhancement of Relational Reasoning for Long-Context LLMs (2025.findings-emnlp)
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| Challenge: | JERR is a graph-based reasoning framework for large language models . it enables LLMs to handle extended contexts with improved reliability and transparency . |
| Approach: | They propose a graph-based reasoning framework that integrates synopsis extraction, graph construction, and relational reasoning. |
| Outcome: | The proposed framework outperforms baselines on ROUGE and F1 metrics and achieves the highest scores on the LLM-Rater evaluation. |
Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search (2023.emnlp-main)
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Xiang Geng, Yu Zhang, Zhejian Lai, Shuaijie She, Wei Zou, Shimin Tao, Hao Yang, Jiajun Chen, Shujian Huang
| Challenge: | evaluating the quality of machine translation outputs becomes increasingly essential with the rapid development of machine language (MT). |
| Approach: | They propose to generate pseudo data using the MT model with constrained beam search (CBSQE) they propose to preserve the reference parts with high MT probabilities as correct translations . |
| Outcome: | The proposed model outperforms strong baselines in both supervised and unsupervised settings. |
MECoT: Markov Emotional Chain-of-Thought for Personality-Consistent Role-Playing (2025.findings-acl)
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| Challenge: | Existing Large Language Models struggle to maintain emotionally consistent and psychologically plausible character personalities. |
| Approach: | They propose a framework that enhances LLMs’ ability to generate authentic personality-driven dialogues through stochastic emotional transitions. |
| Outcome: | The proposed framework achieves 93.3% emotional accuracy on the RAPD dataset and significantly outperforms existing approaches. |
Stephanie: Step-by-Step Dialogues for Mimicking Human Interactions in Social Conversations (2025.findings-naacl)
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Hao Yang, Hongyuan Lu, Xinhua Zeng, Yang Liu, Xiang Zhang, Haoran Yang, Yumeng Zhang, Shan Huang, Yiran Wei, Wai Lam
| Challenge: | a new paradigm for dialogue systems is being developed to mimic human interactions . the current single-step dialogue paradigm lacks the depth and fluidity of human interactions. |
| Approach: | They propose a step-by-step dialogue paradigm that mimics human interactions . they use a dataset to fine-tune existing language models . |
| Outcome: | The proposed system mimics the dynamic nature of human conversations . it is compared with existing paradigms and will be released later this year . |
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation (2022.lrec-1)
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Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen
| Challenge: | Recent research on domain adaptation neglects diversity in translation within a domain . current research on NMT models considers very broad target domains . |
| Approach: | They propose a fine-grained domain adaptation task for autonomous vehicles, AI education, real-time networks, and smart phone. |
| Outcome: | The proposed task is compared with a dataset of Chinese-English translation tasks for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. |
Addressing Semantic Drift in Generative Question Answering with Auxiliary Extraction (2021.acl-short)
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| Challenge: | Recent work focuses on question answering based on machine reading comprehension . current approaches treat QA as extracting a consecutive piece of text to a given question. |
| Approach: | They propose a generative QA model that incorporates an extractive mechanism into a model. |
| Outcome: | The proposed model improves quality and semantic accuracy over baseline models. |
Cross-Document Cross-Lingual NLI via RST-Enhanced Graph Fusion and Interpretability Prediction (2025.emnlp-main)
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| Challenge: | Despite the development of many subdirections, Cross-Document Cross-Lingual NLI remains largely unexplored. |
| Approach: | They propose a novel paradigm that extends traditional NLI capabilities to multi-document, multilingual scenarios by integrating RST-enhanced graph fusion with interpretability-aware prediction. |
| Outcome: | The proposed method improves on existing models and document-level NLI to multi-document, multilingual scenarios. |
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers (2022.coling-1)
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Bowen Qin, Lihan Wang, Binyuan Hui, Bowen Li, Xiangpeng Wei, Binhua Li, Fei Huang, Luo Si, Min Yang, Yongbin Li
| Challenge: | Existing methods that learn from multiple semantically-equivalent questions are limited to one-to-one mapping . |
| Approach: | They propose a constraint to explore the underlying complementary semantic information among multiple semantically-equivalent questions and learn robust feature representations with reduced spurious associations. |
| Outcome: | The proposed method outperforms strong competitors and achieves state-of-the-art results on five benchmark datasets. |
AI-Press: A Multi-Agent News Generating and Feedback Simulation System Powered by Large Language Models (2025.coling-demos)
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Xiawei Liu, Shiyue Yang, Xinnong Zhang, Haoyu Kuang, Libo Sun, Yihang Yang, Siming Chen, Xuanjing Huang, Zhongyu Wei
| Challenge: | We introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation. |
| Approach: | They introduce AI-Press, an automated news drafting and polishing system based on multi-agent collaboration and Retrieval-Augmented Generation. |
| Outcome: | The proposed system generates public responses considering demographic distributions. |
A Structure-Aware Argument Encoder for Literature Discourse Analysis (2022.coling-1)
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Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu
| Challenge: | Existing research for argument representation learning treats tokens in sentences equally and ignores the implied structure information of argumentative context. |
| Approach: | They propose to separate tokens into two groups to capture structural information of arguments and to incorporate paragraph-level position information into the model. |
| Outcome: | The proposed model captures structural information of arguments and is able to identify arguments automatically. |
EchoVLM: Dynamic Mixture-of-Experts Vision-Language Model for Universal Ultrasound Intelligence (2026.acl-long)
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| Challenge: | Ultrasound is the preferred early cancer screening modality due to non-ionizing radiation, cost-effectiveness, and real-time imaging. |
| Approach: | They propose to use ultrasound-tailored vision-language models with a mixture-of-experts architecture to train ultrasound-specific knowledge across seven anatomical systems. |
| Outcome: | The proposed model outperforms Qwen2-VL by 7.58 BLEU-1 and 3.45 ROUGE-1 points in report generation. |
LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement (2024.findings-emnlp)
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| Challenge: | Using advanced Large Language Models, instructors can improve training of smaller models by analyzing their own model's errors. |
| Approach: | They propose a framework that leverages advanced Large Language Models to enhance training of smaller target models. |
| Outcome: | The proposed framework outperforms ChatGPT on multiple benchmarks and shows that it improves on both in-domain and out-of-domain benchmarks. |
Pretraining Context Compressor for Large Language Models with Embedding-Based Memory (2025.acl-long)
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| Challenge: | Efficient processing of long contexts in large language models is essential for real-world applications such as retrieval-augmented generation and in-context learning. |
| Approach: | They propose a decoupled compressor-LLM framework that preserves contextual information within condensed embedding representations. |
| Outcome: | The proposed framework outperforms baseline models in three domains and across eight datasets while adapting to different downstream LLMs. |
Finding the Evidence: Localization-aware Answer Prediction for Text Visual Question Answering (2020.coling-main)
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| Challenge: | Existing text VQA systems generate an answer by selecting from optical character recognition (OCR) texts or a fixed vocabulary. |
| Approach: | They propose a localization-aware answer prediction network that generates the answer and predicts a bounding box as evidence of the generated answer. |
| Outcome: | The proposed network outperforms existing methods on three benchmark datasets for the text VQA task by a noticeable margin. |
Does Chain-of-Thought Reasoning Really Reduce Harmfulness from Jailbreaking? (2025.findings-acl)
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| Challenge: | Existing jailbreak attacks fail against reasoning models enhanced by Chain-of-Thought (CoT) reasoning. |
| Approach: | They propose a jailbreak method that uses Chain-of-Thought reasoning to reduce harmfulness from jailbreaking. |
| Outcome: | The proposed jailbreak method performs well against open AI models and deepseek-R1 reasoning models. |
Revisiting Entropy in Reinforcement Learning for Large Reasoning Models (2026.findings-acl)
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Renren Jin, Pengzhi Gao, Yuqi Ren, Zhuowen Han, Tongxuan Zhang, Wuwei Huang, Wei Liu, Jian Luan, Deyi Xiong
| Challenge: | Reinforcement learning with verifiable rewards (RLVR) has emerged as a paradigm for enhancing the reasoning capabilities of large language models. |
| Approach: | They propose a positive-advantage reweighting approach that regulates model entropy by adjusting the loss weights assigned to tokens with positive advantages during RLVR training. |
| Outcome: | The proposed approach regulates model entropy by adjusting loss weights assigned to tokens with positive advantages during RLVR training while maintaining competitive performance. |
Latent Distribution Decouple for Uncertain-Aware Multimodal Multi-label Emotion Recognition (2025.findings-acl)
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Jingwang Huang, Jiang Zhong, Qin Lei, Gaojinpeng Gaojinpeng, Ymyang Ymyang, Sirui Wang, PeiguangLi PeiguangLi, Kaiwen Wei
| Challenge: | Existing studies focus on improving fusion strategies and modeling modality-to-label dependencies, but they overlook the impact of aleatoric uncertainty, which is inherent noise in multimodal data. |
| Approach: | They propose a latent emotional distribution decomposition with uncertainty perception framework to model aleatoric uncertainty in multimodal data. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the CMU-MOSEI and M3ED datasets, highlighting the importance of uncertainty modeling in MMER. |
Improve Speech Translation Through Text Rewrite (2025.coling-industry)
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| Challenge: | Recent advances in speech translation (ST) research have focused on the unique characteristics of spontaneous speech, including accents and presentation quality. |
| Approach: | They propose to transform transcribed speech into a cleaner style more in line with the expectations of translation models built from written text. |
| Outcome: | Experiments on public and in-house translation models show that the proposed model can be effectively distilled into a standalone translation model. |
PlugAT: A Plug and Play Module to Defend against Textual Adversarial Attack (2022.coling-1)
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| Challenge: | Recent studies have shown that adversarial examples can be easily fooled by adversarially perturbed examples. |
| Approach: | They propose a pluggable defense module PlugAT to provide robust predictions by adding a few trainable parameters to the model inputs while keeping the original model frozen. |
| Outcome: | The proposed model improves robustness over several strong baselines whilst training only 9.1% parameters. |
P-MMEval: A Parallel Multilingual Multitask Benchmark for Consistent Evaluation of LLMs (2025.emnlp-main)
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Yidan Zhang, Yu Wan, Boyi Deng, Baosong Yang, Hao-Ran Wei, Fei Huang, Bowen Yu, Dayiheng Liu, Junyang Lin, Fei Huang, Jingren Zhou
| Challenge: | Recent advances in large language models showcase varied multilingual capabilities across tasks . previous assessments focused on fundamental natural language processing (NLP) or isolated capability-specific tasks. |
| Approach: | They propose a multilingual multitask benchmark to assess multilingual capabilities . they use a large-scale benchmark covering fundamental and capability-specialized datasets . |
| Outcome: | The proposed benchmark compares models and tasks across languages and tasks and examines knowledge transfer from English to other languages. |
Detecting Adversarial Samples through Sharpness of Loss Landscape (2023.findings-acl)
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Rui Zheng, Shihan Dou, Yuhao Zhou, Qin Liu, Tao Gui, Qi Zhang, Zhongyu Wei, Xuanjing Huang, Menghan Zhang
| Challenge: | Existing studies have shown that adversarial samples are more vulnerable than normal ones to textual adversarials. |
| Approach: | They propose a simple and effective sharpness-based detector that can distinguish adversarial samples by maximizing the loss increment within the region where the inference sample is located. |
| Outcome: | The proposed method outperforms previous detection methods by large margins on three text classification tasks. |
Toward Recognizing More Entity Types in NER: An Efficient Implementation using Only Entity Lexicons (2020.findings-emnlp)
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| Challenge: | Existing named entity recognition systems require large scale labeled data to perform, while annotation of NER data is laborious and time-consuming. |
| Approach: | They propose to adjust an existing named entity recognition system to recognize entity types not defined in the system. |
| Outcome: | The proposed method can be quickly adjusted to a named entity recognition system. |
Data Augmentation for Text Generation Without Any Augmented Data (2021.acl-long)
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| Challenge: | Existing methods for data augmentation need to define or choose proper data mapping functions to create augmented samples. |
| Approach: | They propose to use data mapping functions to augment text samples without using specific mapping functions. |
| Outcome: | The proposed approach can approximate or even surpass popular data augmentation methods on two text generation tasks with a convergence rate guarantee. |
MARK: Multi-agent Collaboration with Ranking Guidance for Text-attributed Graph Clustering (2025.findings-acl)
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Yiwei Fu, Yuxing Zhang, Chunchun Chen, JianwenMa JianwenMa, Quan Yuan, Rong-Cheng Tu, Xinli Huang, Wei Ye, Xiao Luo, Minghua Deng
| Challenge: | Existing approaches to cluster graphs with GNNs are limited due to label scarcity. |
| Approach: | They propose to leverage large language models to enhance text-attributed graph clustering by using three LLMs as ranking-based supervision signals. |
| Outcome: | The proposed approach generates reliable guidance using collaboration of three LLM-based agents as ranking-based supervision signals. |
A Simple Hash-Based Early Exiting Approach For Language Understanding and Generation (2022.findings-acl)
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Tianxiang Sun, Xiangyang Liu, Wei Zhu, Zhichao Geng, Lingling Wu, Yilong He, Yuan Ni, Guotong Xie, Xuanjing Huang, Xipeng Qiu
| Challenge: | Existing methods to measure instance difficulty use generalization and threshold-tuning . a new approach to learn to exit is based on hash functions to assign tokens to a fixed exiting layer. |
| Approach: | They propose a Hash-based Early Exiting approach that replaces learn-to-exit modules with hash functions to assign each token to a fixed exiting layer. |
| Outcome: | The proposed approach improves on learning to exit and predicting instance difficulty. |
Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining (2023.emnlp-main)
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Jingcong Liang, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei
| Challenge: | Recent studies have discussed its capability to assist language models for various applications. |
| Approach: | They propose a structure to organize arguments using the **Hi**erarchical **Ar**gumentation **G**raph (Hi-ArG) and propose two approaches to exploit Hi-AarG, including a text-graph multi-modal model GreaseArR and a framework augmented with graph information. |
| Outcome: | The proposed structure supersedes existing language models on two argumentation tasks while incorporating graph information during further training improves vanilla language models. |
Typos Correction Training against Misspellings from Text-to-Text Transformers (2024.lrec-main)
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| Challenge: | Existing dense retrieval systems suffer from typoed queries due to mistyping or phonetic typing errors. |
| Approach: | They propose a method that incorporates the spelling correction objective into the DR model and a prompt-based augmentation technique to enhance the alignment of the typoed query and its original query. |
| Outcome: | The proposed model outperforms existing typos-aware training approaches and sophisticated training advanced retrievers. |
Task-oriented Dialogue System for Automatic Diagnosis (P18-2)
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Zhongyu Wei, Qianlong Liu, Baolin Peng, Huaixiao Tou, Ting Chen, Xuanjing Huang, Kam-fai Wong, Xiangying Dai
| Challenge: | Existing methods to identify phenotypes using electronic health records (EHRs) are expensive and difficult to transfer models from one disease to another. |
| Approach: | They propose a task-oriented dialogue system framework to make diagnosis for patients automatically, which can converse with patients to collect additional symptoms beyond their self-reports. |
| Outcome: | The proposed system can collect additional symptoms from conversation and improve disease identification accuracy. |
A Reinforcement Learning Framework for Natural Question Generation using Bi-discriminators (C18-1)
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| Challenge: | Existing research on visual question generation is focused on training models to fit the annotated data set that makes them indifferent from other language generation tasks. |
| Approach: | They propose to use two discriminators to enhance the training of a visual question generator to ask natural questions about an image. |
| Outcome: | The proposed model outperforms state-of-the-art models in terms of automatic and human evaluation metrics. |
DocBank: A Benchmark Dataset for Document Layout Analysis (2020.coling-main)
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| Challenge: | Existing approaches for document layout analysis are based on rule-based or machine learning methods that ignore textual information. |
| Approach: | They present a benchmark document layout analysis dataset using a computer vision model . they build strong baselines and manually split train/dev/test sets for evaluation . |
| Outcome: | The proposed model trains on DocBank accurately recognize layout information for a variety of documents. |
Are LLMs Good Annotators for Discourse-level Event Relation Extraction? (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) have demonstrated proficiency in a wide array of natural language processing tasks, but their effectiveness over discourse-level event relation extraction tasks remains unexplored. |
| Approach: | They evaluate LLMs' ability to address discourse-level event relation extraction tasks using an open-source model and a commercial model. |
| Outcome: | The proposed model performs poorly on discourse-level event relation extraction tasks. |
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. |
Connectivity Patterns are Task Embeddings (2023.findings-acl)
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Zhiheng Xi, Rui Zheng, Yuansen Zhang, Xuanjing Huang, Zhongyu Wei, Minlong Peng, Mingming Sun, Qi Zhang, Tao Gui
| Challenge: | Existing methods for predicting inter-task transferability are sparse and task-specific. |
| Approach: | They propose a method that uses connectivity patterns of neurons as a unique identifier associated with a task. |
| Outcome: | The proposed method outperforms baselines in predicting inter-task transferability across data regimes and transfer settings while keeping high efficiency in computation and storage. |
Task-oriented Word Embedding for Text Classification (C18-1)
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| Challenge: | Existing word embeddings only consider contextual information, which is suboptimal when used in various tasks due to a lack of task-specific features. |
| Approach: | They propose a task-oriented word embedding method that regularizes the distribution of words to enable a clear classification boundary. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on a text classification task. |
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods to solve complex logical reasoning problems are cumbersome for language models. |
| Approach: | They propose to use iterative methodology to construct a cognitive tree using language models . they propose to generate multiple responses by utilizing in-context examples . |
| Outcome: | The proposed model achieves a performance level comparable to that of GPT-3.5 . the proposed model contains fewer parameters than 5% of the model with 175B parameters . |
Capturing Global Structural Information in Long Document Question Answering with Compressive Graph Selector Network (2022.emnlp-main)
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| Challenge: | Existing methods to answer long document questions ignore the global structure of the long document, which is essential for long-range understanding. |
| Approach: | They propose a Compressive Graph Selector Network to capture the global structure of the long document in a compressive and iterative manner. |
| Outcome: | The proposed model outperforms existing methods on two datasets. |
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration (2023.emnlp-main)
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| Challenge: | Existing data for instruction-tuning are inadequate for a wide range of tasks, limiting the scope for nuanced comprehension and interactions within these domains. |
| Approach: | They propose to use Large Language Models to explore a multitude of variations or possibilities to improve instruction-tuning data by active exploration. |
| Outcome: | The proposed approach improves domain-specific instruction coverage and shows significant improvements over baselines. |
Reward Modeling Requires Automatic Adjustment Based on Data Quality (2024.findings-emnlp)
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Binghai Wang, Rui Zheng, Lu Chen, Zhiheng Xi, Wei Shen, Yuhao Zhou, Dong Yan, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Reinforcement Learning from Human Feedback (RLHF) is a method for aligning language models with human values. |
| Approach: | They propose a method that automatically adjusts reward modeling based on data quality . they use preference data to train a reward model that is more aligned with human values . |
| Outcome: | The proposed method stabilizes reward model training and significantly improves alignment performance on human preference datasets. |
One-Model-Connects-All: A Unified Graph Pre-Training Model for Online Community Modeling (2023.findings-emnlp)
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Ruoxue Ma, Jiarong Xu, Xinnong Zhang, Haozhe Zhang, Zuyu Zhao, Qi Zhang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Existing studies on integrating online community to solve social problems have not fully utilized these three components and the relationship among them. |
| Approach: | They propose a framework that simultaneously considers communities, users, and texts and can easily connect with a variety of downstream tasks related to social media. |
| Outcome: | The proposed model can be used to perform violation detection, sentiment analysis, and community recommendation across multiple tasks. |
Local Interpretation of Transformer Based on Linear Decomposition (2023.acl-long)
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| Challenge: | Existing work on local explanation generation attempts to understand model dynamics on word-level or phraselevel by assigning importance scores on input features. |
| Approach: | They propose to interpret neural networks by linear decomposition by a Transformer model on a single input and a linear decomposing of the output to generate local explanations. |
| Outcome: | The proposed method achieves competitive performance in sentiment classification and machine translation, and fidelity of explanation. |
Open Set Relation Extraction via Unknown-Aware Training (2023.acl-long)
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Jun Zhao, Xin Zhao, WenYu Zhan, Qi Zhang, Tao Gui, Zhongyu Wei, Yun Wen Chen, Xiang Gao, Xuanjing Huang
| Challenge: | Existing supervised relation extraction methods can still misclassify unknown relations into known relations due to the lack of supervision signals. |
| Approach: | They propose a method that regularizes the model by dynamically synthesizing negative instances that can provide the missing supervision signals. |
| Outcome: | The proposed method achieves SOTA unknown relation detection without compromising the classification of known relations. |
Discrete Argument Representation Learning for Interactive Argument Pair Identification (2021.naacl-main)
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| Challenge: | Existing research on monological argumentation covers claims generation, argument structure prediction, and essay scoring. |
| Approach: | They propose to identify argument pairs from two posts with opposite stances to a certain topic. |
| Outcome: | The proposed framework outperforms competing models on a large-scale dataset . it also proves that it is useful for analyzing argument pairs from two posts . |
Jailbreak LLMs through Internal Stance Manipulation (2025.emnlp-main)
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| Challenge: | Existing approaches to exploit LLMs' inherent safety mechanism, including GCG and AutoDAN, are ineffective for certain malicious requests. |
| Approach: | They propose a method that generates jailbreak prompts to suppress a refusal stance and induce affirmative responses by modifying adversarial prompts. |
| Outcome: | The proposed method outperforms the best baseline approach in Llama-2-7b-chat and achieves a 92.2% success rate across all models. |
AutoSchemaKG: Autonomous Knowledge Graph Construction through Dynamic Schema Induction from Web-Scale Corpora (2026.acl-long)
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Jiaxin Bai, Wei Fan, Qi Hu, Qing Zong, Chunyang Li, Hong Ting Tsang, Hongyu Luo, Yauwai Yim, Haoyu Huang, Xiao Zhou, Feng Qin, Tianshi Zheng, Xi Peng, Xin Yao, Huiwen Yang, Leijie Wu, JI Yi, Gong Zhang, Renhai Chen, Yangqiu Song
| Challenge: | Existing knowledge graph construction frameworks require predefined schemas, limiting their scalability and domain coverage. |
| Approach: | They propose a framework for fully autonomous knowledge graph construction that eliminates the need for predefined schemas. |
| Outcome: | The proposed framework outperforms state-of-the-art models on multi-hop QA tasks and enhances LLM factuality. |
SoMeLVLM: A Large Vision Language Model for Social Media Processing (2024.findings-acl)
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Xinnong Zhang, Haoyu Kuang, Xinyi Mou, Hanjia Lyu, Kun Wu, Siming Chen, Jiebo Luo, Xuanjing Huang, Zhongyu Wei
| Challenge: | Genereal domain large models lack nuanced multimodal understanding of social media . general domain models focus more on text than other modalities, which is not consistent with real-world user habits. |
| Approach: | They propose a Large Vision Language Model for Social Media Processing that combines five key capabilities to understand and generate real social media behavior. |
| Outcome: | The proposed model achieves state-of-the-art performance in multiple social media tasks. |
DatawiseAgent: A Notebook-Centric LLM Agent Framework for Adaptive and Robust Data Science Automation (2025.emnlp-main)
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| Challenge: | Existing large language model (LLM) agents for data science automation are limited by narrow task scopes, limited generalization across tasks and models, and over-reliance on state-of-the-art (SOTA) LLMs. |
| Approach: | They propose a notebook-centric LLM agent framework for adaptive and robust data science automation. |
| Outcome: | The proposed framework surpasses baselines such as AutoGen and TaskWeaver in performance tests across diverse data science scenarios and models. |
NL2Lean: Translating Natural Language into Lean 4 through Multi-Aspect Reinforcement Learning (2025.emnlp-main)
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Yue Fang, Shaohan Huang, Xin Yu, Haizhen Huang, Zihan Zhang, Weiwei Deng, Furu Wei, Feng Sun, Qi Zhang, Zhi Jin
| Challenge: | Existing formal proof assistants rely on instruction tuning and lack fine-grained structural and semantic alignment. |
| Approach: | They propose a reinforcement learning framework that enables LLMs to translate natural language into formal language such as Lean 4 . they use a model with basic translation ability to refine the model's reinforcement learning . |
| Outcome: | The proposed method outperforms baseline models on NL-to-Lean 4 tasks. |
Bridging by Word: Image Grounded Vocabulary Construction for Visual Captioning (P19-1)
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| Challenge: | Existing research on image captioning generates frequent n-grams with irrelevant words. |
| Approach: | They propose to construct an image-grounded vocabulary incorporating visual information and relations among words into the decoding process directly. |
| Outcome: | The proposed framework is compared with state-of-the-art models on MS COCO and Flickr30k and shows that it is more efficient than existing models. |
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion (2023.findings-emnlp)
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| Challenge: | Existing knowledge graph completion methods struggle with long-tail entities due to limited structural information and imbalanced distributions of entities. |
| Approach: | They propose a framework that integrates a large language model and a triple-based KGC retriever to alleviate the long-tail problem without incurring additional training overhead. |
| Outcome: | The proposed model reduces training overhead and finetuning costs on benchmark datasets. |
K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters (2021.findings-acl)
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Ruize Wang, Duyu Tang, Nan Duan, Zhongyu Wei, Xuanjing Huang, Jianshu Ji, Guihong Cao, Daxin Jiang, Ming Zhou
| Challenge: | Existing methods for injecting knowledge into pre-trained models are inconsistent and can flush out knowledge when multiple kinds of knowledge are injected. |
| Approach: | They propose a framework that retains the original parameters of pre-trained models fixed and supports the development of versatile knowledge-infused models. |
| Outcome: | The proposed framework retains the original parameters of the pre-trained model fixed and supports the development of versatile knowledge-infused models. |
Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification (2022.coling-1)
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| Challenge: | Existing methods for hierarchical text classification are lacking in the field of natural language processing. |
| Approach: | They propose a hierarchy-aware T5 model with path-adaptive attention mechanism to exploit hierarchical dependency across different levels. |
| Outcome: | The proposed model outperforms state-of-the-art models especially in Macro-F1 and low Macro. |
FEA-Bench: A Benchmark for Evaluating Repository-Level Code Generation for Feature Implementation (2025.acl-long)
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Wei Li, Xin Zhang, Zhongxin Guo, Shaoguang Mao, Wen Luo, Guangyue Peng, Yangyu Huang, Houfeng Wang, Scarlett Li
| Challenge: | Existing benchmarks focus on standalone programming problems, such as HumanEval, MBPP, and LiveCodeBench. |
| Approach: | They propose to use large language models to evaluate their ability to perform incremental development within code repositories by collecting pull requests from 83 GitHub repositorias and using rule-based and intent-based filtering to construct task instances focused on new feature development. |
| Outcome: | The proposed benchmarks show that large language models perform significantly worse in the FEA-Bench, highlighting considerable challenges in repository-level incremental code development. |
THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption (2022.findings-acl)
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Tianyu Chen, Hangbo Bao, Shaohan Huang, Li Dong, Binxing Jiao, Daxin Jiang, Haoyi Zhou, Jianxin Li, Furu Wei
| Challenge: | enabling pre-trained models inference on ciphertext data is difficult due to the complex computations in transformer blocks. |
| Approach: | They propose an approximation approach for transformers which enables inference on ciphertext data. |
| Outcome: | The proposed approach can infer pre-trained models on encrypted data with negligible performance drop but enjoy theory-guaranteed privacy-preserving advantage. |
PromptBERT: Improving BERT Sentence Embeddings with Prompts (2022.emnlp-main)
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Ting Jiang, Jian Jiao, Shaohan Huang, Zihan Zhang, Deqing Wang, Fuzhen Zhuang, Furu Wei, Haizhen Huang, Denvy Deng, Qi Zhang
| Challenge: | Existing research shows that BERT and RoBERTa are poor in sentence embeddings due to static token embeddable bias and ineffective BERT layers. |
| Approach: | They propose a novel contrastive learning method for better sentence embeddings by using a template denoising technique. |
| Outcome: | The proposed method achieves 2.29 and 2.58 points of improvement compared to SimCSE and RoBERTa in the unsupervised setting. |
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing (2022.emnlp-demos)
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Chengyu Wang, Minghui Qiu, Taolin Zhang, Tingting Liu, Lei Li, Jianing Wang, Ming Wang, Jun Huang, Wei Lin
| Challenge: | Pre-Trained Models (PTMs) have reshaped the development of natural language processing (NLP) but it is not easy to obtain high-performing PTMs without a large amount of labeled training data and deploy them online with fast inference speed. |
| Approach: | They propose to make it easy to build NLP applications with knowledge-enhanced pre-training and knowledge distillation. |
| Outcome: | EasyNLP supports a comprehensive suite of NLP algorithms and features knowledge-enhanced pre-training, knowledge distillation and few-shot learning functionalities. |
Pre-training Multi-party Dialogue Models with Latent Discourse Inference (2023.acl-long)
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| Challenge: | Existing studies have failed to scale up the pre-training process by putting aside unlabeled data . et al., 2019: multi-party dialogues are more difficult for models to understand since they involve multiple interlocutors resulting in interweaving reply-to relations and information flows. |
| Approach: | They propose to treat discourse structures as latent variables and jointly infer them to pre-train a model that understands the discourse structure of multi-party dialogues. |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art results on multiple downstream tasks. |
Large Language and Protein Assistant for Protein-Protein Interactions Prediction (2025.acl-long)
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Peng Zhou, Pengsen Ma, Jianmin Wang, Xibao Cai, Haitao Huang, Wei Liu, Longyue Wang, Lai Hou Tim, Xiangxiang Zeng
| Challenge: | Existing methods for predicting protein-protein interactions oversimplify the problem of PPI prediction in a semi-supervised manner. |
| Approach: | They propose a multimodal large language model that integrates proteins and PPI networks. |
| Outcome: | Experiments show that LLaPA can predict protein-protein interactions (mPPI) types and affinities based on sequence data. |
DIAG-NRE: A Neural Pattern Diagnosis Framework for Distantly Supervised Neural Relation Extraction (P19-1)
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| Challenge: | Existing methods for labeling relational facts require significant expert labor to write relation-specific patterns, which makes them too sophisticated to generalize quickly. |
| Approach: | They propose a neural pattern diagnosis framework that can summarize and refine relation-specific patterns with human experts in the loop. |
| Outcome: | The proposed framework can summarize and refine high-quality relational patterns from noise data with human experts in the loop. |
MMLU-CF: A Contamination-free Multi-task Language Understanding Benchmark (2025.acl-long)
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Qihao Zhao, Yangyu Huang, Tengchao Lv, Lei Cui, Qinzheng Sun, Shaoguang Mao, Xin Zhang, Ying Xin, Qiufeng Yin, Scarlett Li, Furu Wei
| Challenge: | Multiple-choice question datasets like Massive Multitask Language Understanding (MMLU) have inevitably led to benchmark contamination, resulting in unreliable evaluation. |
| Approach: | They propose a contamination-free MCQ benchmark called MMLU-CF which reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination. |
| Outcome: | The proposed MMLU-CF reassesses LLMs’ understanding of world knowledge by averting both unintentional and malicious data contamination. |
AIR: Complex Instruction Generation via Automatic Iterative Refinement (2025.emnlp-main)
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| Challenge: | Existing methods for generating complex instructions are resource-intensive and lack diversity. |
| Approach: | They propose a framework to generate complex instructions with constraints using a document-generated initial instruction and an iterative refinement framework to incorporate LLM-as-judge guidance. |
| Outcome: | The proposed framework significantly outperforms existing methods for generating complex instructions, and outperformed existing methods. |
InCharacter: Evaluating Personality Fidelity in Role-Playing Agents through Psychological Interviews (2024.acl-long)
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Xintao Wang, Yunze Xiao, Jen-tse Huang, Siyu Yuan, Rui Xu, Haoran Guo, Quan Tu, Yaying Fei, Ziang Leng, Wei Wang, Jiangjie Chen, Cheng Li, Yanghua Xiao
| Challenge: | Existing methods focus on knowledge and linguistic patterns of characters. |
| Approach: | They propose to evaluate character fidelity of role-playing agents with psychological scales . they propose to use psychological scale to measure personality traits of RPAs based on personality traits. |
| Outcome: | The proposed model reproduces character fidelity with psychological scales and shows that it is effective in measuring personality traits. |
Self-Reflection Improves Safety of Large Reasoning Models (2026.findings-acl)
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| Challenge: | Existing safety alignment methods are shallow and do not address deeper risks and attacks in reasoning processes. |
| Approach: | They propose a technique that introduces a special Self-Reflection token to enable LRMs to perform self-reflection during generation and recover from harmful outputs. |
| Outcome: | The proposed approach outperforms the baseline model in terms of safety and helpfulness, and significantly improves model safety without adversarial training. |
SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters (2025.naacl-long)
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| Challenge: | Existing studies have focused on the potential misuse of large language models (LLMs) however, the ability to align LLMs with human values is still vulnerable to malicious attacks. |
| Approach: | They propose a red-teaming strategy to enhance LLM safety by using a framework to design jailbreak prompts automatically. |
| Outcome: | The proposed framework achieves attack success rates of 88% and 60% in cold-start scenarios. |
Cross-lingual Prompting: Improving Zero-shot Chain-of-Thought Reasoning across Languages (2023.emnlp-main)
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| Challenge: | Existing methods for zero-shot CoT are limited to a single language, making it difficult to generalize to other languages and hindering global development. |
| Approach: | They introduce cross-lingual prompting (CLP) to improve zero-shot CoT reasoning across languages. |
| Outcome: | The proposed method outperforms existing prompting methods on several benchmarks. |
Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation (2022.emnlp-main)
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| Challenge: | Existing zero-shot methods fail to align speech and text into a shared semantic space . Existing methods require expensive and expensive parallel ST data . |
| Approach: | They propose a method that uses a shared discrete vocabulary space to align speech and text into a common space. |
| Outcome: | The proposed method significantly improves the SOTA and even performs on par with the strong supervised ST baselines. |
LongWeave: A Long-Form Generation Benchmark Bridging Real-World Relevance and Verifiability (2025.findings-emnlp)
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Zikai Xiao, Fei Huang, Jianhong Tu, Jianhui Wei, Wen Ma, Yuxuan Zhou, Jian Wu, Bowen Yu, Zuozhu Liu, Junyang Lin
| Challenge: | Existing benchmarks for long-form generation assess real-world queries with hard-to-verify metrics or use synthetic setups that overlook real-life intricacies. |
| Approach: | They propose a new approach that balances verifiable and real-world assessment with Target-Anchored Evaluation. |
| Outcome: | The proposed model balances real-world and verifiable assessment with Target-Anchored Evaluation (TAE) it generates queries, textual materials, and anchors based on verifier targets within real-life scenarios . |
IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation (2026.acl-long)
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Yinghao Tang, Xueding Liu, Boyuan Zhang, Tingfeng Lan, Yupeng Xie, Jiale Lao, Yiyao Wang, Haoxuan Li, Tingting Gao, Bo Pan, Luoxuan Weng, Xiuqi Huang, Minfeng Zhu, Yingchaojie Feng, Yuyu Luo, Wei Chen
| Challenge: | Generated infographics may appear correct at first glance but contain easily overlooked issues, such as distorted data encoding or incorrect textual content. |
| Approach: | They propose to evaluate reliability of text-to-infographic generation using IGenBench . they employ multimodal large language models to verify each question . |
| Outcome: | The proposed framework decomposes reliability verification into atomic yes/no questions based on a taxonomy of 10 question types. |
Robust Lottery Tickets for Pre-trained Language Models (2022.acl-long)
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| Challenge: | Recent studies have shown that pre-trained language models contain smaller matching subnetworks that are not robust to adversarial examples. |
| Approach: | They propose a method to find robust tickets hidden in pre-trained language models by learning binary weight masks and an adversarial loss objective to guide the search. |
| Outcome: | The proposed method improves on previous work on adversarial robustness evaluation. |
LegalCore: A Dataset for Event Coreference Resolution in Legal Documents (2025.findings-acl)
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Kangda Wei, Xi Shi, Jonathan Tong, null Sai Ramana Reddy, Anandhavelu Natarajan, Rajiv Jain, Aparna Garimella, Ruihong Huang
| Challenge: | Existing research on event coreference resolution is limited to news articles . existing datasets for news articles are limited to events and coreferences . |
| Approach: | They present a dataset for the legal domain LegalCore which has been annotated with event and event coreference information. |
| Outcome: | The legal contract documents annotated in this dataset are several times longer than news articles, with an average length of around 25k tokens per document. |
From Behavioral Performance to Internal Competence: Interpreting Vision-Language Models with VLM-Lens (2025.emnlp-demos)
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Hala Sheta, Eric Haoran Huang, Shuyu Wu, Ilia Alenabi, Jiajun Hong, Ryker Lin, Ruoxi Ning, Daniel Wei, Jialin Yang, Jiawei Zhou, Ziqiao Ma, Freda Shi
| Challenge: | Existing vision-language models are based on exactmatch based accuracy and its derivations to evaluate performance. |
| Approach: | They propose a toolkit that supports systematic benchmarking, analysis, and interpretation of vision-language models by extracting intermediate outputs from any layer during the forward pass of open-source VLMs. |
| Outcome: | The proposed toolkit supports 16 state-of-the-art base VLMs and their over 30 variants and is extensible to accommodate new models without changing the core logic. |
Dual-Alignment Pre-training for Cross-lingual Sentence Embedding (2023.acl-long)
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Ziheng Li, Shaohan Huang, Zihan Zhang, Zhi-Hong Deng, Qiang Lou, Haizhen Huang, Jian Jiao, Furu Wei, Weiwei Deng, Qi Zhang
| Challenge: | Recent studies have shown that dual encoder models trained with the sentence-level translation ranking task are effective methods for cross-lingual sentence embedding. |
| Approach: | They propose a dual-alignment pre-training framework that incorporates both sentence-level and token-level alignment. |
| Outcome: | The proposed framework improves cross-lingual sentence embedding on three cross-linguistic benchmarks. |
Uncovering Argumentative Flow: A Question-Focus Discourse Structuring Framework (2025.emnlp-main)
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| Challenge: | Existing structure modeling approaches fail to capture the author’s rhetorical intent and reasoning process. |
| Approach: | They propose a Question-Focus discourse structuring framework that explicitly models the underlying argumentative flow by anchoring each argumentative unit to a guiding question and a set of attentional foci. |
| Outcome: | The proposed framework outperforms baseline models and curated models on an argument reconstruction task in Chinese think-tank articles and claims coverage. |
Fast Quiet-STaR: Thinking Without Thought Tokens (2025.findings-emnlp)
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| Challenge: | Large Language Models have achieved impressive performance across a range of tasks, but further gains require more than scaling up model sizes or training data. |
| Approach: | They propose a method that gradually reduces the number of thought tokens . this method allows models to internalize more abstract reasoning processes . |
| Outcome: | The proposed framework preserves the benefits of token-level reasoning while reducing computational cost. |
SAM3-I: Segment Anything with Instructions (2026.acl-long)
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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng
| Challenge: | Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering. |
| Approach: | They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework. |
| Outcome: | Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability. |
Cross-lingual Multimodal Sentiment Analysis for Low-Resource Languages via Language Family Disentanglement and Rethinking Transfer (2025.findings-acl)
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| Challenge: | Existing multimodal sentiment analysis methods are limited to textual data and cannot handle multimodal scenarios. |
| Approach: | They propose a transfer learning framework that allows cross-lingual and cross-modal alignments and a language family disentanglement module that enhances the sharing of language universals within families. |
| Outcome: | The proposed method is superior to existing methods and can handle low-resource languages. |
Benchmarking Chinese Commonsense Reasoning of LLMs: From Chinese-Specifics to Reasoning-Memorization Correlations (2024.acl-long)
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| Challenge: | Currently, many benchmarks evaluate the commonsense reasoning of large language models (LLMs), but most are English-based, limiting non-English evaluations. |
| Approach: | They propose to use Chinese commonsense reasoning to evaluate LLMs' commonsensing ability. |
| Outcome: | The proposed benchmark covers both globally known and Chinese-specific commonsense reasoning abilities and can be used as a reference for future research. |
Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)
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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen
| Challenge: | Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. |
| Approach: | They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
| Outcome: | The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling (2025.findings-naacl)
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| Challenge: | Recent advances in large language models (LLMs) have significantly enhanced their performance in various natural language processing tasks. |
| Approach: | They propose a robust and pluggable knowledge rewriter that is optimized for LLM generation by supporting the model's supportiveness. |
| Outcome: | The proposed model can be used to rewrite knowledge in a supervised manner. |
Pre-Training to Learn in Context (2023.acl-long)
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| Challenge: | Pre-trained language models are not explicitly trained to learn in context. |
| Approach: | They propose a framework to enhance in-context learning by pre-training language models on a large collection of "intrinsic tasks" they evaluate the in-constitution learning performance of the model trained with PICL on seven widely-used text classification datasets and the Super-NaturalInstrctions benchmark . |
| Outcome: | The proposed framework outperforms larger language models with nearly 4x parameters on seven widely-used datasets and the Super-NaturalInstrctions benchmark. |
Aligned Dual Channel Graph Convolutional Network for Visual Question Answering (2020.acl-main)
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| Challenge: | Existing graph-based methods focus only on relations between objects in an image and neglect the importance of syntactic dependency relations between words. |
| Approach: | They propose a dual channel graph convolutional network to capture relations between objects in an image and syntactic dependency relations between words in a question. |
| Outcome: | The proposed model achieves comparable performance with the state-of-the-art approaches. |
Unsupervised Fine-tuning for Text Clustering (2020.coling-main)
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| Challenge: | Existing approaches to text clustering fine-tune pre-trained models have been limited. |
| Approach: | They propose a method to fine-tune pre-trained models unsupervisedly for text clustering by learning text representations and cluster assignments using a clustering oriented loss. |
| Outcome: | The proposed model outperforms baseline methods and achieves state-of-the-art results on three text clustering datasets. |
DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models (2026.findings-acl)
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Yakun Zhu, Zhongzhen Huang, Linjie Mu, Yutong Huang, Wei Nie, Jiaji Liu, Shaoting Zhang, Pengfei Liu, Xiaofan Zhang
| Challenge: | Existing medical benchmarks for diagnostic reasoning are limited in their ability to perform complex tasks. |
| Approach: | They propose to benchmark diagnostic capabilities of large language models to assess their accuracy and generalization bottlenecks. |
| Outcome: | The proposed model achieves 45.82%, 31.09%, and 17.79% accuracy, compared to current models, o3-mini, e1 and DeepSeek-R1 . |
SDGO: Self-Discrimination-Guided Optimization for Consistent Safety in Large Language Models (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) excel at various tasks but are vulnerable to jailbreak attacks that induce harmful content generation. |
| Approach: | They propose a reinforcement learning framework that leverages the model’s own discrimination capabilities as a reward signal to enhance generation safety through iterative self-improvement. |
| Outcome: | The proposed framework improves model safety by iterative self-improvement without additional annotated data or external models during training phase. |
LIST: Linearly Incremental SQL Translator for Single-Hop Reasoning, Generation and Verification (2025.findings-acl)
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| Challenge: | Existing schema linking methods are not able to handle complex SQL queries. |
| Approach: | They propose a new algorithm that transforms SQL queries into grammatically verifiable sub-queries which are arranged sequentially to reflect single-hop reasoning steps. |
| Outcome: | The proposed algorithm achieves significant performance gains on the BIRD dataset and surpasses schema linking methods at comparable or better cost. |
DaMoC: Efficiently Selecting the Optimal Large Language Model for Fine-tuning Domain Tasks Based on Data and Model Compression (2025.findings-emnlp)
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| Challenge: | Large language models excel in general tasks but struggle with domain-specific ones, requiring fine-tuning with specific data. |
| Approach: | They propose a Data and Model Compression Framework that categorizes data filtering methodologies into three distinct paradigms: (1) distribution-aware methods, (2) quality-a aware methods, and (3) hybrid approaches considering both dimensions. |
| Outcome: | The proposed framework can select the optimal LLM while saving approximately 20-fold in training time. |
Lexicon-Based Graph Convolutional Network for Chinese Word Segmentation (2021.findings-emnlp)
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| Challenge: | Existing methods for Chinese word segmentation have high performance on benchmarks but are limited by the small-scale annotated corpus. |
| Approach: | They propose a framework that incorporates a lexicon-based graph convolutional network into the Transformer encoder to improve Chinese word segmentation (CWS) Chinese word is an essential and pre-processing step for many downstream NLP tasks. |
| Outcome: | The proposed framework captures the information of candidate words and improves performance on benchmarks and datasets. |
Online Distilling from Checkpoints for Neural Machine Translation (N19-1)
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| Challenge: | Existing neural machine translation models have a deep structure with large amounts of parameters, making them hard to train. |
| Approach: | They propose an online method to generate a teacher model from checkpoints . they show steady improvement over a strong self-attention-based baseline system . |
| Outcome: | The proposed method improves on-the-fly on several datasets and language pairs. |
Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering (2022.coling-1)
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| Challenge: | Existing methods for multi-hop reasoning ignore grounding on supporting facts of each step, which tends to generate inaccurate decompositions. |
| Approach: | They propose an interpretable stepwise reasoning framework that incorporates supporting sentences and questions at each intermediate step and utilizes the inference of the current hop for the next until reasoning out the final result. |
| Outcome: | The proposed model can boost performance and yield a better interpretable reasoning process without decomposition supervision. |
Long-context Language Models Fail in Basic Retrieval Tasks Without Sufficient Reasoning Steps (2025.findings-emnlp)
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| Challenge: | despite their extensive context window, long-context language models fail in some basic cases . a recent study shows that long-cot methods are not necessary for long-constituency tasks . |
| Approach: | a new study evaluates long-context language models with a large context window . the authors propose a method that can be well addressed with arbitrary reasoning steps . |
| Outcome: | The proposed methods are well addressed with a sufficient number of reasoning steps, guided by specific CoT prompts. |
AI Hospital: Benchmarking Large Language Models in a Multi-agent Medical Interaction Simulator (2025.coling-main)
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| Challenge: | Recent large language models (LLMs) have demonstrated superior performance in static medical question answering benchmarks, rivaling even human experts. |
| Approach: | They propose a multi-agent framework emulating dynamic medical interactions between Doctor as player and NPCs including Patient and Examiner to assess the performance of LLM-driven Doctor agents in simulated clinical scenarios. |
| Outcome: | The proposed framework emulates dynamic medical interactions between Doctor as player and NPCs including Patient and Examiner. |
Confidence is not Timeless: Modeling Temporal Validity for Rule-based Temporal Knowledge Graph Forecasting (2024.acl-long)
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| Challenge: | Existing literature on temporal knowledge Graph Forecasting lacks in-depth investigation into how confidence evolves with time. |
| Approach: | They propose a framework to model the temporal validity of rules for Temporal Knowledge Graph Forecasting (TKGF) they propose rule-adversarial negative sampling and time-aware negative sampling strategies to facilitate TempValid learning. |
| Outcome: | The proposed framework outperforms state-of-the-art (SOTA) rule-based methods on six TKGF datasets. |
Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph (2020.emnlp-main)
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| Challenge: | Existing approaches that integrate commonsense knowledge into pre-trained language models simply transfer relational knowledge while ignoring rich connections within the knowledge graph. |
| Approach: | They propose a method that leverages structural and semantic information of the knowledge graph to generate commonsense-aware text. |
| Outcome: | The proposed method outperforms baseline models on three text generation tasks that require reasoning over commonsense knowledge. |
LegalAgentBench: Evaluating LLM Agents in Legal Domain (2025.acl-long)
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Haitao Li, Junjie Chen, Jingli Yang, Qingyao Ai, Wei Jia, Youfeng Liu, Kai Lin, Yueyue Wu, Guozhi Yuan, Yiran Hu, Wuyue Wang, Yiqun Liu, Minlie Huang
| Challenge: | Existing general-domain benchmarks do not capture complexity of real-world judicial cognition and decision-making. |
| Approach: | They propose a benchmark specifically designed to evaluate LLM Agents in the legal domain. |
| Outcome: | The proposed benchmark includes 17 corpora from real-world legal scenarios and provides 37 tools for interacting with external knowledge. |
Implicit Relation Linking for Question Answering over Knowledge Graph (2022.findings-acl)
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| Challenge: | Existing methods rely on textual similarities between NL and KG to build relation links. |
| Approach: | They propose an implicit relation linking method called ImRL which links relation phrases in NL to relation paths in KG. |
| Outcome: | The proposed method significantly outperforms state-of-the-art methods on two benchmarks and a newly-created datasets. |
METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models (2026.acl-long)
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| Challenge: | Existing benchmarks evaluate contextual causal reasoning in fragmented settings, failing to ensure context consistency or cover the full causal hierarchy. |
| Approach: | They use a unified context to benchmark large language models' contextual causal reasoning skills. |
| Outcome: | The proposed benchmarks show that LLMs are susceptible to distraction by irrelevant but factually correct information at lower level of causality. |
Continual Test-time Adaptation for End-to-end Speech Recognition on Noisy Speech (2024.emnlp-main)
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| Challenge: | Current ASR TTA methods focus on non-continual TTA, which limits cross-sample knowledge learning compared to continual TTA. |
| Approach: | They propose a Fast-slow TTA framework that leverages the advantage of continual and non-continual TTA and a Dynamic SUTA method that automatically detects domain shifts and resets the model. |
| Outcome: | The proposed method outperforms non-continual and continual TTA methods while maintaining robustness to domain shifts without requiring domain boundary information. |
ResLoRA: Identity Residual Mapping in Low-Rank Adaption (2024.findings-acl)
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Shuhua Shi, Shaohan Huang, Minghui Song, Zhoujun Li, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Low-rank adaptation (LoRA) is one of the most popular parameter-efficient fine-tuning methods. |
| Approach: | They propose a low-rank adaptation method that adds residual paths during training and merges them together during inference to achieve better results. |
| Outcome: | The proposed method achieves 2.5x faster convergence speed and improves performance by 14.3% on NLG, NLU, and text-to-image tasks. |
Generating Commonsense Explanation by Extracting Bridge Concepts from Reasoning Paths (2020.aacl-main)
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| Challenge: | Existing tasks that use commonsense reasoning as multi-choice reading comprehension lack direct assessment to machine commonsence and impede its practicability to realistic scenarios. |
| Approach: | They propose a method that first extracts the underlying concepts which are served as bridges in the reasoning chain and then integrates these concepts to generate the final explanation. |
| Outcome: | The proposed model outperforms the state-of-the-art models in automatic and human evaluation. |
RiskLab: A Controlled Toolkit for Probing Emergent Risks in LLM-Based Multi-Agent Systems (2026.acl-demo)
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Yu Jiang, Wenjie Wang, Yue Huang, Yanbo Wang, Zhenhong Zhou, Xiuying Chen, Yang Liu, Pin-Yu Chen, Wei Wang, Xiangliang Zhang
| Challenge: | Recent advances in large language model (LLM) agents have accelerated deployment of multi-agent systems for complex tasks. |
| Approach: | They propose an open-source toolkit for instantiating, probing, and measuring emergent risks in LLM-based multi-agent systems under controlled conditions. |
| Outcome: | The proposed toolkit is based on a structured topology–environment–protocol–agent–task quintuple enabling reproducible studies of how communication structure, coordination mechanisms, and incentives shape system-level risks. |
CoMAE: A Multi-factor Hierarchical Framework for Empathetic Response Generation (2021.findings-acl)
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| Challenge: | Existing methods for empathetic response generation ignore hierarchical relationships between different factors, leading to a weak ability of empathy modeling. |
| Approach: | They propose a multi-factor hierarchical framework for empathetic response generation which models the above three key factors in a hierarchically structured way. |
| Outcome: | The proposed model generates more empathetic responses than previous methods. |
MoralDial: A Framework to Train and Evaluate Moral Dialogue Systems via Moral Discussions (2023.acl-long)
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| Challenge: | A moral dialogue system aligned with users’ values could enhance conversation engagement and user connections. |
| Approach: | They propose a framework to train and evaluate moral dialogue systems based on communication mechanisms of morality and a method to construct moral discussions between simulated users and the dialogue system. |
| Outcome: | The proposed framework can train and evaluate moral dialogue systems based on simulated users and their values . |
Shorten After You’re Right: Lazy Length Penalties for Reasoning RL (2026.findings-acl)
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Danlong Yuan, Tian Xie, Shaohan Huang, Huishuai Zhang, Zhuocheng Gong, Chong Luo, Furu Wei, Dongyan Zhao
| Challenge: | Existing shortening methods for long reasoning models rely on additional supervision or multi-stage post-training. |
| Approach: | They propose a lazy length penalty that imposes length pressure on models without extra training stages. |
| Outcome: | The proposed method significantly reduces response length without extra training stages while maintaining or improving performance. |
An Enhanced Knowledge Injection Model for Commonsense Generation (2020.coling-main)
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Zhihao Fan, Yeyun Gong, Zhongyu Wei, Siyuan Wang, Yameng Huang, Jian Jiao, Xuanjing Huang, Nan Duan, Ruofei Zhang
| Challenge: | a recent study shows that digging the relationship of concepts from scratch is non-trivial for commonsense generation tasks. |
| Approach: | They use a retrieve-and-edit framework to retrieve a prototype with these concepts . they use qt and qq to generate commonsense questions at scale . |
| Outcome: | The proposed method significantly improves the performance on commonsense generation tasks. |
Playing 20 Question Game with Policy-Based Reinforcement Learning (D18-1)
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| Challenge: | The 20 Questions (Q20) game encourages deductive reasoning and creativity. |
| Approach: | They propose a policy-based Reinforcement Learning method which learns optimal question selection . the method is robust to noisy answers and uses a reward network to estimate the more informative reward . |
| Outcome: | The proposed method outperforms an entropy-based engineering system and has competitive performance in noisy-free simulation environment. |
Can GRPO Boost Complex Multimodal Table Understanding? (2025.emnlp-main)
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Xiaoqiang Kang, Shengen Wu, Zimu Wang, Yilin Liu, Xiaobo Jin, Kaizhu Huang, Wei Wang, Yutao Yue, Xiaowei Huang, Qiufeng Wang
| Challenge: | Existing table understanding methods struggle with low initialization accuracy and coarse rewards in tabular contexts. |
| Approach: | They propose a three-stage RL framework that enhances multimodal table understanding through: (1) Warm-up that prompts initial perception and reasoning capabilities; (2) Perception Alignment GRPO (PA-GRPO); (3) Hint-Completion GR PO (HC-GRP); |
| Outcome: | The proposed framework outperforms existing models on held-in and held-out datasets, outperforming SFT and GRPO largely. |
Effidit: An Assistant for Improving Writing Efficiency (2023.acl-demo)
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Shuming Shi, Enbo Zhao, Wei Bi, Deng Cai, Leyang Cui, Xinting Huang, Haiyun Jiang, Duyu Tang, Kaiqiang Song, Longyue Wang, Chenyan Huang, Guoping Huang, Yan Wang, Piji Li
| Challenge: | Effidit is a digital writing assistant that provides three modules to help users write faster and more efficiently. |
| Approach: | They present Effidit, a digital writing assistant that provides three modules to help users write higher-quality text more efficiently. |
| Outcome: | Effidit expands the capabilities of a typical writing assistant by providing three modules . Effit can help users create their own text faster and more efficiently . |
AlphaContext: An Evolutionary Tree-based Psychometric Context Generator for Creativity Assessment (2026.acl-long)
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Yixuan Wang, Yue Huang, Hong Qian, Yunzhao Wei, Yifei Ding, Wenkai Wang, Zhi Liu, Zhongjing Huang, Aimin Zhou, Jiajun Guo
| Challenge: | Existing LLM-based tools struggle with insufficient assessment cues, weak narrative coherence, limited stylistic diversity, and poor support for creative thinking. |
| Approach: | They propose an evolutionary tree-based psychometric context generator that integrates rule-guided outline planning, sentence-level MCTS generation, MAP-Elites quality-diversity optimization and assessment-guide refiner simulation. |
| Outcome: | The proposed tool outperforms strong LLMs and structured frameworks on 7 evaluation dimensions and shows higher alignment with expert-designed contexts. |
Activating Distributed Visual Region within LLMs for Efficient and Effective Vision-Language Training and Inference (2025.acl-long)
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| Challenge: | Existing Large Vision-Language Models (LVLMs) learn visual capacity through visual instruction tuning. |
| Approach: | They propose a method for LVLMs to be trained by selective layers tuning . they propose removing non-critical layers outside the visual region . |
| Outcome: | The proposed approach preserves nearly 99% of visual performance and improves textual task results while reducing training time. |
Robustness Testing of Language Understanding in Task-Oriented Dialog (2021.acl-long)
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Jiexi Liu, Ryuichi Takanobu, Jiaxin Wen, Dazhen Wan, Hongguang Li, Weiran Nie, Cheng Li, Wei Peng, Minlie Huang
| Challenge: | a lack of systematic studies on the robustness of language understanding models in task-oriented dialog systems is limiting . authors propose a model-agnostic toolkit LAUG to approximate natural language perturbations . |
| Approach: | They propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness of language understanding models in task-oriented dialog systems. |
| Outcome: | The proposed toolkit reveals critical robustness issues in state-of-the-art models. |
Keep it Consistent: Topic-Aware Storytelling from an Image Stream via Iterative Multi-agent Communication (2020.coling-main)
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| Challenge: | Existing methods for visual storytelling construct text description independently for each image and roughly concatenate them as a story, which leads to the problem of generating semantically incoherent content. |
| Approach: | They propose a topic description task to detect the global semantic context of an image stream and a story is then constructed with the guidance of the topic description. |
| Outcome: | The proposed framework can generate stories with higher quality compared to state-of-the-art methods on a VIST dataset. |
Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models (2025.coling-main)
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Haoran Lian, Junmin Chen, Wei Huang, Yizhe Xiong, Wenping Hu, Guiguang Ding, Hui Chen, Jianwei Niu, Zijia Lin, Fuzheng Zhang, Di Zhang
| Challenge: | Recent studies show that Large language models struggle with handling long token sequences due to limited training context size. |
| Approach: | They propose a single-stage continual pretraining method to equip LLMs with long context modeling capabilities. |
| Outcome: | The proposed method outperforms existing methods on 4 language modeling benchmarks. |
No One Fits All: From Fixed Prompting to Learned Routing in Multilingual LLMs (2026.findings-acl)
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| Challenge: | Existing studies show that translation-based prompting is not universally optimal for multilingual LLMs. |
| Approach: | They evaluate translation-based prompting across ten languages and four benchmarks . they propose a lightweight classifier that predicts whether native or translation- based prompts are optimal . |
| Outcome: | The proposed classifiers achieve statistically significant improvements over fixed prompting strategies across ten languages and four benchmarks. |
Towards Stable and Effective Reinforcement Learning for Mixture-of-Experts (2026.acl-long)
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| Challenge: | Reinforcement learning with verifiable rewards (RLVR) training with Mixture-of-Experts policies remains fragile and prone to reward collapse. |
| Approach: | They propose a router shift-based policy optimization method that computes a per-token router-shift ratio conditioned on the previously activated experts and applies stop-gradient and a lower-bound floor. |
| Outcome: | The proposed method achieves better performance and greater stability than previous methods. |
Improving Text Embeddings with Large Language Models (2024.acl-long)
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| Challenge: | Existing methods for obtaining text embeddings require complex training pipelines . authors leverage proprietary LLMs to generate diverse synthetic data for text embeds based on 93 languages . |
| Approach: | They propose a method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. |
| Outcome: | The proposed method achieves strong performance on competitive text embedding benchmarks without using any labeled data. |
Targeted Exploration via Unified Entropy Control for Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing methods for group relative policy optimization suffer from entropy collapse . Existing exploration methods introduce additional bias or variance during exploration, making it difficult to maintain stability. |
| Approach: | They propose a framework that provides targeted mechanisms for exploration and stabilization. |
| Outcome: | The proposed framework expands search space on difficult prompts while preventing entropy growth uncontrollably. |
Mitigating Gender Bias via Fostering Exploratory Thinking in LLMs (2025.findings-emnlp)
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| Challenge: | Large Language Models often exhibit gender bias, resulting in unequal treatment of male and female subjects across contexts. |
| Approach: | They propose a framework that encourages exploratory thinking in large language models . the framework generates story pairs featuring male and female protagonists in structurally identical scenarios . |
| Outcome: | The proposed framework reduces gender bias while preserving or even enhancing general model capabilities. |
LifeSim: Long-Horizon User Life Simulator for Personalized Assistant Evaluation (2026.findings-acl)
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| Challenge: | Existing benchmarks for personalized assistants fail to capture the complexity of external contexts and users’ cognitive states. |
| Approach: | They propose a user simulator that models user cognition through the Belief-Desire-Intention model within physical environments for coherent life trajectories generation and simulates intention-driven user interactive behaviors. |
| Outcome: | The proposed model can model user cognition through the Belief-Desire-Intention model within physical environments for coherent life trajectories generation and simulates intention-driven user interactive behaviors. |
Instruction Pre-Training: Language Models are Supervised Multitask Learners (2024.emnlp-main)
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| Challenge: | Unsupervised multitask pre-training has been the key to the success of language models (LMs) however, scaling it in the post-training stage trends towards better generalization. |
| Approach: | They propose a framework that augments massive raw corpora with instruction-response pairs to pre-train LMs. |
| Outcome: | The proposed framework augments massive raw corpora with instruction-response pairs to pre-train LMs. |
A Fully Probabilistic Perspective on Large Language Model Unlearning: Evaluation and Optimization (2025.emnlp-main)
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| Challenge: | Large Language Model Unlearning (LLMU) is a promising way to remove private or sensitive information from large language models. |
| Approach: | They propose a Fully Probabilistic Evaluation framework that incorporates input and output distributions in LLMU evaluation. |
| Outcome: | The proposed framework improves unlearning effectiveness by 50.1% and robustness by 37.2% on Llama-2-7B. |
GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level (D19-1)
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| Challenge: | SQA is an emerging application of NLP in the medical, geography, and legal domains. |
| Approach: | They propose a dataset of 1,981 scenarios and 4,110 multiple-choice questions in geography domain at high school level. |
| Outcome: | The proposed dataset consists of 1,981 scenarios and 4,110 multiple-choice questions in the geography domain at high school level. |
PersonaAgent: Bridging Memory and Action for Personalized LLM Agents (2026.findings-acl)
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Weizhi Zhang, Xinyang Zhang, Chenwei Zhang, Liangwei Yang, Jingbo Shang, Zhepei Wei, Henry Peng Zou, Zijie Huang, Zhengyang Wang, Yifan Gao, Xiaoman Pan, Lian Xiong, Jingguo Liu, Philip S. Yu, Xian Li
| Challenge: | Existing Large Language Model (LLM) enabled agents lack flexibility to respond to users’ varying needs and preferences. |
| Approach: | They propose a test-time user-preference alignment strategy that optimizes the persona prompt, ensuring real-time preference alignment through textual loss feedback between simulated and ground-truth responses. |
| Outcome: | The proposed framework outperforms baseline methods in real-time and in real applications. |
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 . |
IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory (2025.acl-long)
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| Challenge: | Large language models have demonstrated exceptional performance across a wide range of tasks . however, selecting the optimal LLM to respond to a user query often necessitates a delicate balance between performance and cost. |
| Approach: | They propose a multi-LLM routing framework that efficiently routes user queries to the most suitable LLM. |
| Outcome: | The proposed framework outperforms baseline methods in terms of effectiveness and interpretability. |
S3HQA: A Three-Stage Approach for Multi-hop Text-Table Hybrid Question Answering (2023.acl-short)
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| Challenge: | Existing question answering systems use a retriever-reader framework to answer multi-hop questions . existing models lack retrieval, selector, and reasoner capabilities . |
| Approach: | They propose a three-stage text tableQA framework which comprises of retriever, selector, and reasoner. |
| Outcome: | The proposed framework outperforms baseline methods in the few-shot setting and ranks first on the HybridQA leaderboard. |
Incorporating Argument-Level Interactions for Persuasion Comments Evaluation using Co-attention Model (C18-1)
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| Challenge: | Existing research explores different text features of reply comments on word level and ignores interactions between participants. |
| Approach: | They propose a co-attention mechanism based neural network to capture interactions between participants on argument level to better model dialogical argumentation. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a publicly available dataset showing that it extracts interactive argument pairs from the original post and the reply. |
Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System (2023.emnlp-main)
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| Challenge: | generative models struggle to distinguish subtle differences among retrieved knowledge records, resulting in suboptimal quality of generated responses. |
| Approach: | They propose to use maximum marginal likelihood to train a perceptive retriever by utilizing signals from response generation for supervision. |
| Outcome: | The proposed approach improves on three task-oriented dialogue datasets using T5 and ChatGPT as the backbone models. |
LCMA-SRT: Language-Conditional Mixture-of-Experts Adapters for Joint Multilingual Speech Recognition and Translation (2026.acl-long)
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| Challenge: | Existing hierarchical transducers suffer from negative transfer and unstable target-language generation, while training separate models for each direction is computationally prohibitive. |
| Approach: | They propose a hierarchical transducer with language-conditional Mixture-of-Experts adapters to improve multilingual joint automatic speech recognition and speech translation. |
| Outcome: | Experiments on Europarl-ST (9 languages, 72 directions) show that LCMA-SRT improves both ASR and ST within a single joint model, reducing average WER and improving BLEU and COMET over strong hierarchical transducer baselines. |
Language-Specific Neurons: The Key to Multilingual Capabilities in Large Language Models (2024.acl-long)
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Tianyi Tang, Wenyang Luo, Haoyang Huang, Dongdong Zhang, Xiaolei Wang, Xin Zhao, Furu Wei, Ji-Rong Wen
| Challenge: | Despite the impressive multilingual capabilities demonstrated by LLMs, the understanding of how these abilities develop and function remains nascent. |
| Approach: | They propose a novel detection method to pinpoint language-specific neurons within LLMs by selectively activating or deactivating these neurons. |
| Outcome: | The proposed method can “steer” the output language of LLMs by selectively activating or deactivating language-specific neurons. |
TRANS-ZERO: Self-Play Incentivizes Large Language Models for Multilingual Translation Without Parallel Data (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have reshaped machine translation, but multilingual MT still relies heavily on parallel data for supervised fine-tuning. |
| Approach: | They propose a framework that leverages only monolingual data and the intrinsic multilingual knowledge of Large Language Models (LLMs). |
| Outcome: | The proposed framework matches models trained on large-scale parallel data and excels in non-English translation directions. |
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)
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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang
| Challenge: | Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level. |
| Approach: | They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context. |
| Outcome: | The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set. |
Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection (2025.acl-long)
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| Challenge: | Existing work has been using decoding-free candidate selection methods to obtain candidate probability from initial output logits over vocabulary. |
| Approach: | They propose to evaluate a set of tasks using decoding-free candidate selection methods on a comprehensive set of questions. |
| Outcome: | The proposed methods are evaluated on a set of tasks including five multiple-choice QA tasks with a small candidate pool and four clinical decision tasks with 10k+ options. |
Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling (2025.acl-long)
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Shihan Dou, Jiayi Chen, Chenhao Huang, Feng Chen, Wei Chengzhi, Huiyuan Zheng, Shichun Liu, Yan Liu, Chenxiao Liu, Chao Xin, Lin Yan, Zongzhang Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing reward models concatenate contexts and responses, but they often ignore crucial segments of the context that are important for evaluating the response quality. |
| Approach: | They propose a reward model that evaluates the response quality based on a given context and assigns a rewards reward. |
| Outcome: | The proposed framework significantly improves preference modeling by increasing attention to relevant information within the context and achieves better generalizability. |
Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning (2023.acl-long)
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Barun Patra, Saksham Singhal, Shaohan Huang, Zewen Chi, Li Dong, Furu Wei, Vishrav Chaudhary, Xia Song
| Challenge: | XY-LENT: X-Y bitext enhanced Language ENcodings achieves state-of-the-art performance over 5 cross-lingual tasks within all model size bands. |
| Approach: | They propose a method for building multilingual representation models that are competitive with existing models and more parameter efficient. |
| Outcome: | The proposed model outperforms XLM-R XXL and is 5x and 6x smaller respectively. |
Boosting Event Extraction with Denoised Structure-to-Text Augmentation (2023.findings-acl)
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Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin
| Challenge: | Existing methods for event extraction neglect grammatical incorrectness, structure misalignment, and semantic drifting . et al., 2004; Ahn, 2006) show that the proposed method generates more diverse text representations for event extracting compared with the state-of-the-art. |
| Approach: | They propose a framework for event extraction that generates additional training data and iteratively selects the effective subset from the generated training data. |
| Outcome: | The proposed method generates more diverse representations of training data and achieves comparable results with the state-of-the-art. |
SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval (2023.acl-long)
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Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei
| Challenge: | SimLM uses a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training. |
| Approach: | They propose a simple yet effective pre-training method for dense passage retrieval that learns to compress the passage information into a dense vector through self-supervised pre-tuning. |
| Outcome: | The proposed method outperforms multi-vector approaches on large-scale passage retrieval datasets and shows significant improvements over baselines. |
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. |
DELAN: Dual-Level Alignment for Vision-and-Language Navigation by Cross-Modal Contrastive Learning (2024.lrec-main)
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| Challenge: | Existing studies focus on cross-modal attention at the fusion stage, but modality features generated by disparate uni-encoders reside in their own spaces, leading to a decline in the quality of cross-modulation and decision-making. |
| Approach: | They propose a framework to align navigation-related modalities before fusion by cross-modal contrastive learning. |
| Outcome: | The proposed framework integrates with the majority of existing models, resulting in improved navigation performance on various VLN benchmarks, including R2R, R4R, and CVDN. |
Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models (2024.findings-naacl)
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| Challenge: | Existing methods that rely on limited demos and out-of-demonstration (OOD) queries fail when faced with out- of-demotion queries. |
| Approach: | They propose a query-aware prompting method that elicits the inherent generalizability of large language models by query-based demo generation. |
| Outcome: | The proposed method outperforms state-of-the-art methods in the OOD setting and two public math benchmarks. |
Making Harmful Behaviors Unlearnable for Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) are often customized by fine-tuning for the requirements of different domains. |
| Approach: | They propose a controllable training framework to make undesired behaviors unlearnable during the fine-tuning process. |
| Outcome: | The proposed framework makes undesired behaviors unlearnable during the fine-tuning process while preserving the ability to learn other information. |
Induct-Learn: Short Phrase Prompting with Instruction Induction (2024.emnlp-main)
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| Challenge: | Existing methods for generating instructions from demonstrations rely on large datasets or numerous examples, which is impractical and costly in real-world scenarios. |
| Approach: | They propose a task-level framework that induces pseudo instructions from a few demonstrations and a short phrase, adding a CoT process into existing demonstrations. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two datasets and exhibits cross-model adaptability and lower cost. |
SWING: Balancing Coverage and Faithfulness for Dialogue Summarization (2023.findings-eacl)
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Kung-Hsiang Huang, Siffi Singh, Xiaofei Ma, Wei Xiao, Feng Nan, Nicholas Dingwall, William Yang Wang, Kathleen McKeown
| Challenge: | Existing approaches to dialogue summarization rely on features of conversation data. |
| Approach: | They propose to use natural language inference models to improve coverage and faithfulness . they use fine-grained training signals to encourage model to generate missing content . |
| Outcome: | The proposed model achieves higher faithfulness and coverage while maintaining conciseness compared to prior methods. |
Enhancing LLM Language Adaption through Cross-lingual In-Context Pre-training (2025.emnlp-main)
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| Challenge: | Existing methods for enhancing cross-lingual transfer are limited by parallel resources and lack linguistic and domain coverage. |
| Approach: | They propose a cross-lingual in-context pre-training approach that leverages semantically related bilingual Wikipedia documents to enhance cross-linguistic transfer. |
| Outcome: | The proposed approach improves multilingual performance on three models across six target languages. |
Bridging the Gap between Synthetic and Natural Questions via Sentence Decomposition for Semantic Parsing (2023.tacl-1)
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| Challenge: | Existing methods to train a parser to perform zero-shot learning are limited by the lack of training data. |
| Approach: | They propose a decomposition-based method to unify the sentence structures of questions . their method can generalize to natural questions with novel text expressions . |
| Outcome: | The proposed method improves on synthetic data and on complex web questions with novel expressions. |
StructuralLM: Structural Pre-training for Form Understanding (2021.acl-long)
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| Challenge: | Existing pre-trained language models focus on text-only representation, neglecting cell-level layout information. |
| Approach: | They propose a pre-training approach to leverage cell and layout information from scanned documents. |
| Outcome: | The proposed model achieves state-of-the-art in various downstream tasks . it uses 2Dposition embeddings to model word-level layout information . |
PASUM: A Pre-training Architecture for Social Media User Modeling Based on Text Graph (2024.lrec-main)
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| Challenge: | Existing studies have incorporated different digital traces to better learn the representations of social media users, limited by overloaded text information and hard-to-collect social network information. |
| Approach: | They propose a Pre-training Architecture for Social Media User Modeling based on Text Graph and combine microblogs to represent social media users based upon the text graph model. |
| Outcome: | The proposed framework can represent users based on text even without social network information on microblogs. |
Advancing Sequential Numerical Prediction in Autoregressive Models (2025.acl-short)
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| Challenge: | Autoregressive models are the de facto choice for sequence generation tasks, but standard approaches treat digits as independent tokens and apply cross-entropy loss, overlooking the coherent structure of numerical sequences. |
| Approach: | They propose a novel approach to entropy loss by extending the Earth Mover’s Distance to preserve ordinal relationships between numerical values and sequence-level to penalize the overall discrepancy between predicted and actual sequences. |
| Outcome: | Extensive experiments show that NTIL improves numerical prediction and integrates effectively with LLMs/MLLMs. |
Debatrix: Multi-dimensional Debate Judge with Iterative Chronological Analysis Based on LLM (2024.findings-acl)
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| Challenge: | Recent studies have focused on short dialogues, but mainly on short debates. |
| Approach: | They propose to use Large Language Models to construct an automated debate judge to evaluate multi-turn debates. |
| Outcome: | The proposed system improves on the PanelBench benchmark, which compares its performance to actual debate outcomes. |
Joint Multi-Label Attention Networks for Social Text Annotation (N19-1)
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| Challenge: | Present research shows that title metadata could affect social annotation. |
| Approach: | They propose a title-guided attention network for document annotation with user-generated tags that separates the title from the content of a document and applies a semantic-based loss regulariser over each sentence in the content. |
| Outcome: | The proposed approach outperforms the Bi-GRU and Hierarchical Attention Network (HAN) on two open datasets with 10%-30% reduction in training time. |
mPLUG: Effective and Efficient Vision-Language Learning by Cross-modal Skip-connections (2022.emnlp-main)
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Chenliang Li, Haiyang Xu, Junfeng Tian, Wei Wang, Ming Yan, Bin Bi, Jiabo Ye, He Chen, Guohai Xu, Zheng Cao, Ji Zhang, Songfang Huang, Fei Huang, Jingren Zhou, Luo Si
| Challenge: | Existing pre-trained vision-language models suffer from inefficiency and linguistic signal overwhelmed by long visual sequences in cross-modal alignment. |
| Approach: | They propose a vision-language foundation model with cross-modal skip-connections that can be pre-trained end-to-end on large-scale image-text pairs with both discriminative and generative objectives. |
| Outcome: | The proposed model achieves state-of-the-art results on a wide range of vision-language downstream tasks, including image captioning, image-text retrieval, visual grounding and visual question answering. |
Unifying Cross-Lingual and Cross-Modal Modeling Towards Weakly Supervised Multilingual Vision-Language Pre-training (2023.acl-long)
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| Challenge: | Existing studies address the problem of translating English data into other languages, but they are limited in form and scale. |
| Approach: | They propose a framework to unify cross-lingual and cross-modal pre-training by using English data. |
| Outcome: | The proposed framework unifies cross-lingual and cross-modal pre-training on different data. |
SeMob: Semantic Synthesis for Dynamic Urban Mobility Prediction (2025.emnlp-main)
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| Challenge: | Existing spatiotemporal models struggle to interpret and adapt to abrupt changes caused by external events. |
| Approach: | They propose a LLM-powered semantic synthesis pipeline that extracts spatiotemporally related text from online texts and integrates it with spatio-temporal data. |
| Outcome: | The proposed framework achieves maximal reductions of 13.92% in MAE and 11.12% in RMSE compared to the spatiotemporal model. |
AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists (2025.emnlp-main)
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Yifei Li, Hanane Nour Moussa, Ziru Chen, Shijie Chen, Botao Yu, Mingyi Xue, Benjamin Burns, Tzu-Yao Chiu, Vishal Dey, Zitong Lu, Chen Wei, Qianheng Zhang, Tianyu Zhang, Song Gao, Xuhui Huang, Xia Ning, Nesreen K. Ahmed, Ali Payani, Huan Sun
| Challenge: | AutoSDT-5K is the only automatically collected and the largest open dataset for data-driven scientific discovery. |
| Approach: | They propose an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows. |
| Outcome: | The proposed pipeline synthesizes accurate tasks and tasks from a dataset of 5,404 tasks covering four scientific disciplines and 756 Python packages. |
Simplify the Usage of Lexicon in Chinese NER (2020.acl-main)
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| Challenge: | Named entity recognition (NER) is concerned with the identification of named entities in unstructured text. |
| Approach: | They propose a method for incorporating word lexicon into character representations . experimental results show method can be easily incorporated with pre-trained models . |
| Outcome: | The proposed method achieves 6.15 times faster inference speed and better performance on four benchmark Chinese NER datasets. |
GROVE: A Retrieval-augmented Complex Story Generation Framework with A Forest of Evidence (2023.findings-emnlp)
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| Challenge: | Existing methods for generating stories with complex plots rely on detailed prompts, which inadvertently limit the creative potential of the generated stories. |
| Approach: | They propose a retrieval-auGmented stoRy generation framework with a fOrest of eVidEnce to enhance stories’ complexity. |
| Outcome: | The proposed framework enables generating more diverse plotlines from human-written stories. |
Aspect Sentiment Classification with Aspect-Specific Opinion Spans (2020.emnlp-main)
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| Challenge: | Existing attention-based models for sentiment analysis are not able to capture opinion spans as a whole or variable-length opinion span. |
| Approach: | They propose a model that extracts aspect-specific opinion spans and evaluates sentiment polarity by exploiting extracted opinion features. |
| Outcome: | The proposed model extracts aspect-specific opinion spans and evaluates sentiment polarity using extracted opinion features. |
EmbSpatial-Bench: Benchmarking Spatial Understanding for Embodied Tasks with Large Vision-Language Models (2024.acl-short)
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| Challenge: | Recent studies have revealed significant deficiencies of LVLMs in understanding visual contents, leaving the gap between current embodied intelligence and large vision-language models (LVLM) . |
| Approach: | They propose to use a benchmark to evaluate LVLMs' spatial understanding of embodied environments to evaluate their ability to understand visual contents. |
| Outcome: | The proposed benchmark is derived from embodied scenes and covers 6 spatial relationships from an egocentric perspective. |
A Length-Extrapolatable Transformer (2023.acl-long)
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Yutao Sun, Li Dong, Barun Patra, Shuming Ma, Shaohan Huang, Alon Benhaim, Vishrav Chaudhary, Xia Song, Furu Wei
| Challenge: | Existing Transformers can only deal with the in-distribution size of inputs. |
| Approach: | They propose a relative position embedding to explicitly maximize attention resolution . they also use blockwise causal attention during inference for better resolution a . |
| Outcome: | The proposed model achieves strong performance in interpolation and extrapolation settings. |
UPPAM: A Unified Pre-training Architecture for Political Actor Modeling based on Language (2023.acl-long)
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| Challenge: | Existing studies have incorporated contextual information to better learn the representation of political actors for specific tasks. |
| Approach: | They propose to use statements to represent political actors and learn mapping from languages to representations using social networks and behaviors as self-constructed supervision. |
| Outcome: | The proposed model can be generalized to political actors and solve downstream tasks. |
Chain-of-Dictionary Prompting Elicits Translation in Large Language Models (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have shown surprisingly good performance in multilingual neural machine translation . yet, they struggle with translating low-resource languages. |
| Approach: | They propose a framework that chained multilingual dictionaries to elicit translation abilities for LLMs . they show that CoD can significantly improve LLM translation by evoking more information . |
| Outcome: | The proposed framework improves on ChatGPT and InstructGPT's translation abilities. |
Neural Document Summarization by Jointly Learning to Score and Select Sentences (P18-1)
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| Challenge: | Sentence scoring and sentence selection are two main steps in extractive document summarization systems. |
| Approach: | They propose an end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences. |
| Outcome: | The proposed framework outperforms the state-of-the-art summarization models on the CNN/Daily Mail dataset. |
Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References (2024.naacl-long)
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Tianyi Tang, Hongyuan Lu, Yuchen Jiang, Haoyang Huang, Dongdong Zhang, Xin Zhao, Tom Kocmi, Furu Wei
| Challenge: | Existing evaluation benchmarks with limited references may not accurately reflect the quality of the model’s hypotheses. |
| Approach: | They propose a method to enrich evaluation benchmarks by diversifying the expression of a single reference into multiple high-quality ones to cover the semantic space of the reference sentence as much as possible. |
| Outcome: | The proposed method can enhance evaluation benchmarks by diversifying the expression of reference into multiple high-quality ones to cover the semantic space of the reference sentence as much as possible. |
Incorporating Latent Meanings of Morphological Compositions to Enhance Word Embeddings (P18-1)
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| Challenge: | Existing word embedding methods learn semantic information at word level while neglecting meaningful inner structures of words like morphemes. |
| Approach: | They propose to use latent meanings of morphological compositions of words to train word embeddings. |
| Outcome: | The proposed models outperform baseline models on word similarity, syntactic analogy and text classification tasks. |
Consistency Regularization for Cross-Lingual Fine-Tuning (2021.acl-long)
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Bo Zheng, Li Dong, Shaohan Huang, Wenhui Wang, Zewen Chi, Saksham Singhal, Wanxiang Che, Ting Liu, Xia Song, Furu Wei
| Challenge: | Experimental results show that consistency regularization improves cross-lingual fine-tuning . pre-trained cross-linguistic models can transfer task-specific supervision from one language to the other . |
| Approach: | They propose to improve cross-lingual fine-tuning with consistency regularization . they use example consistency regularized to penalize prediction sensitivity to four types of data augmentations . |
| Outcome: | The proposed method improves cross-lingual fine-tuning across tasks . it can be generalized to other target languages without additional training . |
Trait Activation in Silicon: A Situation-Aware Framework for Psychologically Grounded Role-Playing (2026.acl-long)
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| Challenge: | Role-playing agents lack a deep understanding of complex human psychological mechanisms. |
| Approach: | They propose a situation-aware framework that decouples personality traits into bidirectional LoRA adapters. |
| Outcome: | Empirical results show that PD-LLM achieves superior performance in both static fidelity and dynamic adaptability. |
DA-Code: Agent Data Science Code Generation Benchmark for Large Language Models (2024.emnlp-main)
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Yiming Huang, Jianwen Luo, Yan Yu, Yitong Zhang, Fangyu Lei, Yifan Wei, Shizhu He, Lifu Huang, Xiao Liu, Jun Zhao, Kang Liu
| Challenge: | DA-Code is a code generation benchmark designed to assess LLMs on agent-based data science tasks. |
| Approach: | They propose a code generation benchmark specifically designed for LLMs on agent-based data science tasks. |
| Outcome: | The benchmark performs better than existing frameworks, but lacks accuracy . it is based on real-world data, and includes examples that cover a wide range of tasks . |
Calibrating LLM-Based Evaluator (2024.lrec-main)
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Yuxuan Liu, Tianchi Yang, Shaohan Huang, Zihan Zhang, Haizhen Huang, Furu Wei, Weiwei Deng, Feng Sun, Qi Zhang
| Challenge: | Existing models for large language models lack the ability to calibrate their outputs towards human preference. |
| Approach: | They propose a multi-stage, gradient-free approach to calibrate an LLM-based evaluator toward human preference. |
| Outcome: | The proposed approach improves correlation with expert evaluation on multiple text quality evaluation datasets. |
Multi-Stage Balanced Distillation: Addressing Long-Tail Challenges in Sequence-Level Knowledge Distillation (2024.findings-emnlp)
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| Challenge: | Knowledge distillation (KD) is a promising solution for large language models, but their deployment remains computationally expensive. |
| Approach: | They propose a framework which iteratively balances training data within a fixed computational budget and enables the transfer of knowledge from expensive teacher LLMs to smaller student models. |
| Outcome: | The proposed framework achieves state-of-the-art performance across diverse long-tailed datasets, enhancing both the efficiency and efficacy of the distilled models. |
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment (2021.acl-long)
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| Challenge: | Experimental results show that denoising word alignment improves cross-lingual transferability . most applications and resources are still English-centric, making non-English users hard to access. |
| Approach: | They propose to denoise word alignment as a cross-lingual pre-training task . they first self-label word alignments for parallel sentences and then mask tokens . |
| Outcome: | The proposed model improves cross-lingual transferability on token-level tasks, especially on question answering, and structured prediction. |
UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation (2023.emnlp-main)
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Daixuan Cheng, Shaohan Huang, Junyu Bi, Yuefeng Zhan, Jianfeng Liu, Yujing Wang, Hao Sun, Furu Wei, Weiwei Deng, Qi Zhang
| Challenge: | Large Language Models (LLMs) have impressive capabilities but need for task-specific prompt engineering can hinder their generalization. |
| Approach: | They propose a lightweight and versatile retriever that automatically retrieves prompts for a given zero-shot task input. |
| Outcome: | The proposed model is universally applicable across tasks and models . it mitigates hallucination problem in chatGPT, and it improves even the strongest LLMs. |
OpenUE: An Open Toolkit of Universal Extraction from Text (2020.emnlp-demos)
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Ningyu Zhang, Shumin Deng, Zhen Bi, Haiyang Yu, Jiacheng Yang, Mosha Chen, Fei Huang, Wei Zhang, Huajun Chen
| Challenge: | a large number of natural language processing tasks focus on token-level or sentence-level understandings. |
| Approach: | They propose an open-source and extensible toolkit for various extraction tasks . they deploy an online demo with restful APIs to support real-time extraction . |
| Outcome: | The proposed model can be used to extract information from text without training and deployment. |
IDEA: An Interpretable and Editable Decision-Making Framework for LLMs via Verbal-to-Numeric Calibration (2026.findings-acl)
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| Challenge: | Existing approaches fail to integrate domain expert insights beyond simple prompting. |
| Approach: | They propose a framework that extracts LLM decision knowledge into an interpretable parametric model over semantically meaningful factors. |
| Outcome: | Experiments show that IDEA outperforms DeepSeek R1 and GPT-5.2 in accuracy and accuracy. |
Event Detection from Social Media for Epidemic Prediction (2024.naacl-long)
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Tanmay Parekh, Anh Mac, Jiarui Yu, Yuxuan Dong, Syed Shahriar, Bonnie Liu, Eric Yang, Kuan-Hao Huang, Wei Wang, Nanyun Peng, Kai-Wei Chang
| Challenge: | Social media is an easy-to-access platform providing timely updates about societal trends and events. |
| Approach: | They propose a framework to extract epidemic-related events from social media posts to provide early warnings. |
| Outcome: | The proposed framework can detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue while existing models fail miserably. |
MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers (2021.findings-acl)
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| Challenge: | Existing work on deep self-attention distillation for natural language processing tasks is limited by computational resources and latency. |
| Approach: | They generalize deep self-attention distillation in MINILM by using only self- attention relation distillation for taskagnostic compression of pretrained Transformers. |
| Outcome: | The proposed model outperforms the state-of-the-art in a multilingual and multilingual teacher model. |
Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains (2021.findings-acl)
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| Challenge: | Large pre-trained models suffer from domain shift and are not optimal for specific domains. |
| Approach: | They propose a general approach to developing small, fast and effective pretrained models for specific domains by adapting off-the-shelf general pretrained model and performing task-agnostic knowledge distillation in target domains. |
| Outcome: | The proposed approach achieves better performance over the BERT BASE model in domain-specific tasks while 3.3 smaller and 5.1 faster than the BRT BASE. |
Breaking the Ceiling: Exploring the Potential of Jailbreak Attacks through Expanding Strategy Space (2025.findings-acl)
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| Challenge: | Existing methods to exploit black-box jailbreaks fail to capture key attack patterns . a novel framework decomposes jailbreak strategies into essential components . |
| Approach: | They propose a framework that decomposes jailbreak strategies into essential components and develops genetic-based optimization with intention evaluation mechanisms. |
| Outcome: | The proposed framework achieves 90% success rate on Claude-3.5, where prior methods completely fail . it also surpasses specialized safeguard models in evaluation accuracy . |
Neeko: Leveraging Dynamic LoRA for Efficient Multi-Character Role-Playing Agent (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have revolutionized open-domain dialogue agents but face challenges in multi-character role-playing (MCRP) scenarios. |
| Approach: | They propose a framework for efficient multi-character role-playing that employs a dynamic low-rank adapter strategy and distinct LoRA blocks for each character. |
| Outcome: | Neeko employs a dynamic low-rank adapter (LoRA) strategy, enabling it to adapt seamlessly to diverse characters. |
Leveraging Declarative Knowledge in Text and First-Order Logic for Fine-Grained Propaganda Detection (2020.emnlp-main)
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| Challenge: | Existing methods for fine-grained propaganda detection are not based on input-output data, but instead use declarative knowledge to detect propagandistic text fragments. |
| Approach: | They propose a method to inject declarative knowledge of fine-grained propaganda techniques into training data to get better representations of propagandistic texts. |
| Outcome: | The proposed method achieves superior performance on a large dataset for propaganda detection. |
UnifiedVisual: A Framework for Constructing Unified Vision-Language Datasets (2025.emnlp-main)
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Pengyu Wang, Shaojun Zhou, Chenkun Tan, Xinghao Wang, Wei Huang, Zhen Ye, Zhaowei Li, Botian Jiang, Dong Zhang, Xipeng Qiu
| Challenge: | Existing datasets address understanding and generation in isolation, limiting the performance of unified vision large language models. |
| Approach: | They propose a dataset that facilitates mutual enhancement between multimodal understanding and generation. |
| Outcome: | The proposed framework integrates diverse visual and textual inputs and outputs, enabling comprehensive cross-modal reasoning and precise text-to-image alignment. |
Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback (2023.findings-emnlp)
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| Challenge: | Experimental results prove that language models can learn from human feedback better, irrespective of sequence length . emergence of length bias often induces the model to favor longer outputs . |
| Approach: | They propose to separate reward modeling from the influence of sequence length by using the Product-of-Experts technique. |
| Outcome: | The proposed approach shows that language models perform better regardless of sequence length . the main expert is focused on understanding human intents, while the biased expert targets the identification and capture of length bias. |
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)
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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang
| Challenge: | Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored. |
| Approach: | They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities. |
| Outcome: | The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities. |
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning (2021.naacl-main)
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| Challenge: | a few-shot text classification task requires a large number of output classes, with few training examples per class. |
| Approach: | They propose a data augmentation technique suitable for training with limited data for few-shot, highly-multiclass text classification scenarios. |
| Outcome: | The proposed technique improves performance on four classification tasks by 3.0% on average. |
Beyond Surface Features: Advancing Medical Vision-Language Alignment via Dynamic Evidence-Guided Preference Optimization (2026.acl-long)
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Zixuan Huang, Zhihong Zhu, Xiaolong Liu, Yanchao Hao, Manman Zhang, Zheng Wei, Bowen Xing, Xian Wu, Ye Li, Fen Miao, Yefeng Zheng
| Challenge: | Existing preference-based methods for medical large vision-Language Models face limitations in medical settings . existing methods are limited by overfitting to superficial cues and pseudo convergence of the preference signal. |
| Approach: | They propose a framework that enables evidence-aware and adaptive preference learning for Med-LVLMs. |
| Outcome: | The proposed framework improves evidence-aware and adaptive preference learning for Med-LVLMs. |
Multi-step Jailbreaking Privacy Attacks on ChatGPT (2023.findings-emnlp)
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| Challenge: | With the rapid evolution of large language models (LLMs), many downstream NLP tasks can be well solved given appropriate prompts. |
| Approach: | They propose to integrate ChatGPT and Bing GPT3 into their applications to create a set of LLMs that can be used to generate NLP tasks with appropriate prompts. |
| Outcome: | The proposed models can be zero-shot or few-shot learners to solve specified tasks and can even be zero or few shot learners. |
TRIPS: Efficient Vision-and-Language Pre-training with Text-Relevant Image Patch Selection (2022.emnlp-main)
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| Challenge: | Existing vision-and-language pre-training models suffer from long visual sequences . experimental results show that TRIPS gains a speedup of 40% over previous similar VLP models . |
| Approach: | They propose an efficient vision-and-language pre-training model with text-relevant image patch selection, TRIPS, which reduces the visual sequence progressively with a text-guided patch-selection layer in the visual backbone for efficient training and inference. |
| Outcome: | The proposed model can speed up training and inference by 40% over previous models. |
MM-ChatAlign: A Novel Multimodal Reasoning Framework based on Large Language Models for Entity Alignment (2024.findings-emnlp)
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| Challenge: | Existing MMEA methods rely on knowledge representation learning (KRL) to measure the similarity of entity embeddings. |
| Approach: | They propose a framework that utilizes the visual reasoning abilities of MLLMs for multimodal entity alignment. |
| Outcome: | The proposed framework integrates the visual reasoning abilities of MLLMs for multimodal entity alignment. |
Distill Visual Chart Reasoning Ability from LLMs to MLLMs (2025.findings-emnlp)
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Wei He, Zhiheng Xi, Wanxu Zhao, Xiaoran Fan, Yiwen Ding, Zifei Shan, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | a new method for generating chart annotations is proposed to improve visual reasoning in multimodal large language models. |
| Approach: | They propose a code-as-intermediary translation method for distilling visual reasoning abilities from LLMs to MLLMs. |
| Outcome: | The proposed method is cost-effective, efficient and scalable. |
Layer-Aware Task Arithmetic: Disentangling Task-Specific and Instruction-Following Knowledge (2025.findings-emnlp)
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Yan-Lun Chen, Yi-Ru Wei, Chia-Yi Hsu, Chia-Mu Yu, Chun-Ying Huang, Ying-Dar Lin, Yu-Sung Wu, Wei-Bin Lee
| Challenge: | Large language models (LLMs) demonstrate strong task-specific capabilities through fine-tuning, but merging multiple fine- tuned models often leads to degraded performance due to overlapping instruction-following components. |
| Approach: | They propose a layer-wise approach that assigns layer-specific weights to task vectors based on their alignment with instruction-following or task-specific components. |
| Outcome: | The proposed approach outperforms existing methods in learning and forgetting tasks while preserving overall model utility. |
KNSE: A Knowledge-aware Natural Language Inference Framework for Dialogue Symptom Status Recognition (2023.findings-acl)
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| Challenge: | Existing frameworks for symptom status recognition in doctor-patient dialogues are inadequate. |
| Approach: | They propose a framework for symptom status recognition that formalizes a natural language inference task . they generate knowledge about the symptom and a hypothesis about its status for each symptom . |
| Outcome: | The proposed framework outperforms baselines and has advantages in cross-disease and cross-symptom scenarios. |
Training a Better Chinese Spelling Correction Model via Prior-knowledge Guided Teacher (2024.findings-acl)
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| Challenge: | Chinese Spelling Correction models are prone to over-correct and poor generalization for error patterns outside the standard distribution. |
| Approach: | They propose a teacher network guided by prior knowledge for distillation learning of CSC models. |
| Outcome: | The proposed method significantly enhances the CSC model’s language modeling capabilities, crucial for minimizing over-correction. |
QRMeM: Unleash the Length Limitation through Question then Reflection Memory Mechanism (2024.findings-emnlp)
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| Challenge: | Existing methods for processing large textual content face insufficient adaptation to task-specific needs and missing multi-segmentation relationships. |
| Approach: | They propose a question then reflection memory mechanism which integrates a dual-structured memory pool and a structured graph guidance to facilitate a reflective trial-and-error approach for navigating and identifying relevant segments. |
| Outcome: | The proposed model achieves superior performance on multiple-choice questions and multi-doc QA. |
SEGO: Sequential Subgoal Optimization for Mathematical Problem-Solving (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, including mathematical problem-solving. |
| Approach: | They propose a framework that connects the subgoal breakdown process and the probability of solving problems by identifying better subgoals with theoretical guarantees. |
| Outcome: | The proposed framework outperforms existing methods on two benchmarks, GSM8K and MATH, highlighting the potential of SEGO in AI-driven mathematical problem-solving. |
Automatic Term Name Generation for Gene Ontology: Task and Dataset (2020.findings-emnlp)
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Yanjian Zhang, Qin Chen, Yiteng Zhang, Zhongyu Wei, Yixu Gao, Jiajie Peng, Zengfeng Huang, Weijian Sun, Xuanjing Huang
| Challenge: | Gene Ontology (GO) terms are used to describe gene function in biology and bio-medicine. |
| Approach: | They propose a task to generate term names for GO and build a large-scale benchmark dataset. |
| Outcome: | The proposed model outperforms baselines by incorporating the relations between genes, words and terms for term name generation. |
Privacy Evaluation Benchmarks for NLP Models (2024.findings-emnlp)
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| Challenge: | Several kinds of privacy attacks are studied in depth, but they are non-systematic and lack a comprehensive understanding of the impact caused by the attacks. |
| Approach: | They propose a privacy attack and defense evaluation benchmark in the field of NLP . they propose an improved attack method and a chained framework for privacy attacks . |
| Outcome: | The proposed framework can be chained to achieve a higher-level attack objective. |
AgentMark: Utility-Preserving Behavioral Watermarking for Agents (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have improved text generation and reasoning. |
| Approach: | They propose a behavioral watermarking framework that embeds multi-bit identifiers into planning decisions while preserving utility. |
| Outcome: | The proposed framework embeds multi-bit provenance into planning decisions while preserving utility. |
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning (D18-1)
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| Challenge: | Existing systems for automatic essay scoring are trained to predict the score of each essay at a time without considering rating schema. |
| Approach: | They propose a reinforcement learning framework that incorporates quadratic weighted kappa as guidance to optimize the scoring system. |
| Outcome: | Experiments on benchmark datasets show the proposed framework is effective. |
EvoWiki: Evaluating LLMs on Evolving Knowledge (2025.acl-long)
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Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao
| Challenge: | Existing knowledge evolution benchmarks are static and fail to capture the evolving nature of LLMs and knowledge. |
| Approach: | They propose an evolving dataset that categorizes information into stable, evolved, and uncharted states. |
| Outcome: | The proposed dataset is auto-updatable and enables evaluation of continuously changing knowledge and newly released LLMs. |
On Synthetic Data for Back Translation (2022.naacl-main)
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| Challenge: | Existing studies on back translation (BT) focus on beam search or random sampling . a new method to generate synthetic data with a backward model is proposed to improve BT performance. |
| Approach: | They propose a method to generate synthetic data to trade off quality and importance factors . back translation (BT) is one of the most significant technologies in NMT research fields . |
| Outcome: | The proposed method outperforms the baseline methods on WMT14 DE-EN, EN-DE, and RU-EN benchmark tasks. |
A Progressive Framework for Role-Aware Rumor Resolution (2022.coling-1)
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| Challenge: | Existing methods for rumor resolution ignore intrinsic propagation mechanisms of rumors and present poor adaptive ability when unprecedented news emerges. |
| Approach: | They propose to identify triggering posts and exploit their characteristics to facilitate rumor verification. |
| Outcome: | The proposed model and scheme exploits rumor diffusion patterns and linguistic features to facilitate verification. |
MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation (2025.naacl-long)
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Jinsheng Huang, Liang Chen, Taian Guo, Fu Zeng, Yusheng Zhao, Bohan Wu, Ye Yuan, Haozhe Zhao, Zhihui Guo, Yichi Zhang, Jingyang Yuan, Wei Ju, Luchen Liu, Tianyu Liu, Baobao Chang, Ming Zhang
| Challenge: | Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, but many benchmarks suffer from systematic biases. |
| Approach: | They propose a benchmark to avoid Type-I errors by creating one perception question and one knowledge anchor question through a meticulous annotation process. |
| Outcome: | The proposed benchmark avoids Type-I errors while maintaining reliability of MCQ evaluations. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
Prompt-based Zero-shot Text Classification with Conceptual Knowledge (2023.acl-srw)
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| Challenge: | Existing approaches to pre-training language models rely on verbalizers to translate the predicted vocabulary to task-specific labels. |
| Approach: | They propose a framework that incorporates conceptual knowledge for text classification in the extreme zero-shot setting. |
| Outcome: | The proposed framework outperforms prompt-based approaches on four widely-used datasets for sentiment analysis and topic detection on the same experimental settings. |
Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting (2025.findings-acl)
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Hao Feng, Shu Wei, Xiang Fei, Wei Shi, Yingdong Han, Lei Liao, Jinghui Lu, Binghong Wu, Qi Liu, Chunhui Lin, Jingqun Tang, Hao Liu, Can Huang
| Challenge: | Current document image parsing solutions rely on specialized models or generate content autoregressively. |
| Approach: | They propose a multimodal document image parsing model that integrates specialized models with autogeneous content generation. |
| Outcome: | The proposed model achieves state-of-the-art performance across diverse page-level and element-level settings while ensuring superior efficiency. |
LAiW: A Chinese Legal Large Language Models Benchmark (2025.coling-main)
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Yongfu Dai, Duanyu Feng, Jimin Huang, Haochen Jia, Qianqian Xie, Yifang Zhang, Weiguang Han, Wei Tian, Hao Wang
| Challenge: | Xie et al., 2023) show that large language models (LLMs) can generate legal text, but lack the legal syllogism . legal experts are cautious about their practical application due to the opaque nature of the LLMs. |
| Approach: | They propose a Chinese legal LLM benchmark structured around the legal syllogism . they evaluate LLMs across three levels of capability, each reflecting a more complex stage of legal . |
| Outcome: | The proposed benchmark identifies that LLMs lack the legal syllogism, which hinders trust and understanding from legal experts. |
DPF-CM: A Data Processing Framework with Privacy-Preserving Vector Databases for Chinese Medical LLMs Training and Deployment (2025.findings-emnlp)
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| Challenge: | a new framework for training data processing for Chinese medical language models is proposed . experimental results show that the framework significantly improves model accuracy . |
| Approach: | They propose a data processing framework for Chinese medical language models training and deployment . the framework is based on a question-oriented model training strategy and privacy preservation . |
| Outcome: | The proposed framework significantly improves model accuracy and reduces privacy leakage by 27%. |
QCRD: Quality-guided Contrastive Rationale Distillation for Large Language Models (2025.emnlp-main)
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| Challenge: | Recent research has focused on smaller, task-specific models enhanced by distilling knowledge from LLMs, but the diversity and quality of negative knowledge remains understudied. |
| Approach: | They propose a quality-guided contrastive rationale distillation framework that aims to enhance reasoning capabilities through contrastive knowledge learning. |
| Outcome: | The proposed method consistently outperforms existing distillation techniques yielding higher-quality rationales. |
MASTER: A Multi-Agent System with LLM Specialized MCTS (2025.naacl-long)
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Bingzheng Gan, Yufan Zhao, Tianyi Zhang, Jing Huang, Li Yusu, Shu Xian Teo, Changwang Zhang, Wei Shi
| Challenge: | Large Language Models (LLMs) are increasingly being explored for problem-solving tasks . their strategic planning capability is often viewed with skepticism due to their limited planning capabilities. |
| Approach: | They propose a framework that coordinates agent recruitment and communication through LLM specialized MCTS. |
| Outcome: | The proposed framework achieves 76% accuracy on HotpotQA and 80% on WebShop . it relies on extensive sampling simulations to approximate the true reward distribution . |
VECO: Variable and Flexible Cross-lingual Pre-training for Language Understanding and Generation (2021.acl-long)
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| Challenge: | Existing work in multilingual pretraining relies on the shared vocabulary and bilingual contexts to encourage the correlation across languages. |
| Approach: | They propose to plug a cross-attention module into a Transformer encoder to explicitly build the interdependence between languages. |
| Outcome: | The proposed model outperforms existing models on XTREME and English-to-French translation datasets. |
InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training (2021.naacl-main)
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Zewen Chi, Li Dong, Furu Wei, Nan Yang, Saksham Singhal, Wenhui Wang, Xia Song, Xian-Ling Mao, Heyan Huang, Ming Zhou
| Challenge: | Existing methods for learning cross-lingual representations are lacking in the field of NLP. |
| Approach: | They propose a framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts. |
| Outcome: | The proposed approach improves cross-lingual transferability on benchmarks. |
AdapThink: Adaptive Thinking Preferences for Reasoning Language Models (2026.findings-acl)
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| Challenge: | Recent research has highlighted a significant inefficiency associated with the slow thinking paradigm . models often overthink simple tasks while underthinking complex challenges . |
| Approach: | They propose a framework for adaptive reasoning preference control that dynamically adjusts reflection preferences based on group-level distributional statistics of reasoning length and reflection intensity. |
| Outcome: | The proposed framework reduces average response length by 17.1%-21.4% while improving performance by 6.12-6.59 points under 32K token budgets. |
Large Margin Neural Language Model (D18-1)
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| Challenge: | Conventionally, neural language models are trained by minimizing perplexity (PPL) on grammatical sentences. |
| Approach: | They propose a large margin criterion for training neural language models by minimizing perplexity on grammatical sentences and propose enlarged margins for task-specific training. |
| Outcome: | The proposed method gains up to 1.1 WER reduction for speech recognition and 1.0 BLEU increase for machine translation. |