Papers by Zhu Liu
RETAIL: Towards Real-world Travel Planning for Large Language Models (2025.emnlp-main)
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| Challenge: | Existing travel planning systems assume users provide explicit queries, limiting their practical utility. |
| Approach: | They propose a dataset RETAIL which supports decision-making for implicit queries while covering explicit queries. |
| Outcome: | The proposed model achieves a 1.0% pass rate, suggesting real-world travel planning remains challenging. |
Enabling Self-Improving Agents to Learn at Test Time With Human-In-The-Loop Guidance (2025.emnlp-industry)
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Yufei He, Ruoyu Li, Alex Chen, Yue Liu, Yulin Chen, Yuan Sui, Cheng Chen, Yi Zhu, Luca Luo, Frank Yang, Bryan Hooi
| Challenge: | Existing large language model (LLM) agents are unable to adapt to changing domain knowledge and rules. |
| Approach: | They propose an LLM agent framework that continuously learns updated domain knowledge at test time. |
| Outcome: | The proposed agent improves on a customer due diligence name screening task on . the agent learns updated domain knowledge at test time. |
Cognitive-Uncertainty Guided Knowledge Distillation for Accurate Classification of Student Misconceptions (2026.findings-acl)
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| Challenge: | Existing methods for identifying student misconceptions overlook students' reasoning processes, authors report . |
| Approach: | They propose a knowledge distillation framework that mines high-value samples from existing data. |
| Outcome: | The proposed framework outperforms sota LLM and standard fine-tuned 72B models on cross-topic tests. |
Chinese SafetyQA: A Safety Short-form Factuality Benchmark for Large Language Models (2025.acl-long)
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Yingshui Tan, Boren Zheng, Baihui Zheng, Kerui Cao, Huiyun Jing, Jincheng Wei, Jiaheng Liu, Yancheng He, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang
| Challenge: | Large language models have created significant safety concerns . factuality ability is crucial in determining whether they can be deployed and applied safely and compliantly within specific regions. |
| Approach: | They propose a benchmark to evaluate the factuality of large language models in China . they evaluate the models' ability to provide accurate and reliable information . |
| Outcome: | The proposed benchmark evaluates the factuality abilities of existing LLMs and compares them to LLM abilities. |
Tracing the Roots: A Multi-Agent Framework for Uncovering Data Lineage in Post-Training LLMs (2026.acl-long)
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Yu Li, Xiaoran Shang, Qizhi Pei, Yun Zhu, Xin Gao, Honglin Lin, Zhanping Zhong, Zhuoshi Pan, Zheng Liu, Xiaoyang Wang, Conghui He, Dahua Lin, Feng Zhao, Lijun Wu
| Challenge: | High-quality post-training data is the primary engine driving LLM capabilities . datasets are often treated as isolated artifacts, overlooking their true developmental context . |
| Approach: | They propose a framework to reconstruct the evolutionary graph of dataset development using data lineage. |
| Outcome: | The proposed framework characterizes domain-specific structural patterns in Math-oriented datasets and general-domain corpora. |
Want To Reduce Labeling Cost? GPT-3 Can Help (2021.findings-emnlp)
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| Challenge: | Data annotation is labor-intensive and time-consuming for many NLP tasks. |
| Approach: | They propose to use GPT-3 to train models which are deployed for inference . they propose to combine pseudo labels from GPT3 with human labels . |
| Outcome: | The proposed method can be generalizable to many practical applications. |
Step-level Verifier-guided Hybrid Test-Time Scaling for Large Language Models (2025.emnlp-main)
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Kaiyan Chang, Yonghao Shi, Chenglong Wang, Hang Zhou, Chi Hu, Xiaoqian Liu, Yingfeng Luo, Yuan Ge, Tong Xiao, JingBo Zhu
| Challenge: | Recent training-based TTS methods, such as continued reinforcement learning, have surged in popularity, while training-free TTS approaches are gradually fading from prominence. |
| Approach: | They propose a fine-grained sequential scaling method guided by process verification that integrates training-free TTS methods with other classical parallel scaling methods at the step level. |
| Outcome: | Experiments on five instruction-tuned large language models (LLMs) show that training-free TTS methods can extend reasoning performance boundaries. |
ParaTag: A Dataset of Paraphrase Tagging for Fine-Grained Labels, NLG Evaluation, and Data Augmentation (2022.emnlp-main)
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| Challenge: | Existing datasets only annotate a binary label for each sentence pair. Existing models only annnotate binary labels for each phrase pair. |
| Approach: | They propose a novel binary paraphrase classification task that annotates the degree of paraphrase between sentences and a new annotation schema that labels the minimum spans of tokens in a sentence that don't have the corresponding paraphrases in the other sentence. |
| Outcome: | The proposed dataset can be used to train an automatic scorer for language generation evaluation. |
SILO-BENCH: A Scalable Environment for Evaluating Distributed Coordination in Multi-Agent LLM Systems (2026.acl-long)
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Yuzhe Zhang, Feiran Liu, Yi Shan, Xinyi Huang, Xin Yang, Yueqi Zhu, Xuxin Cheng, Cao Liu, Ke Zeng, Terry Jingchen Zhang, Wenyuan Jiang
| Challenge: | Existing benchmarks conflate coordination ability with role-based priors. |
| Approach: | They propose a role-free benchmark for evaluating free-form collaboration under information silos. |
| Outcome: | The proposed benchmark systematically probes coordination capabilities under information silos using 54 configurations and 3 frontier LLMs. |
Adaptive Spatial and Temporal Redundancy Optimization for Efficient Reasoning in Large Language Models (2026.acl-long)
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Tianle Chen, Pengyu Cheng, Qiyuan Zhu, Jiacheng Wang, Bei Liu, Hao Gu, Ruijie Shen, Xiaofeng Hou, Sirui Han, Jiacheng Liu
| Challenge: | Existing research to improve CoT efficiency falls into three categories, each with distinct limitations. |
| Approach: | They propose a training-free framework that addresses both dimensions of CoT reasoning by applying a progressive precision reduction strategy coupled with an entropy-based confidence mechanism for adaptive termination. |
| Outcome: | Empirical results show that the proposed framework achieves 11.3 efficiency gain without compromising accuracy. |
PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents (2024.eacl-long)
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| Challenge: | Using chain-of-thought prompting, large language models perform better on complex reasoning tasks. |
| Approach: | They propose a prompting framework that decomposes a question into a sequence of actions and executes them over the document to obtain the answer. |
| Outcome: | The proposed framework outperforms zero-shot and chain-of-thought prompting on a QuALITY dataset . it proposes a plan based on actions mined from a training set and executes it step by step . |
CART: A Generative Cross-Modal Retrieval Framework With Coarse-To-Fine Semantic Modeling (2025.acl-long)
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Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, Zhenhua Dong, Zhou Zhao
| Challenge: | Cross-modal retrieval tasks are used to retrieve data from one modality or another based on a query from another modality. |
| Approach: | They propose a generative cross-modal retrieval framework based on coarse-to-fine semantic modeling . they propose combining K-Means and RQ-VAE to discretize multimodal data into token sequences that support autoregressive generation. |
| Outcome: | The proposed framework achieves excellent performance and efficiency in multimodal retrieval tasks. |
Span-level Aspect-based Sentiment Analysis via Table Filling (2023.acl-long)
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| Challenge: | Existing methods to analyze aspect-based sentiment analysis focus on word-level dependencies between aspect and opinion expressions. |
| Approach: | They propose a span-level ABSA model which considers consistency of multi-word opinion expressions at the span- level. |
| Outcome: | The proposed model can be used to identify the sentiment polarity of a given aspect . it is based on a table filling method and a regularizer to guarantee consistency . |
HiCoLoRA: Addressing Context-Prompt Misalignment via Hierarchical Collaborative LoRA for Zero-Shot DST (2026.findings-acl)
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| Challenge: | Existing approaches to zero-shot Dialog State Tracking (zs-DST) are inadequate to generalize to new domains without extensive training. |
| Approach: | They propose a framework that enhances zero-shot slot inference through robust prompt alignment. |
| Outcome: | Experiments on multi-domain datasets show that HiCoLoRA outperforms baselines, achieving SOTA in zs-DST. |
UAQFact: Evaluating Factual Knowledge Utilization of LLMs on Unanswerable Questions (2025.findings-acl)
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| Challenge: | Existing datasets to assess LLMs' performance on unanswerable questions lack factual knowledge support. |
| Approach: | They propose a bilingual unanswerable question dataset with auxiliary factual knowledge created from a Knowledge Graph and two new tasks to measure LLMs' ability to utilize internal and external factual information. |
| Outcome: | The proposed datasets show that LLMs do not consistently perform well even when they have factual knowledge stored. |
Do Large Language Models excel in Complex Logical Reasoning with Formal Language? (2025.emnlp-main)
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| Challenge: | Existing studies on LLMs have focused on formal language, but evaluations of their performance are limited. |
| Approach: | They propose to use a formal language to evaluate LLMs across logical reasoning problems using formal languages. |
| Outcome: | The proposed model outperforms Instruct models in three dimensions, taxonomy of tasks, and format of trajectories, and achieves the best generalization performance across other languages. |
Zero-shot Sharpness-Aware Quantization for Pre-trained Language Models (2023.emnlp-main)
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| Challenge: | Existing zero-shot quantization methods are based on overfitting problem in adversarial learning process, leading to sub-optimal performance. |
| Approach: | They propose a zero-shot sharpness-aware quantization framework for the quantization of various PLMs by optimizing a minimax problem. |
| Outcome: | The proposed framework can achieve significant performance gains on discriminative and generative PLMs. |
PricingLogic: Evaluating LLMs Reasoning on Complex Tourism Pricing Tasks (2025.emnlp-main)
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Yunuo Liu, Dawei Zhu, Zena Al-Khalili, Dai Cheng, Yanjun Chen, Dietrich Klakow, Wei Zhang, Xiaoyu Shen
| Challenge: | Recent advances in Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, such as code generation, mathematical problem-solving, and general-purpose human instruction following. |
| Approach: | They propose to use large language models to process questions expressed in natural language to automate tourism-booking prices when multiple, overlapping farerules apply. |
| Outcome: | The proposed model can automate tourism-booking prices when multiple, overlapping farerules apply. |
USB: A COMPREHENSIVE AND UNIFIED SAFETY EVALUATION BENCHMARK FOR MULTIMODAL LARGE LANGUAGE MODELS (2026.acl-long)
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Baolin Zheng, Guanlin Chen, Qingyang Teng, Hongqiong Zhong, Yingshui Tan, Zhendong Liu, Weixun Wang, Jiaheng Liu, Jian Yang, Huiyun Jing, Jincheng Wei, Wenbo Su, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang
| Challenge: | Existing safety benchmarks fail to provide reliable assessments due to limited risk coverage, insufficient scale and the oversight of complex modality combinations. |
| Approach: | They propose a framework that covers 61 risk categories across four modality interactions to address this gap. |
| Outcome: | The proposed framework covers 61 risk categories across four distinct modality interactions. |
CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation (2024.acl-long)
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Weixiang Yan, Haitian Liu, Yunkun Wang, Yunzhe Li, Qian Chen, Wen Wang, Tingyu Lin, Weishan Zhao, Li Zhu, Hari Sundaram, Shuiguang Deng
| Challenge: | Existing benchmarks for evaluating the code understanding and generation capacities of Large Language Models are insufficient . existing benchmarks focus on a narrow range of popular programming languages and specific tasks . |
| Approach: | They propose an execution-based, multilingual, multitask evaluation benchmark for LLMs . they evaluate coding performance from three dimensions: length, difficulty, efficiency . |
| Outcome: | The proposed benchmark covers 43 programming languages and eight coding tasks. |
Lookahead Q-Cache: Achieving More Consistent KV Cache Eviction via Pseudo Query (2025.emnlp-main)
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| Challenge: | Existing KV cache eviction methods prune tokens using prefilling-stage attention scores, causing inconsistency with actual inference queries. |
| Approach: | They propose a lookahead q-cache framework that generates low-cost pseudo lookaheaded queries to better approximate the true decoding-stage queries. |
| Outcome: | The proposed framework outperforms existing methods on LongBench and Needle-in-a-Haystack benchmarks and can be flexibly combined to yield further improvements. |
Leveraging Unit Language Guidance to Advance Speech Modeling in Textless Speech-to-Speech Translation (2025.findings-acl)
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Yuhao Zhang, Xiangnan Ma, Kaiqi Kou, Peizhuo Liu, Weiqiao Shan, Benyou Wang, Tong Xiao, Yuxin Huang, Zhengtao Yu, JingBo Zhu
| Challenge: | Existing textless speech-to-speech translation models have two main challenges: 1) learning cross-modal features and 2) learning alignment of difference languages in long sequences. |
| Approach: | They propose a unit language to overcome two main modeling challenges . they propose task prompt modeling to utilize the unit language in guiding the modeling process. |
| Outcome: | The proposed language improves over a strong baseline and achieves comparable performance to models trained with text. |
Shared-Private Bilingual Word Embeddings for Neural Machine Translation (P19-1)
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| Challenge: | Word embedding is central to neural machine translation, but indirectly interfaces with other layers, making them comparatively isolated. |
| Approach: | They propose a shared-private bilingual word embedding which gives a closer relationship between the source and target embedders and reduces the number of model parameters. |
| Outcome: | The proposed model improves on 5 language pairs belonging to 6 different language families and written in 5 different alphabets and significantly reduces model parameters. |
Hierarchical Memory Organization for Wikipedia Generation (2025.acl-long)
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Eugene J. Yu, Dawei Zhu, Yifan Song, Xiangyu Wong, Jiebin Zhang, Wenxuan Shi, Xiaoguang Li, Qun Liu, Sujian Li
| Challenge: | Existing methods for generating Wikipedia articles do not utilize memory directly for outline generation. |
| Approach: | They propose a method to generate Wikipedia articles autonomously by leveraging a hierarchical memory architecture. |
| Outcome: | The proposed framework outperforms baseline methods in producing informative and reliable articles. |
Large Language Models are not Fair Evaluators (2024.acl-long)
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Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Lingpeng Kong, Qi Liu, Tianyu Liu, Zhifang Sui
| Challenge: | Existing evaluation frameworks that use large language models as referees are insufficient for accurately assessing their alignment with human intent. |
| Approach: | They propose a calibration framework to address positional bias in large language models as evaluators by manually annotating the “win/tie/lose” outcomes of responses from ChatGPT and Vicuna-13B in the Vicun A Benchmark’s question prompt. |
| Outcome: | The proposed framework alleviates evaluation bias, resulting in closer alignment with human judgments. |
CoRanking: Collaborative Ranking with Small and Large Ranking Agents (2025.findings-emnlp)
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| Challenge: | Listwise ranking based on Large Language Models (LLMs) has achieved state-of-the-art performance in Information Retrieval (IR) however, their effectiveness often depends on LLMs with massive parameter scales and computationally expensive sliding window processing, leading to substantial efficiency bottlenecks. |
| Approach: | They propose a Collaborative Ranking framework (CoRanking) for LLM-based listwise ranking based on large language models with massive parameter scales and computationally expensive sliding window processing. |
| Outcome: | The proposed framework reduces ranking latency by approximately 70% while improving effectiveness compared to the standalone large reranker. |
DraDDP: A Multimodal Multi-Party Dialogue Discourse Parsing Dataset (2026.findings-acl)
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| Challenge: | Existing studies on multi-party dialogue discourse parsing focus on textual modality and two-party dialog . et al., 2016) focused on text-based discourse parses, ignoring the complexity and richness of multimodal interactions in real-world scenarios. |
| Approach: | They construct the first publicly available English multimodal dataset for multi-party dialogue discourse parsing based on American TV dramas. |
| Outcome: | The proposed dataset contains 495 dialogue segments with 6,374 utterances and 9.1 hours of parallel video content, covering rich multi-party interaction scenarios. |
Cross Copy Network for Dialogue Generation (2020.emnlp-main)
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| Challenge: | Despite the success of sequence-to-sequence models, dialogue logics are often ignored. |
| Approach: | They propose a network architecture to explore the current dialog context and similar dialogue instances’ logical structure simultaneously. |
| Outcome: | The proposed network architecture is superior to existing state-of-the-art models. |
Multi-turn Response Selection using Dialogue Dependency Relations (2020.emnlp-main)
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| Challenge: | Existing models for multi-turn response selection ignore the dependencies between the turns. |
| Approach: | They propose a dialogue extraction algorithm to transform a dialog history into threads based on their dependency relations. |
| Outcome: | The proposed model outperforms the state-of-the-art models on DSTC7 and DSTF8* with competitive results on UbuntuV2 . |
Retrieval, Analogy, and Composition: A framework for Compositional Generalization in Image Captioning (2021.findings-emnlp)
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| Challenge: | Existing approaches fail to generalize well to concepts that are not observed during training. |
| Approach: | They propose a framework that revolves around probing several similar image caption training instances and performing analogical reasoning over relevant entities in retrieved prototypes. |
| Outcome: | The proposed framework improves on the widely used image captioning benchmarks and on composition-related evaluation metrics. |
Opinion Summarization by Weak-Supervision from Mix-structured Data (2022.emnlp-main)
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| Challenge: | Existing methods for opinion summarization of multiple reviews lack reference summaries . OAs and ISs are often mismatched between review input and summary . |
| Approach: | They propose a method to generate mixed-structured synthetic training data for opinion summarization. |
| Outcome: | The proposed method outperforms existing methods on Yelp, Amazon and RottenTomatos datasets. |
Distilled Dual-Encoder Model for Vision-Language Understanding (2022.emnlp-main)
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| Challenge: | Experimental results show that the proposed cross-modal attention distillation is crucial to the success of our framework. |
| Approach: | They propose a framework that distills knowledge of fusion-encoder teacher into dual-encoding student model. |
| Outcome: | The proposed model is competitive with the fusion-encoder teacher model in performance, but suffers from a lack of deep cross-modal interactions. |
RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework (2025.acl-long)
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Kunlun Zhu, Yifan Luo, Dingling Xu, Yukun Yan, Zhenghao Liu, Shi Yu, Ruobing Wang, Shuo Wang, Yishan Li, Nan Zhang, Xu Han, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing evaluation metrics for RAG systems are lacking due to high costs of data construction and lack of factual accuracy. |
| Approach: | They propose a framework to evaluate RAG systems in specialized scenarios . they propose three new metrics to evaluate LLM-generated responses . |
| Outcome: | The proposed framework outperforms zero-shot and one-shot methods in terms of clarity, safety, conformity, and richness of generated samples. |
AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)
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Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified. |
| Approach: | They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 . |
| Outcome: | Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 . |
IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation (2021.acl-demo)
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| Challenge: | Automated Essay Assessment (AEA) aims to judge students’ writing proficiency in an automatic way. |
| Approach: | They propose to use Chinese AEA system IFlyEssayAssess to evaluate essays written by native Chinese students from primary and junior schools. |
| Outcome: | The proposed system provides application services for essay scoring, review generation, recommendation, and explainable analytical visualization. |
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. |
Towards a Unified Multi-Dimensional Evaluator for Text Generation (2022.emnlp-main)
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Ming Zhong, Yang Liu, Da Yin, Yuning Mao, Yizhu Jiao, Pengfei Liu, Chenguang Zhu, Heng Ji, Jiawei Han
| Challenge: | Existing evaluation frameworks for natural language generation are dominated by similarity-based metrics. |
| Approach: | They propose a multi-dimensional evaluator for natural language generation that integrates multiple dimensions into one evaluer. |
| Outcome: | The proposed evaluator improves on three typical NLG tasks and improves with external knowledge. |
Ambiguity Meets Uncertainty: Investigating Uncertainty Estimation for Word Sense Disambiguation (2023.findings-acl)
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| Challenge: | Existing supervised methods treat word sense disambiguation as a classification task but ignore uncertainty estimation (UE) in the real-world setting, the data is always noisy and out of distribution. |
| Approach: | They propose to use word sense disambiguation to determine an appropriate sense for a word given its context to determine the most appropriate sense. |
| Outcome: | The proposed model reflects data uncertainty satisfactorily but underestimates model uncertainty. |
PopAlign: Diversifying Contrasting Patterns for a More Comprehensive Alignment (2025.acl-long)
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Zekun Moore Wang, Shenzhi Wang, King Zhu, Jiaheng Liu, Ke Xu, Jie Fu, Wangchunshu Zhou, Wenhao Huang
| Challenge: | Typical approaches to training large language models rely on limited contrasting patterns . contrasting data is limited and models are susceptible to harmful response tendencies . |
| Approach: | They propose a framework that integrates contrasting patterns across the prompt, model, and pipeline levels. |
| Outcome: | The proposed framework outperforms existing methods in the comparison of RQ1 and RQ2 . the proposed framework significantly outperformed existing methods, leading to more comprehensive alignment. |
RepoDebug: Repository-Level Multi-Task and Multi-Language Debugging Evaluation of Large Language Models (2025.findings-emnlp)
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Jingjing Liu, Zeming Liu, Zihao Cheng, Mengliang He, Xiaoming Shi, Yuhang Guo, Xiangrong Zhu, Yuanfang Guo, Yunhong Wang, Haifeng Wang
| Challenge: | Large Language Models (LLMs) have exhibited significant proficiency in code debugging, especially in automatic program repair. |
| Approach: | They propose a repository-level code debugging dataset with 22 subtypes of errors that supports 8 commonly used programming languages and 3 debug tasks. |
| Outcome: | The proposed dataset supports 8 commonly used programming languages and 3 debugging tasks. |
Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do (2026.acl-long)
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| Challenge: | Existing open-source models often yield only marginal overall improvements, possibly due to an overemphasis on mathematical reasoning at the expense of broader capabilities. |
| Approach: | They evaluate 12 multimodal tasks using 14 non-reasoning models and 8 reasoning models. |
| Outcome: | The proposed method is effective in multimodal reasoning tasks, the authors show . they show that it lacks the ability to maintain deep visual introspection throughout the reasoning process. |
GeoLaux: A Benchmark for Evaluating MLLMs’ Geometry Performance on Long-Step Problems Requiring Auxiliary Lines (2026.acl-long)
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| Challenge: | Existing benchmarks for Geometry problem solving lack fine-grained evaluation for long-step problems necessitating auxiliary line construction. |
| Approach: | They present a fine-grained annotated dataset with long-step reasoning and auxiliary line construction that provides a detailed evaluation of 23 leading MLLMs. |
| Outcome: | The proposed model performs significantly worse on long-step problems than short-step ones, with 18 models showing a performance drop of over 50%. |
ACR: Adaptive Context Refactoring via Context Refactoring Operators for Multi-Turn Dialogue (2026.findings-acl)
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Jiawei Shen, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Qingyu Niu, Guoqing Ma, Jingjiang Liu, Yidan Liang, Yilin Wang, Shimin Di, Jiajie Xu
| Challenge: | Existing approaches to multi-turn dialogues lack contextual consistency and dependencies, and models struggle to maintain factual faithfulness as interaction turns increase. |
| Approach: | They propose an adaptive context refactoring framework that monitors and reshapes the interaction history to mitigate contextual inertia and state drift. |
| Outcome: | The proposed model outperforms baselines while reducing token consumption. |
Exploring End-to-End Differentiable Natural Logic Modeling (2020.coling-main)
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| Challenge: | Existing approaches to integrate natural logic with neural networks are brittle and prone to fail in the presence of noise and uncertainty. |
| Approach: | They propose to integrate natural logic with neural networks to create differentiable models that integrate natural reasoning with subsymbolic vector representations and neural components. |
| Outcome: | The proposed model can model monotonicity-based reasoning, compared to baseline models without inductive bias. |
The LLM Already Knows: Estimating LLM-Perceived Question Difficulty via Hidden Representations (2025.emnlp-main)
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| Challenge: | Existing methods for difficulty estimation rely on repeated response sampling, auxiliary models, or fine-tuning the target model itself. |
| Approach: | They propose a method that leverages only the hidden representations produced by large language models. |
| Outcome: | The proposed method outperforms baselines in difficulty estimation on textual and multimodal tasks and improves adaptive reasoning strategies with fewer generated tokens. |
Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing (2020.acl-main)
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| Challenge: | Existing semantic parsing frameworks rely on nontrivial human labor to generate canonical utterances. |
| Approach: | They propose a framework that uses an unsupervised paraphrase model to parse canonical utterances. |
| Outcome: | The proposed framework is effective and compatible with supervised training. |
ActionStudio: A Lightweight Framework for Data and Training of Large Action Models (2025.emnlp-main)
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Jianguo Zhang, Thai Quoc Hoang, Ming Zhu, Zuxin Liu, Shiyu Wang, Tulika Manoj Awalgaonkar, Akshara Prabhakar, Haolin Chen, Weiran Yao, Zhiwei Liu, Juntao Tan, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Existing infrastructure for efficient agentic data processing and model training remains underdeveloped. |
| Approach: | They propose a lightweight and extensible data and training framework for large action models . they propose to unify diverse agent trajectories using Unified Format 2.0 . |
| Outcome: | The proposed framework shows 9 higher throughput than existing frameworks and performs well across public and realistic agent benchmarks. |
ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch (2026.acl-long)
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Zheng Liu, Honglin Lin, Xiaoyang Wang, Xin Gao, Yu Li, Mengzhang Cai, Yun Zhu, Zhanping Zhong, Qizhi Pei, Zhuoshi Pan, Xiaoran Shang, Conghui He, Bin Cui, Wentao Zhang, Lijun Wu
| Challenge: | Existing open-source vision language models lack high-quality training data for chart reasoning . current models are simplistic and repetitive, while associated QA pairs are prone to hallucinations . |
| Approach: | They propose a framework to synthesize complex charts and reliable reasoning data from scratch. |
| Outcome: | Experimental results show that ChartVerse-8B surpasses existing models in QA and difficulty . lack of high-quality training data hampers development of open-source models . |
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. |
DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories (2024.findings-acl)
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Jia Li, Ge Li, Yunfei Zhao, Yongmin Li, Huanyu Liu, Hao Zhu, Lecheng Wang, Kaibo Liu, Zheng Fang, Lanshen Wang, Jiazheng Ding, Xuanming Zhang, Yuqi Zhu, Yihong Dong, Zhi Jin, Binhua Li, Fei Huang, Yongbin Li, Bin Gu, Mengfei Yang
| Challenge: | Existing benchmarks are poorly aligned with real-world code repositories and are insufficient to evaluate the coding abilities of Large Language Models (LLMs). |
| Approach: | They propose a repository-level benchmark named DevEval to evaluate LLMs' coding abilities in real-world code repositories. |
| Outcome: | The proposed benchmarks show that the LLMs perform better in real-world code repositories than existing benchmarks. |
A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction (2025.coling-main)
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| Challenge: | Existing approaches to address Grammatical Error Correction (GEC) tasks are based on large scale labeled data, which leads to extremely high data annotation costs. |
| Approach: | They propose a Chain-of-Task framework to reduce over-correction in large language models . they propose supervised fine-tuning strategy and an algorithm for automatic dataset annotation . |
| Outcome: | The proposed framework achieves state-of-the-art on both FCGEC (in-domain) and NaCGEC (out-of domain) test sets. |
Facet-Aware Evaluation for Extractive Summarization (2020.acl-main)
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| Challenge: | lexical overlap is a common evaluation metric for extractive summarization, but recent studies reveal its limitations. |
| Approach: | They propose a facet-aware evaluation setup for better assessment of information coverage in extractive summaries. |
| Outcome: | The proposed evaluation setup improves human correlation with extractive summarization datasets and improves comparative analysis. |
TexSmart: A System for Enhanced Natural Language Understanding (2021.acl-demo)
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Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Dick Zhu, Xiao Feng, Tao Chen, Tao Yang, Dong Yu, Feng Zhang, ZhanHui Kang, Shuming Shi
| Challenge: | TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications. |
| Approach: | They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. |
| Outcome: | The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions. |
MINER: Multi-Interest Matching Network for News Recommendation (2022.findings-acl)
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| Challenge: | Existing methods learn a single user embedding from user’s historical behaviors to represent the reading interest. |
| Approach: | They propose a poly attention scheme to learn multiple interest vectors for each user, which encodes the different aspects of user interest. |
| Outcome: | The proposed approach significantly outperforms existing state-of-the-art methods on the MIND news recommendation benchmark. |
Progressive Re-ranking for Multimodal Retrieval-Augmented Generation via Curriculum Learning (2026.findings-acl)
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| Challenge: | Existing approaches to improve retrieval performance of large language models are limited by static knowledge. |
| Approach: | They propose a multimodal re-ranking framework that combines curriculum learning with fine-grained reranking and multimodal section reassessment to improve CLIP-based visual coarse-grain retrieval. |
| Outcome: | The proposed framework achieves state-of-the-art answer accuracy and competitive retrieval performance on InfoSeek and Enc-VQA. |
End-to-End Segmentation-based News Summarization (2022.findings-acl)
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| Challenge: | Existing summarization systems only provide one genetic summary of the whole article, making it difficult for users to navigate the reading. |
| Approach: | They propose a task of segmenting a news article into multiple sections and generating the corresponding summary to each section. |
| Outcome: | The proposed model outperforms state-of-the-art models on a 27k news article dataset . it can jointly segment a document and produce the summary for each section . |
SILC-EFSA: Self-aware In-context Learning Correction for Entity-level Financial Sentiment Analysis (2025.coling-main)
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Senbin Zhu, ChenYuan He, Hongde Liu, Pengcheng Dong, Hanjie Zhao, Yuchen Yan, Yuxiang Jia, Hongying Zan, Min Peng
| Challenge: | Currently, most sentiment analysis corpora use sequence-level annotation. |
| Approach: | They propose a two-stage approach to financial entity-level sentiment analysis called Self-aware In-context Learning Correction. |
| Outcome: | The proposed approach achieves state-of-the-art on the largest English and Chinese financial entity-level sentiment analysis datasets to date. |
Generalization-Enhanced Code Vulnerability Detection via Multi-Task Instruction Fine-Tuning (2024.findings-acl)
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| Challenge: | Existing CodePre-trained models struggle to generalize due to superficial mapping from source code to labels instead of understanding the root causes of code vulnerabilities. |
| Approach: | They propose a framework that integrates multi-task learning with Large Language Models to effectively mine deep-seated vulnerability features. |
| Outcome: | The proposed framework surpasses seven state-of-the-art models in effectiveness, generalization, and robustness. |
ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models (2024.emnlp-main)
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Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, Hayato Yamana
| Challenge: | Currently, tool-augmented large language models (LLMs) only achieve total scores of 45.3 and 37.0, respectively, on a scale of 100. |
| Approach: | They propose a multi-level diagnostic process to assess the LLM's hallucinations through two perspectives: depth and breadth. |
| Outcome: | The proposed diagnostic process assesses the hallucinations of large language models through two perspectives: depth and breadth. |
DetectRL-X: Towards Reliable Multilingual and Real-World LLM-Generated Text Detection (2026.acl-long)
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Junchao Wu, Yefeng Liu, Chenyu Zhu, Hao Zhang, Zeyu Wu, Tianqi Shi, Yichao Du, Longyue Wang, Weihua Luo, Jinsong Su, Derek F. Wong
| Challenge: | Existing detectors are limited in their ability to detect large language models generated content in multilingual environments. |
| Approach: | They propose a multilingual benchmark to evaluate advanced detectors across 8 dimensions to better align with real-world applications. |
| Outcome: | The proposed benchmark encompasses 8 languages commonly used in commercial contexts and collects human-written texts from 6 domains highly susceptible to LLM misuse. |
Pass off Fish Eyes for Pearls: Attacking Model Selection of Pre-trained Models (2022.acl-long)
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| Challenge: | Existing feature-based model selection methods are vulnerable to fine-tuning and are not reliable indicators for the PTM’s transferability. |
| Approach: | They propose feature-based model selection methods which assess PTMs’ transferability to a specific task in a fast way without fine-tuning. |
| Outcome: | The proposed methods can make FMS mistakenly judge transferability of models and can be combined with the backdoor attack to misguide the FMS to select poisoned models. |
A Self-supervised Joint Training Framework for Document Reranking (2022.findings-naacl)
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| Challenge: | Pretrained language models have been successfully applied to a wide range of tasks . however, the pretraining tasks were based on the context of documents . |
| Approach: | They propose a self-supervised joint training framework with a method called Masked Query Prediction to establish semantic relations between given queries and positive documents. |
| Outcome: | The proposed framework outperforms existing models on document reranking tasks without further pre-training . it uses a self-supervised method to establish semantic relations between given queries and positive documents. |
FastMem: Fast Memorization of Prompt Improves Context Awareness of Large Language Models (2024.findings-emnlp)
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| Challenge: | Large language models struggle with context awareness, leading to inaccuracies in tasks requiring faithful adherence to provided information. |
| Approach: | They propose a method to enhance LLMs' context awareness by updating only the last Feed-Forward Network module to maximize the likelihood of the prompt before inference . |
| Outcome: | The proposed method improves the accuracy of Llama 3-8B-Inst on the NQ-SWAP dataset from 59.1% to 71.6% and reduces the output structure failure rate of Qwen 1.5-4B-Chat from 34.9% to 25.5%. |
WIKIGENBENCH:Exploring Full-length Wikipedia Generation under Real-World Scenario (2025.coling-main)
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Jiebin Zhang, Eugene J. Yu, Qinyu Chen, Chenhao Xiong, Dawei Zhu, Han Qian, Mingbo Song, Weimin Xiong, Xiaoguang Li, Qun Liu, Sujian Li
| Challenge: | Existing efforts to generate Wikipedia articles for new events fall short of real-world application. |
| Approach: | They propose a benchmark to generate Wikipedia articles for new events under real-world scenarios . they use systematic metrics and LLM-based metrics to assess verifiability, organization, and other aspects aligned with real-life scenarios. |
| Outcome: | The proposed benchmarks show that hierarchical-based methods generate more comprehensive content while fine-tuned methods achieve better verifiability. |
Make Some Noise: Unlocking Language Model Parallel Inference Capability through Noisy Training (2024.emnlp-main)
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Yixuan Wang, Xianzhen Luo, Fuxuan Wei, Yijun Liu, Qingfu Zhu, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
| Challenge: | Existing speculative decoding methods require additional model structure and training processes to assist the model for draft token generation. |
| Approach: | They propose a make some noise training framework that introduces some noise at the input for the model to learn the denoising task. |
| Outcome: | The proposed model improves inference speed by 2.3-2.7x times without compromising model performance. |
CRPO: Confidence-Reward Driven Preference Optimization for Machine Translation (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have shown great potential in natural language processing tasks, but their application to machine translation remains challenging due to pretraining on predominantly English-centric datasets. |
| Approach: | They propose a method that combines reward scores with model confidence to improve model selection for fine-tuning. |
| Outcome: | The proposed method outperforms existing methods in translation accuracy and data efficiency. |
Perplexity-Aware Data Scaling Law: Perplexity Landscapes Predict Performance for Continual Pre-training (2026.acl-long)
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| Challenge: | Large language models (LLMs) have impressive capabilities across a wide range of domains, but their generalpurpose pre-training objectives often leave them illsuited for specialized applications such as healthcare. |
| Approach: | They propose a perplexity-aware data scaling law that establishes a predictive relationship between the perplexities of domain-specific data and the test loss. |
| Outcome: | Experiments on medical and general-domain benchmarks show that the proposed scaling law consistently identifies near-optimal training subsets with significantly reduced data consumption. |
LLM Agents in Law: Taxonomy, Applications, and Challenges (2026.acl-long)
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Shuang Liu, Ruijia Zhang, Ruoyun Ma, Yujia Deng, Lanyi Zhu, Jiayu Li, Zelong Li, Zhibin Shen, Mengnan Du
| Challenge: | Large language models (LLMs) have improved the legal domain, but deployment of standalone models faces significant limitations regarding hallucination, outdated information, and verifiability. |
| Approach: | They present a survey of LLM agents for legal tasks and analyze their architectures . they analyze the transition from standard legal LLMs to legal agents . |
| Outcome: | The proposed architectures bridge the gap between technical capabilities and domain-specific needs. |
Z-Code++: A Pre-trained Language Model Optimized for Abstractive Summarization (2023.acl-long)
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Pengcheng He, Baolin Peng, Song Wang, Yang Liu, Ruochen Xu, Hany Hassan, Yu Shi, Chenguang Zhu, Wayne Xiong, Michael Zeng, Jianfeng Gao, Xuedong Huang
| Challenge: | Z-Code++ is a pre-trained language model optimized for abstractive text summarization. |
| Approach: | They propose a pre-trained language model optimized for abstractive text summarization that uses a two-phase pre-training technique to improve model's performance. |
| Outcome: | The proposed model outperforms the competing models on low-resource summarization tasks in zero-shot and few-shot settings. |
Granular Entity Mapper: Advancing Fine-grained Multimodal Named Entity Recognition and Grounding (2024.findings-emnlp)
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| Challenge: | Existing methods for fine-grained content extraction are limited by long-tailed distribution of textual entity categories and performance of object detectors. |
| Approach: | They propose a multi-granularity entity recognition module and a reranking module to integrate hierarchical information of entity categories, visual cues, and external textual resources collectively. |
| Outcome: | The proposed framework achieves state-of-the-art on the fine-grained content extraction task. |
Bootstrapping meaning through listening: Unsupervised learning of spoken sentence embeddings (2022.findings-emnlp)
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| Challenge: | a new study addresses the challenge of learning semantic representations from speech signals . speech-based semantic representation can be used for speech mining and spoken language understanding . |
| Approach: | They propose a multimodal sequential autoencoder that converts speech signals into hidden units . they propose s-HuBERT to induce meaning through knowledge distillation . |
| Outcome: | The proposed model achieves a moderate correlation with human judgments without labels or transcriptions. |
PersonaBench: Evaluating AI Models on Understanding Personal Information through Accessing (Synthetic) Private User Data (2025.findings-acl)
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Juntao Tan, Liangwei Yang, Zuxin Liu, Zhiwei Liu, Rithesh R N, Tulika Manoj Awalgaonkar, Jianguo Zhang, Weiran Yao, Ming Zhu, Shirley Kokane, Silvio Savarese, Huan Wang, Caiming Xiong, Shelby Heinecke
| Challenge: | Existing research lacks direct access to such data, making benchmarking difficult due to privacy concerns. |
| Approach: | They propose a synthetic data pipeline that generates realistic user profiles and private documents and a benchmark to evaluate models' ability to understand personal information. |
| Outcome: | The proposed pipeline generates realistic user profiles and private documents, enabling PersonaBench, a benchmark for evaluating models’ ability to understand personal information. |
The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding (2020.acl-demos)
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Xiaodong Liu, Yu Wang, Jianshu Ji, Hao Cheng, Xueyun Zhu, Emmanuel Awa, Pengcheng He, Weizhu Chen, Hoifung Poon, Guihong Cao, Jianfeng Gao
| Challenge: | MT-DNN is an open-source natural language understanding toolkit . it allows researchers and developers to train customized deep learning models . |
| Approach: | They present MT-DNN, an open-source natural language understanding toolkit . it is designed to facilitate rapid customization for a broad spectrum of NLU tasks . MT supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. |
| Outcome: | The proposed model can significantly compress a large model without significant performance drop. |
HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models (2026.findings-acl)
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| Challenge: | Existing methods for visual token pruning lack insight into the intrinsic property of the vision encoder . et al., 2017: 99.3% of task accuracy with only 1/3 of the tokens. |
| Approach: | They propose a model-agnostic token pruning method that trains without training . they propose 'HiPrune' method which prunes visual tokens according to their attention . |
| Outcome: | The proposed method achieves 99.3% of task accuracy with only 1/3 of the tokens . it reduces inference FLOPs by 58.7% and maintains 99.99% accuracy with 2/9 tokens. |
More than Classification: A Unified Framework for Event Temporal Relation Extraction (2023.acl-long)
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| Challenge: | Existing methods for event temporal relation extraction ignore meaning of relations and wipe out their intrinsic dependency. |
| Approach: | They propose a unified event temporal relation extraction framework that transforms temporal relations into logical expressions of time points and completes the ETRE by predicting the relations between certain time points. |
| Outcome: | The proposed framework outperforms the state-of-the-art model on TB-Dense and MATRES by 0.3% on both datasets. |
ReTRE: Benchmarking LLM Transfer Robustness with Structure-Preserving Variants (2026.acl-long)
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| Challenge: | Learning transfer theory emphasizes that applying acquired knowledge to novel manifestations is a key signal of deep understanding |
| Approach: | They propose a benchmark that probes transfer robustness along two rewrite levels: Near Transfer and Far Transfer. |
| Outcome: | The proposed benchmark demonstrates that large language models are robust when faced with novel manifestations of the same problem. |
Revisiting De-Identification of Electronic Medical Records: Evaluation of Within- and Cross-Hospital Generalization (2023.emnlp-main)
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| Challenge: | De-identification is a natural language processing task to detect and remove the protected health information (PHI) from electronic medical records (EMRs). |
| Approach: | They propose a de-identification dataset comprising EMRs from three hospitals in China and use it to establish a benchmark for evaluating both within- and cross-hospital generalization. |
| Outcome: | The proposed model with almost perfect within-hospital performance struggles when transferred across hospitals. |
QAP: A Quantum-Inspired Adaptive-Priority-Learning Model for Multimodal Emotion Recognition (2023.findings-acl)
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| Challenge: | Experimental results show that multimodal emotion recognition is a state-of-the-art technique . textual, visual and acoustic modalities are involved in multimodal video emotion recognition . |
| Approach: | They propose a quantum-inspired adaptive-priority-learning model to address the challenges . they use quantum state to model modal features and Q-attention to integrate three modalities . |
| Outcome: | Experimental results show that QAP improves on previous models. |
Uncertainty-Aware Cross-Modal Alignment for Hate Speech Detection (2024.lrec-main)
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| Challenge: | Existing methods for detecting hate speech ignore misalignment and uncertainty between modalities . social media platforms have become conduits for the rapid dissemination of hate speech . |
| Approach: | They propose an uncertainty-aware cross-modal alignment framework for hate speech detection that minimizes the misalignment of image and text in memes. |
| Outcome: | The proposed framework produces a competitive performance compared with existing methods. |
Knowledge Graph-Guided Retrieval Augmented Generation (2025.naacl-long)
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| Challenge: | Existing studies on RAG focus on semantic retrieval of isolated relevant chunks, which ignore their intrinsic relationships. |
| Approach: | They propose a framework that utilizes knowledge graphs to provide fact-level relationships between chunks, improving the diversity and coherence of the retrieved results. |
| Outcome: | Extensive experiments on the HotpotQA dataset and its variants demonstrate the advantages of KG2RAG compared to existing RAG-based approaches in terms of response quality and retrieval quality. |
BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook (2026.acl-long)
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Hao Gu, Lujun Li, Hao Wang, Lei Wang, Zheyu Wang, Bei Liu, Jiacheng Liu, Qiyuan Zhu, Sirui Han, Yike Guo
| Challenge: | Recent sparsity-aware binarization approaches can achieve sub-1-bit compression, but they face performance degradation, mask-management overhead, and limited hardware compatibility. |
| Approach: | They propose a binary quantization framework that leverages binary pattern clustering and weight transformation to overcome performance degradation and mask-management overhead. |
| Outcome: | The proposed framework achieves state-of-the-art compression (1.11–0.7 bits) it maintains high performance with only a 3.1% accuracy drop in zero-shot benchmarks while delivering a 1.6 speedup over FP16. |
Chinese Lexical Substitution: Dataset and Method (2023.emnlp-main)
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| Challenge: | Existing benchmarks for lexical substitution (LS) are limited and limited in coverage . despite extensive research on Lexical Substitution in various languages, there is limited evidence for LS in Chinese. |
| Approach: | They propose to use human and machine collaboration to construct a Chinese LS dataset . they combine four unsupervised LS methods to generate candidate substitutes . |
| Outcome: | The proposed method outperforms existing benchmarks on the Chinese lexical substitution task. |
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation (P19-3)
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Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Sachan, Eric Xing
| Challenge: | Texar is an open-source text generation toolkit that supports a broad set of text generation tasks. |
| Approach: | They introduce Texar, an open-source text generation toolkit that supports text generation tasks. |
| Outcome: | Texar supports machine translation, summarization, dialog, content manipulation, and more. |
LLM×MapReduce-V3: Enabling Interactive In-Depth Survey Generation through a MCP-Driven Hierarchically Modular Agent System (2025.emnlp-demos)
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Yu Chao, Siyu Lin, Xiaorong Wang, Zhu Zhang, Zihan Zhou, Haoyu Wang, Shuo Wang, Jie Zhou, Zhiyuan Liu, Maosong Sun
| Challenge: | Generating high-quality long-form survey articles poses significant challenges to AI Agent systems. |
| Approach: | They propose a hierarchically modular agent system for long-form survey generation . they use atomic models to implement skeleton initialization, digest construction, and skelet refinement . human evaluations demonstrate system surpasses representative baselines . |
| Outcome: | The proposed system surpasses representative baselines in both content depth and length, highlighting the strength of MCP-based modular planning. |
Counterfactual Adversarial Learning with Representation Interpolation (2021.findings-emnlp)
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| Challenge: | Existing models with statistical bias are prone to memorized correlations . large pre-trained models such as BERT have revolutionized the model development paradigm in natural language processing . |
| Approach: | They propose a framework to tackle the problem from a causal perspective using a latent space interpolation approach. |
| Outcome: | Extensive experiments show that CAT achieves substantial performance improvement over SOTA across different downstream tasks, including sentence classification, natural language inference and question answering. |
BrailleLLM: Braille Instruction Tuning with Large Language Models for Braille Domain Tasks (2025.emnlp-main)
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Tianyuan Huang, Zepeng Zhu, Hangdi Xing, Zirui Shao, Zhi Yu, Chaoxiong Yang, Jiaxian He, Xiaozhong Liu, Jiajun Bu
| Challenge: | Existing Braille research focuses on isolated tasks while mixed-content Braille tasks face data scarcity and ambiguities. |
| Approach: | They propose a syntax tree-based augmentation method tailored for Braille data. |
| Outcome: | The proposed method improves Braille translation, formula-to-Braille conversion, and mixed-text translation. |
RRNorm: A Novel Framework for Chinese Disease Diagnoses Normalization via LLM-Driven Terminology Component Recognition and Reconstruction (2024.findings-acl)
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| Challenge: | Clinical Terminology Normalization (CTN) aims at finding standard terms from a given termbase for mentions extracted from clinical texts. |
| Approach: | They propose a method that leverages reasoning capability of large language models to recognize components of terms and automate decomposition. |
| Outcome: | The proposed strategy achieves state-of-the-art on the experimental dataset. |
HILL: Hierarchy-aware Information Lossless Contrastive Learning for Hierarchical Text Classification (2024.naacl-long)
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| Challenge: | Existing self-supervised methods in natural language processing rely on augmentation rules to generate contrastive samples. |
| Approach: | They propose a hierarchy-aware information lossless contrastive learning scheme that uses syntactic information reserved in the input sample and fused during the learning process. |
| Outcome: | The proposed learning scheme is superior to existing methods in hierarchical text classification . the proposed learning system is based on a structure encoder and a text encoder . |
SPAGBias: Uncovering and Tracing Structured Spatial Gender Bias in Large Language Models (2026.acl-long)
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| Challenge: | Large language models (LLMs) are being used in urban planning but there is concern that they reproduce or amplify such biases. |
| Approach: | They propose a framework to evaluate spatial gender bias in large language models . they use a taxonomy of 62 urban micro-spaces, a prompt library and three diagnostic layers . |
| Outcome: | The proposed framework identifies structured gender-space associations that go beyond the public-private divide, forming nuanced micro-level mappings. |
Aligning Large Language Models with Human Preferences through Representation Engineering (2024.acl-long)
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Wenhao Liu, Xiaohua Wang, Muling Wu, Tianlong Li, Changze Lv, Zixuan Ling, Zhu JianHao, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Existing methods for achieving this alignment involve employing reinforcement learning from human feedback (RLHF) Existing approaches involve using RLHF to fine-tune LLMs based on human labels . however, RLRF is susceptible to instability during fine- tuning and presents challenges in implementation. |
| Approach: | They propose to use reinforcement learning from human feedback to fine-tune large language models with human preferences to achieve precise control of model behavior. |
| Outcome: | Experiments show that RAHF can be used to capture and manipulate representations to align with a broad spectrum of human preferences or values rather than being confined to a single concept or function. |
RAFFLES: Reasoning-based Attribution of Faults for LLM Systems (2026.eacl-long)
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Chenyang Zhu, Spencer Hong, Jingyu Wu, Kushal Chawla, Yuhui Tang, Youbing Yin, Nathan Wolfe, Erin Babinsky, Daben Liu
| Challenge: | Existing evaluation frameworks focus on simple metrics and end-to-end outcomes, but they struggle with longer contexts. |
| Approach: | They propose an offline evaluation architecture that incorporates iterative reasoning to evaluate the quality of the candidate faults and rationales of the Judge. |
| Outcome: | The proposed architecture outperforms baseline evaluation frameworks with two datasets to identify step-level faults in multi-agent systems and ReasonEval datasets. |
Deja vu: Contrastive Historical Modeling with Prefix-tuning for Temporal Knowledge Graph Reasoning (2024.findings-naacl)
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| Challenge: | Existing text-based methods for Temporal Knowledge Graph Reasoning struggle to balance textual knowledge and temporal information with expensive purpose-built training strategies. |
| Approach: | They propose a Contrastive historical modeling framework with prefix-tuning for TEmporal Reasoning that feeds history-contextualized text into the pseudo-Siamese encoders to strike a textual-temporal balance. |
| Outcome: | The proposed framework achieves superior performance on four transductive and three few-shot inductive TKGR benchmarks. |
Beyond Memorization: The Challenge of Random Memory Access in Language Models (2024.acl-long)
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| Challenge: | Recent advances in Language Models (LMs) have shown their effectiveness in knowledge-intensive tasks. |
| Approach: | They investigate whether a generative language model is able to access its memory sequentially or randomly. |
| Outcome: | The proposed LMs are able to access memory sequentially or randomly. |
Evolutionary Guided Decoding: Iterative Value Refinement for LLMs (2026.acl-long)
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| Challenge: | Existing methods for directing language model outputs are limited in their accuracy due to a distributional gap . existing methods train static value functions on trajectories sampled exclusively from the base policy . |
| Approach: | They propose a framework to bridge a distributional gap in the accuracy of value functions . they propose RLHF to align language models with human values and task requirements . |
| Outcome: | The proposed framework reduces computational costs and improves value function accuracy by leveraging principled value function optimization. |
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation (2021.tacl-1)
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| Challenge: | Existing language representation models (PLMs) cannot capture factual knowledge from text. |
| Approach: | They propose a unified model for Knowledge Embedding and Pre-trained LanguagERepresentation which integrates factual knowledge into PLMs and produces effective text-enhanced KE with the strong PLM. |
| Outcome: | The proposed model improves on existing pre-trained language representation models and improves their performance on various NLP tasks. |
Knowledge-Selective Pretraining for Attribute Value Extraction (2023.findings-emnlp)
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Hui Liu, Qingyu Yin, Zhengyang Wang, Chenwei Zhang, Haoming Jiang, Yifan Gao, Zheng Li, Xian Li, Chao Zhang, Bing Yin, William Wang, Xiaodan Zhu
| Challenge: | Existing methods for AVE are limited on rare attributes due to poor generalization ability. |
| Approach: | They propose to leverage pretraining and transfer learning to address weaknesses in existing methods. |
| Outcome: | The proposed method achieves new state-of-the-art performance without pretraining on rare attributes with limited training resources. |
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving (2025.acl-long)
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Teng Wang, Wing Yin Yu, Zhenqi He, Zehua Liu, HaileiGong HaileiGong, Han Wu, Xiongwei Han, Wei Shi, Ruifeng She, Fangzhou Zhu, Tao Zhong
| Challenge: | Existing datasets in operations research domain lack detailed annotations of the modeling process, focusing only on objective values. |
| Approach: | They propose an annotation-based tree-of-thought tree-based reasoning algorithm that integrates reinforcement learning into a tree- of-though. |
| Outcome: | The proposed algorithm outperforms state-of-the-art methods on StructuredOR, NL4OPT, and MAMO-ComplexLP datasets. |
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. |
Self-Distillation Bridges Distribution Gap in Language Model Fine-Tuning (2024.acl-long)
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| Challenge: | Experimental results show that fine-tuning of large language models for specific tasks can be challenging . distribution shift during fine-timing can lead to performance degradation in general task capabilities . |
| Approach: | They propose a new approach that bridges the distribution gap between task datasets and LLMs by guiding fine-tuning with a distilled dataset generated by the model itself. |
| Outcome: | The proposed approach achieves comparable or superior performance on downstream tasks compared to the vanilla approach. |
Masks Can be Learned as an Alternative to Experts (2025.acl-long)
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| Challenge: | a recent study shows that sparse activation techniques can reduce inference performance without sacrificing performance. |
| Approach: | They propose to sparsify a pre-trained dense large language model into a mixture-of-experts architecture for faster inference. |
| Outcome: | The proposed approach is more efficient than one-shot sparsification techniques . it achieves 97% performance retention on downstream tasks with only 50% of parameters activated . |
TaKG: A New Dataset for Paragraph-level Table-to-Text Generation Enhanced with Knowledge Graphs (2022.findings-aacl)
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| Challenge: | Existing table-to-text generation benchmarks have some limitations, such as E2E and ToTTo focusing on singlesentence generation tasks. |
| Approach: | They propose a new table-to-text generation dataset called TaKG that uses a set of knowledge graphs to enhance table input. |
| Outcome: | The proposed model outperforms existing models for short-text generation tasks and shows reliable performance on long-text generated across a variety of metrics. |
LIRE: listwise reward enhancement for preference alignment (2024.findings-acl)
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| Challenge: | prevailing approaches to preference alignment focus on pairwise comparisons, with limited exploration into multi-response scenarios. |
| Approach: | They propose a listwise reward enhancement approach that integrates offline rewards of multiple responses into a streamlined listwise framework. |
| Outcome: | The proposed approach outperforms existing methods on dialogue and summarization tasks with good transferability to out-of-distribution data. |
Browse and Concentrate: Comprehending Multimodal Content via Prior-LLM Context Fusion (2024.acl-long)
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Ziyue Wang, Chi Chen, Yiqi Zhu, Fuwen Luo, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Maosong Sun, Yang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) lack understanding of multi-image and interleaved inputs due to the visual features encoded by frozen encoders before being fed into the LLM backbone. |
| Approach: | They propose a two phase paradigm to enable in-depth multimodal context fusion prior to feeding the features into LLMs. |
| Outcome: | The proposed paradigm boosts the performance on 7 multi-image scenarios, contributing to increments on average accuracy by 2.13% and 7.60% against strong MLLMs baselines with 3B and 11B LLMs, respectively. |
Memorization, Emergence, and Explaining Reversal Failures: A Controlled Study of Relational Semantics in LLMs (2026.acl-long)
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Yihua Zhu, Qianying Liu, Jiaxin Wang, Fei Cheng, Chaoran Liu, Akiko Aizawa, Sadao Kurohashi, Hidetoshi Shimodaira
| Challenge: | Autoregressive LLMs perform well on relational tasks that require linking entities via relational words, but it is unclear whether they learn the logical semantics of such relations or whether left-to-right order bias is involved. |
| Approach: | They propose a framework that generates text from symmetric/inverse triples and trains autoregressive models from scratch. |
| Outcome: | The proposed framework generates text from symmetric/inverse triples, trains autoregressive models from scratch, and evaluates memorization, logical inference, and in-context generalization to unseen entities. |
Learning to Ask Unanswerable Questions for Machine Reading Comprehension (P19-1)
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| Challenge: | Existing models for extractive reading comprehension are not good at deciding whether no answer is presented in the context. |
| Approach: | They propose a data augmentation technique by automatically generating relevant unanswerable questions according to an answerable question paired with its corresponding paragraph that contains the answer. |
| Outcome: | The proposed model performs better on the SQuAD 2.0 dataset than the baseline model and the BERT-large model. |
INTERS: Unlocking the Power of Large Language Models in Search with Instruction Tuning (2024.acl-long)
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Yutao Zhu, Peitian Zhang, Chenghao Zhang, Yifei Chen, Binyu Xie, Zheng Liu, Ji-Rong Wen, Zhicheng Dou
| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities in various natural language processing tasks, but their application to information retrieval tasks is still challenging due to the infrequent occurrence of many IR-specific concepts in natural language. |
| Approach: | They propose to use instruction tuning to enhance LLMs' proficiency in IR tasks by combining a dataset with manually written templates to analyze the effects of instruction design, template diversity, few-shot demonstrations, and the volume of instructions. |
| Outcome: | The proposed model can be used to perform query understanding, document understanding, and query-document relationship understanding tasks. |
DarwinTOD: LLM-Driven Lifelong Self-evolution for Task-oriented Dialog Systems (2026.acl-long)
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| Challenge: | Continual learning approaches fail to achieve autonomy lifelong improvement in dynamic environments . current task-oriented dialog systems are static, unable to learn from ongoing interactions . |
| Approach: | They propose a lifelong self-evolving dialog framework that integrates evolutionary computation and LLM driven self-improvement into a single framework. |
| Outcome: | The proposed framework surpasses state-of-the-art methods and exhibits continuous performance gains throughout evolution. |
Beyond Atomic Characters: Glyph-Aware Sub-character Alignment for Low-Resource Multilingual OCR (2026.acl-long)
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| Challenge: | Low-resource multilingual OCR models struggle with complex script structures and data scarcity. |
| Approach: | They propose a framework for multilingual character recognition that integrates visual and linguistic backbones with a novel glyph-aware interface. |
| Outcome: | The proposed framework improves on high-resolution visual and language backbones with glyph-aware interface. |
Multi-Aspect Controllable Text Generation with Disentangled Counterfactual Augmentation (2024.acl-long)
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| Challenge: | Existing studies neglect attribute correlations formed by the intertwining of different attributes. |
| Approach: | They propose a multi-aspect controllable text generation method with disentangled counterfactual augmentation that alleviates imbalanced attribute correlations during training by disentanglement. |
| Outcome: | The proposed method outperforms state-of-the-art methods in imbalanced and balanced attribute correlation scenarios. |
Enhancing Descriptive Image Captioning with Natural Language Inference (2021.acl-short)
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| Challenge: | Existing captioning models tend to generate generic captions, but generating descriptive captions is important. |
| Approach: | They propose a novel approach to encourage captioning models to produce more detailed captions using natural language inference. |
| Outcome: | The proposed method outperforms baseline models on MSCOCO metrics on descriptiveness and descriptiveness. |
Let’s Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models (2024.lrec-main)
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| Challenge: | Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
| Approach: | They propose a diffusion model which extracts aspects step by step and learns a denoising process that progressively restores them in a reverse manner. |
| Outcome: | Empirical evaluations on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
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. |
The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions (2023.emnlp-main)
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Siru Ouyang, Shuohang Wang, Yang Liu, Ming Zhong, Yizhu Jiao, Dan Iter, Reid Pryzant, Chenguang Zhu, Heng Ji, Jiawei Han
| Challenge: | Recent advances in large language models (LLMs) have produced models that exhibit remarkable performance across a variety of NLP tasks. |
| Approach: | They analyze a large-scale collection of user-GPT conversations to identify a significant gap between academic research in NLP and the needs of real-world NLP applications. |
| Outcome: | The proposed model outperforms existing models in a large-scale collection of user-GPT conversations and identifies a significant gap between the tasks that users frequently request from LLMs and the tasks commonly studied in academic research. |
Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding (2024.findings-emnlp)
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Derong Xu, Ziheng Zhang, Zhihong Zhu, Zhenxi Lin, Qidong Liu, Xian Wu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
| Challenge: | Medical Information Extraction (MIE) tasks are a fundamental component of medical NLP. |
| Approach: | They propose an alternative adaptive constraint strategy to adjust the scale and scope of contrastive tokens. |
| Outcome: | The proposed approach selectively enhances the identification and classification capabilities while minimizing the influence of other inherent abilities in LLMs. |
InsightEval: An Expert-Curated Benchmark for Assessing Insight Discovery in LLM-Driven Data Agents (2026.findings-acl)
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Zhenghao Zhu, Yuanfeng Song, Xing Chen, Chengzhong Liu, Cui Yakun, Caleb Chen Cao, Sirui Han, Yike Guo
| Challenge: | Existing frameworks for data analysis and insight exploration are lacking in terms of benchmarks . existing frameworks suffer from format inconsistencies, poorly conceived objectives, and redundant insights. |
| Approach: | They propose a data-curation pipeline to construct a new dataset named InsightEval. |
| Outcome: | The proposed benchmarks highlight prevailing challenges in automated insight discovery and raise key findings to guide future research. |
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation (2025.findings-acl)
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Yingfeng Luo, Tong Zheng, Yongyu Mu, Bei Li, Qinghong Zhang, Yongqi Gao, Ziqiang Xu, Peinan Feng, Xiaoqian Liu, Tong Xiao, JingBo Zhu
| Challenge: | Recent advances in machine translation have focused on a single pre-trained decoder . encoder-decoder architectures have received relatively little attention in NMT . |
| Approach: | They propose a method that leverages LLMs as MT encoders and pairs them with lightweight decoders to develop universal translation models. |
| Outcome: | The proposed method matches or surpasses baselines in terms of translation quality but achieves 75% reduction in memory footprint of the KV cache. |
Revisiting Interpolation Augmentation for Speech-to-Text Generation (2024.findings-acl)
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Chen Xu, Jie Wang, Xiaoqian Liu, Qian Dong, Chunliang Zhang, Tong Xiao, JingBo Zhu, Dapeng Man, Wu Yang
| Challenge: | Existing approaches to speech-to-text generation tasks are limited by the lack of extensive labeled datasets. |
| Approach: | They propose to use interpolation augmentation to construct virtual training samples by transforming inputs and labels to enhance generalization in other domains. |
| Outcome: | The proposed approach significantly improves performance across diverse tasks, architectures, and data scales, offering a promising avenue for more robust S2T systems in resource-constrained settings. |
AC-EVAL: Evaluating Ancient Chinese Language Understanding in Large Language Models (2024.findings-emnlp)
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| Challenge: | AC-EVAL is a benchmark designed to assess the advanced knowledge and reasoning capabilities of LLMs within the context of ancient Chinese. |
| Approach: | They propose a benchmark to assess the advanced knowledge and reasoning capabilities of LLMs in ancient Chinese. |
| Outcome: | AC-EVAL aims to assess the comprehension of ancient Chinese texts . the benchmark covers 13 tasks covering historical facts, geography, social customs, art, philosophy, classical poetry and prose. |
Bridging the Granularity Gap for Acoustic Modeling (2023.findings-acl)
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Chen Xu, Yuhao Zhang, Chengbo Jiao, Xiaoqian Liu, Chi Hu, Xin Zeng, Tong Xiao, Anxiang Ma, Huizhen Wang, Jingbo Zhu
| Challenge: | Despite the success of speech recognition, how to encode the speech features effectively remains an open problem. |
| Approach: | They propose a Progressive Down-Sampling technique which compresses acoustic features into coarser-grained units containing more complete semantic information, like text-level representation. |
| Outcome: | The proposed method yields comparable or better results on the speech recognition task and inference speedups ranging from 1.20x to 1.47x. |
Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning (2025.acl-long)
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Zhu Xu, Zhiqiang Zhao, Zihan Zhang, Yuchi Liu, Quanwei Shen, Fei Liu, Yu Kuang, Jian He, Conglin Liu
| Challenge: | Large language models (LLMs) use tokenization methods but often obscure internal character structures within tokens. |
| Approach: | They propose a method that improves models’ ability to capture character positions within tokens by training them on reverse character prediction tasks using the tokenizer’s vocabulary. |
| Outcome: | Experiments show that the proposed method improves position prediction accuracy in large language models, enabling more precise identification of target characters in original text. |
Fusing Context Into Knowledge Graph for Commonsense Question Answering (2021.findings-acl)
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| Challenge: | Existing methods to combine language modeling and knowledge graphs (KG) lack the context to provide a more precise understanding of the concepts. |
| Approach: | They propose to use external entity descriptions to provide contextual information for commonsense question answering models. |
| Outcome: | The proposed model achieves state-of-the-art among non-generative models in OpenBookQA and is the first of its kind in the field. |
AIDE: Attribute-Guided MultI-Hop Data Expansion for Data Scarcity in Task-Specific Fine-tuning (2025.acl-industry)
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| Challenge: | Existing methods for fine-tuning large language models for specific tasks require extensive seed datasets or struggle to balance task relevance and data diversity. |
| Approach: | They propose a data synthesis framework that uses a multi-hop process to expand very few seed data points while ensuring data diversity and task relevance. |
| Outcome: | The proposed framework outperforms state-of-the-art methods in task-specific fine-tuning by over 30%. |
HiDe-LLaVA: Hierarchical Decoupling for Continual Instruction Tuning of Multimodal Large Language Model (2025.acl-long)
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| Challenge: | Existing methods to improve instructionfollowing performance of MLLMs often trade off memory efficiency for performance gains, compromising overall efficiency. |
| Approach: | They propose a task-specific expansion and task-general fusion framework based on variations in Centered Kernel Alignment (CKA) similarity across different model layers when trained on diverse datasets. |
| Outcome: | The proposed framework improves performance compared to existing benchmarks. |
Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation (2020.acl-main)
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| Challenge: | In encoder-decoder neural models, multiple encoders are used to represent contextual information in addition to the individual sentence. |
| Approach: | They propose to use multiple context encoders to encode the individual sentences in document-level neural machine translation (NMT) They propose a noisy dropout setup and a single-encoder approach to encode context sentences. |
| Outcome: | The proposed approach encodes the context and the current sentence without contexts. |
In-Context Demonstration Selection with Cross Entropy Difference (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) can use in-context demonstrations to improve performance on zero-shot tasks. |
| Approach: | They propose a cross-entropy difference method for selecting in-context demonstrations that uses parameter efficient finetuning to train small models on training data. |
| Outcome: | The proposed method outperforms baseline selection methods on a mix-domain dataset and shows that the effectiveness of in-context demonstrations negatively correlates with the perplexity of the test example. |
Agentic Reasoning: A Streamlined Framework for Enhancing LLM Reasoning with Agentic Tools (2025.acl-long)
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| Challenge: | Existing reasoning methods excel in structured domains like math and code, but they are not all effective in knowledge-intensive tasks. |
| Approach: | They introduce a framework that enhances large language model reasoning by integrating external tool-using agents. |
| Outcome: | The proposed framework achieves state-of-the-art among public models and delivers comparable performance to OpenAI Deep Research. |
APPSI-139: A Parallel Corpus of English Application Privacy Policy Summarization and Interpretation (2026.acl-long)
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Pengyun Zhu, Qiheng Sun, Long Wen, Yanbo Wang, Yang Cao, Junxu Liu, Deyi Xiong, Jinfei Liu, Zhibo Wang, Kui Ren
| Challenge: | a lack of high-quality English privacy policy corpus optimized for legal clarity and readability is limiting translation of privacy policies . 139 privacy policies are often considered "incomprehensible" due to technical jargon, legal language, and convoluted grammatical structures. |
| Approach: | They propose a high-quality English privacy policy corpus annotated by domain experts . they propose APPSI-139 to summarize and interpret privacy policies in English . |
| Outcome: | The proposed framework outperforms large language models in terms of readability and accuracy. |
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks (2023.emnlp-industry)
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| Challenge: | Recent large language models such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models . however, their applicability and effectiveness in specific domains like finance needs a better understanding . |
| Approach: | They conduct empirical studies to compare the performance of ChatGPT and GPT-4 on financial text analytical problems using eight benchmark datasets from five categories of tasks. |
| Outcome: | The proposed models outperform the state-of-the-art models on a wide range of financial text analytical tasks. |
Beyond Single-View Detection: A Dual-Space Reasoning Framework for Interpretable Harmful Meme Understanding (2026.acl-long)
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| Challenge: | Existing methods for identifying harmful memes rely on modal alignment or black-box classifiers . BPDMoE-Hate provides visual explanations for viewpoint selection and hierarchical structuring . |
| Approach: | They propose a framework that conceptualizes harmful meme detection as a process of "viewpoint decoupling and hierarchical fusion" they propose BPDMoE-Hate, which generates adversarial binary perspectives via VLMs and incorporates an adaptive viewpoint gating to facilitate viewpoint selection. |
| Outcome: | The proposed framework surpasses existing methods in performance and provides visual explanations for viewpoint selection and hierarchical structuring. |
ReflectEvo: Improving Meta Introspection of Small LLMs by Learning Self-Reflection (2025.findings-acl)
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Jiaqi Li, Xinyi Dong, Yang Liu, Zhizhuo Yang, Quansen Wang, Xiaobo Wang, Song-Chun Zhu, Zixia Jia, Zilong Zheng
| Challenge: | ReflectEvo-460k is a large-scale, comprehensive, self-generated reflection dataset with broadened instructions and diverse multi-domain tasks. |
| Approach: | They propose a pipeline that iteratively generates self-reflection for self-training and a large-scale reflection dataset with broadened instructions and diverse multi-domain tasks. |
| Outcome: | The proposed pipeline improves Llama-3 reasoning ability by up to 71.2% and Mistral by upto 44.4%. |
Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow (2026.findings-acl)
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Yangyang Zhong, Yanmei Gu, Zhengqing Zang, Xiaomeng Li, Yuqi Ding, Xibei Jia, Yuting Shen, Zhenzhong Lan, Liwang Zhu, Weiping Liu, Junlin Zhou, Haisheng Liu, Zhong Xin Yu, Pengxin Luo, Donglian Qi, Yunfeng Yan, Junbo Zhao
| Challenge: | Autoregressive (AR) language models dominate modern natural language processing due to strong likelihood-based training objectives and reliable left-to-right decoding. |
| Approach: | They characterize MDLM behavior along two dimensions: parallelism strength and generation order . authors propose a Generate-then-Edit paradigm that mitigates dependency loss . |
| Outcome: | The proposed model improves on tasks that require "backward information" the Generate-then-Edit paradigm improves parallel decoding efficiency while reducing dependency loss. |
ReasonRank: Empowering Passage Ranking with Strong Reasoning Ability (2026.acl-long)
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| Challenge: | Existing rerankers perform poorly in complex ranking scenarios due to the scarcity of reasoning-intensive training data. |
| Approach: | They propose an automated reasoning-intensive training framework which generates high-quality training labels from training queries and passages. |
| Outcome: | The proposed model outperforms baselines significantly and achieves much lower latency than the pointwise reranker. |
Fine-Tuning Language Models with Differential Privacy through Adaptive Noise Allocation (2024.findings-emnlp)
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| Challenge: | Language models can memorize detailed information and patterns, but raise privacy concerns . ANADP reduces the performance gap between regular and DP fine-tuning while maintaining the privacy constraints. |
| Approach: | They propose an algorithm that allocates additive noise based on the importance of model parameters to reduce the performance gap between regular fine-tuning and traditional DP fine- tuning. |
| Outcome: | The proposed algorithm narrows the performance gap between regular fine-tuning and traditional DP fine- tuning while maintaining privacy constraints. |
Leveraging Locality in Abstractive Text Summarization (2022.emnlp-main)
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Yixin Liu, Ansong Ni, Linyong Nan, Budhaditya Deb, Chenguang Zhu, Ahmed Hassan Awadallah, Dragomir Radev
| Challenge: | Neural attention models have improved on many natural language processing tasks, but their quadratic memory complexity hinders their applications in long text summarization. |
| Approach: | They propose to use a restricted context to study locality in text summarization . they propose to employ a quadratic memory growth with respect to the input length . |
| Outcome: | The proposed model has better performance than baseline models with efficient attention modules. |
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities (2023.acl-demo)
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Zhe Zhao, Yudong Li, Cheng Hou, Jing Zhao, Rong Tian, Weijie Liu, Yiren Chen, Ningyuan Sun, Haoyan Liu, Weiquan Mao, Han Guo, Weigang Gou, Taiqiang Wu, Tao Zhu, Wenhang Shi, Chen Chen, Shan Huang, Sihong Chen, Liqun Liu, Feifei Li, Xiaoshuai Chen, Xingwu Sun, Zhanhui Kang, Xiaoyong Du, Linlin Shen, Kimmo Yan
| Challenge: | Several pre-training models of different modalities are showing a rising trend of homogeneity in their model structures. |
| Approach: | They propose a toolkit that supports pre-training models of different modalities. |
| Outcome: | The proposed toolkit can match the performance of the original implementations on text, vision, and audio benchmarks. |
SARA: Salience-Aware Reinforced Adaptive Decoding for Large Language Models in Abstractive Summarization (2025.acl-long)
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| Challenge: | Existing decoding strategies neglect the explicit use of salient contextual information and rely on static hyperparameters to fix the balance between contextual and prior knowledge. |
| Approach: | They propose a salience-aware reinforced adaptive decoding (SARA) which incorporates salient contextual information and allows the model to determine reliance on source document's context, salient context, and model's prior knowledge based on pointwise mutual information. |
| Outcome: | The proposed model improves the quality and faithfulness of summaries across LLMs without modifying their weights. |
Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)
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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li
| Challenge: | Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences. |
| Approach: | They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks. |
| Outcome: | The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge. |
GDTB: Genre Diverse Data for English Shallow Discourse Parsing across Modalities, Text Types, and Domains (2024.emnlp-main)
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Yang Janet Liu, Tatsuya Aoyama, Wesley Scivetti, Yilun Zhu, Shabnam Behzad, Lauren Levine, Jessica Lin, Devika Tiwari, Amir Zeldes
| Challenge: | Existing shallow discourse parsing systems focus on the Wall Street Journal corpus, but the data is limited to the news domain and is 35 years old. |
| Approach: | They propose to use the Wall Street Journal corpus as a benchmark for PDTB-style shallow discourse parsing. |
| Outcome: | The proposed dataset is compatible with PDTB, but suffers from degradation out-of-domain. |
AutoConv: Automatically Generating Information-seeking Conversations with Large Language Models (2023.acl-short)
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Siheng Li, Cheng Yang, Yichun Yin, Xinyu Zhu, Zesen Cheng, Lifeng Shang, Xin Jiang, Qun Liu, Yujiu Yang
| Challenge: | Existing research on information-seeking conversations is stymied by the lack of training data. |
| Approach: | They propose to use autoconv for synthetic conversation generation to capture the characteristics of the information-seeking process and fine tune an LLM with a few human conversations to generate synthetic conversations with high quality. |
| Outcome: | The proposed model improves on two commonly-used datasets and alleviates the dependence on human annotation. |
DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question Answering (2023.acl-long)
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| Challenge: | Existing evaluation metrics for natural language generation (NLG) tasks face the challenges on generalization ability and interpretability. |
| Approach: | They propose a metric that evaluates natural language generation tasks as an instruction-style question answering task and utilizes instruction-tuned pre-trained language models without training on evaluation datasets. |
| Outcome: | The proposed metric achieves state-of-the-art performance in untrained metrics for evaluating text summarization and dialogue generation, which exhibits strong dimension-level / task-level generalization ability and interpretability. |
Spotlighter: Revisiting Prompt Tuning from a Representative Mining View (2025.findings-emnlp)
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| Challenge: | Spotlighter is a lightweight token-selection framework that enhances accuracy and efficiency in prompt tuning. |
| Approach: | They propose a token-selection framework that enhances accuracy and efficiency in prompt tuning by preserving only the top-scoring tokens for downstream prediction. |
| Outcome: | The proposed framework outperforms CLIP by up to 11.19% in harmonic mean accuracy and achieves 0.8K additional FPS, with only 21 extra parameters. |
Revealing the Parallel Multilingual Learning within Large Language Models (2024.emnlp-main)
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Yongyu Mu, Peinan Feng, Zhiquan Cao, Yuzhang Wu, Bei Li, Chenglong Wang, Tong Xiao, Kai Song, Tongran Liu, Chunliang Zhang, JingBo Zhu
| Challenge: | Large language models (LLMs) can handle multilingual and cross-lingual text within a single input; however, previous studies focusing on using English as the pivot language to enhance language understanding and reasoning focus on using multiple languages. |
| Approach: | They propose to use parallel multilingual input to enhance the model's comprehension of the input and to examine how multilingual processing affects prediction. |
| Outcome: | The proposed model can handle multilingual and cross-lingual text within a single input, but previous studies focused on using English as the pivot language to enhance language understanding and reasoning. |
CARL: Constraint-Aware Reinforcement Learning for Planning with LLMs (2026.findings-acl)
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Qiuyi Qi, Jinjian Zhang, Mutian Bao, Tian Liang, Guocong Li, Dongnan Liu, Wei Zhou, Jie Liu, Ming Kong, Linjian Mo, Feng Zhang, Qiang Zhu
| Challenge: | Existing approaches to constraint-aware planning fail to enhance the model’s intrinsic focus on constraints. |
| Approach: | They propose a constraint-aware reinforcement learning framework that encourages constraint focus and penalizes neglect of LLMs. |
| Outcome: | The proposed framework outperforms existing frameworks and state-of-the-art reasoning models in a number of real-world applications. |
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)
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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang
| Challenge: | Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. |
| Approach: | AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. |
| Outcome: | Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% . |
HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents (2026.acl-long)
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| Challenge: | Existing memory systems represent conversation history as unstructured embedding vectors, retrieving information through semantic similarity. |
| Approach: | They propose a bio-inspired memory architecture that models memory as a dynamic graph with Hebbian learning dynamics. |
| Outcome: | The proposed architecture leverages both semantic similarity and learned associations . it can be used to build a bio-inspired memory graph with Hebbian learning dynamics . |
TS-SQL: Test-driven Self-refinement for Text-to-SQL (2025.findings-emnlp)
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| Challenge: | null |
| Approach: | null |
| Outcome: | null |
More Tokens, Lower Precision: Towards the Optimal Token-Precision Trade-off in KV Cache Compression (2025.findings-emnlp)
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Jiebin Zhang, Dawei Zhu, Yifan Song, Wenhao Wu, Chuqiao Kuang, Xiaoguang Li, Lifeng Shang, Qun Liu, Sujian Li
| Challenge: | storing more tokens in the KV cache at lower precision can enhance the long-context performance of large language models. |
| Approach: | They propose a token-precision trade-off strategy to optimize KV cache compression . they also propose storing more tokens in the KV at lower precision . |
| Outcome: | The proposed method achieves an optimal point within the Information Bottleneck compared to standalone KV pruning or KV quantization. |
TCPO: Thought-Centric Preference Optimization for Effective Embodied Decision-making (2025.emnlp-main)
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Kechen Jiao, Zhirui Fang, Jiahao Liu, Bei Li, Qifan Wang, Xinyu Liu, Junhao Ruan, Zhongjian Qiao, Yifan Zhu, Yaxin Xu, Jingang Wang, Xiu Li
| Challenge: | Existing post-SFT methods for embodied AI are constrained by sparse rewards and action-only optimization, resulting in low sample efficiency, poor consistency, and model degradation. |
| Approach: | They propose to integrate Thought-Centric Preference Optimization (TCPO) into embodied decision-making by transforming sparse reward signals into richer step sample pairs. |
| Outcome: | The proposed approach achieves an average success rate of 26.67% in the ALFWorld environment, and a 6% improvement over RL4VLM. |
RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning (2025.emnlp-industry)
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Deyi Ji, Yuekui Yang, Liqun Liu, Peng Shu, Haiyang Wu, Shaogang Tang, Xudong Chen, Shaoping Ma, Tianrun Chen, Lanyun Zhu
| Challenge: | Recent advances in large language models have improved the detection of non-compliant content, but critical gaps persist in fine-grained understanding, explainability, and generalization. |
| Approach: | They propose a framework that combines active reinforcement learning, fine-grained violation understanding and progressive multi-stage training. |
| Outcome: | The proposed framework outperforms general-purpose LLMs and specialized models in fine-grained violation understanding, explainability, and generalization. |
SLAM-Omni: Timbre-Controllable Voice Interaction System with Single-Stage Training (2025.findings-acl)
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Wenxi Chen, Ziyang Ma, Ruiqi Yan, Yuzhe Liang, Xiquan Li, Ruiyang Xu, Zhikang Niu, Yanqiao Zhu, Yifan Yang, Zhanxun Liu, Kai Yu, Yuxuan Hu, Jinyu Li, Yan Lu, Shujie Liu, Xie Chen
| Challenge: | a new spoken dialogue system with single-stage training is demonstrating its low latency and high quality . SLAM-Omni achieves zero-shot timbre control by modeling spoken language with semantic tokens . |
| Approach: | They propose a timbre-controllable, end-to-end voice interaction system with single-stage training. |
| Outcome: | The proposed system outperforms previous models on 4 GPUs with limited data. |
Hybrid Alignment Training for Large Language Models (2024.findings-acl)
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| Challenge: | Existing approaches to align large language models with instructions and preferences are conflicting . et al., 2023b) show that hybrid alignment training can outperform baselines . |
| Approach: | They propose a hybrid alignment training approach based on alternating alignment and modified elastic weight consolidation methods to achieve better collaboration between different alignment tasks. |
| Outcome: | The proposed approach outperforms baseline alignment training methods on summarization and dialogue tasks. |
A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction (2024.lrec-main)
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| Challenge: | Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from unstructured document. |
| Approach: | They propose a document-prompt-based method for document-level event argument extraction that uses a semantic mention graph to capture relations between documents and prompts. |
| Outcome: | The proposed method surpasses baseline methods and achieves state-of-the-art performance on RAMS and WikiEvents datasets. |
MedQPA-Gen: Medical Question Proposing and Answering for Report Generation (2026.findings-acl)
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Weijie Liang, Xiyue Zhu, Ruike Zhu, Chenhao Li, Cheng Tang, Zhiyu Liu, Zhihua Gong, Shirui Luo, Yudu Li, Volodymyr Kindratenko
| Challenge: | Existing training paradigms fail to explicitly target factual accuracy, resulting in inaccuracies and serious patient safety risks. |
| Approach: | They propose an automatic and generalizable report evaluation technique that uses question proposing and answering to enable controllable, structured reasoning grounded in medical domain knowledge and the factual correctness of the report. |
| Outcome: | The proposed method can improve human preference scores and perform better on downstream tasks. |
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. |
InfoCL: Alleviating Catastrophic Forgetting in Continual Text Classification from An Information Theoretic Perspective (2023.findings-emnlp)
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| Challenge: | Recent studies have identified the severe performance decrease on analogous classes as a key factor for catastrophic forgetting. |
| Approach: | They propose a replay-based continual text classification method that uses fast-slow and current-past contrastive learning to perform mutual information maximization and better recover previously learned representations. |
| Outcome: | The proposed method achieves state-of-the-art on three text classification tasks. |
Same Claim, Different Judgment: Benchmarking Scenario-Induced Bias in Multilingual Financial Misinformation Detection (2026.findings-acl)
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Zhiwei Liu, Yupeng Cao, Yuechen Jiang, Mohsinul Kabir, Polydoros Giannouris, Chen Xu, Ziyang Xu, Tianlei Zhu, Md. Tariquzzaman, Triantafillos Papadopoulos, Yan Wang, Lingfei Qian, Xueqing Peng, Zhuohan Xie, Ye Yuan, Saeed Almheiri, Abdulrazzaq Alnajjar, Ming-Bin Chen, Harry Stuart, Paul Thompson, Prayag Tiwari, Alejandro Lopez-Lira, Xue Liu, Jimin Huang, Sophia Ananiadou
| Challenge: | Existing research on LLM biases has focused on direct questioning or general-purpose settings . pronounced behavioral biase despite their growing deployment in financial analysis, forecasting, and decision support. |
| Approach: | They propose a benchmark to evaluate behavioral biases of large language models in MFMD . they use a multilingual financial misinformation dataset to integrate these with misinformation claims . |
| Outcome: | The proposed benchmark evaluates behavioral biases of large language models across economic scenarios. |
Representation Learning with Ordered Relation Paths for Knowledge Graph Completion (D19-1)
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| Challenge: | Existing knowledge graphs are incomplete and lack the order of relations in paths. |
| Approach: | They propose a method which takes relation paths into account but ignores order of relations in paths which is important for reasoning. |
| Outcome: | The proposed method performs better than state-of-the-art methods on two benchmark datasets. |
See the World, Discover Knowledge: A Chinese Factuality Evaluation for Large Vision Language Models (2025.findings-acl)
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Jihao Gu, Yingyao Wang, Pi Bu, Chen Wang, Ziming Wang, Tengtao Song, Donglai Wei, Jiale Yuan, Yingxiu Zhao, Yancheng He, Shilong Li, Jiaheng Liu, Meng Cao, Jun Song, Yingshui Tan, Xiang Li, Wenbo Su, Xiaoyong Zhu, Bo Zheng
| Challenge: | Existing models for large vision language models do not fully reflect their knowledge capacity and reliability, resulting in erroneous outputs that do not align with the image content or provide answers lacking knowledge evidence. |
| Approach: | They propose a Chinese-based benchmark for visual factuality across 8 major topics and 56 subtopics and a multi-hop question construction. |
| Outcome: | The proposed model decouples visual factuality into two parts: seeing the world and discovering knowledge. |
Marco-Bench-MIF: On Multilingual Instruction-Following Capability of Large Language (2025.acl-long)
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Bo Zeng, Chenyang Lyu, Sinuo Liu, Mingyan Zeng, Minghao Wu, Xuanfan Ni, Tianqi Shi, Yu Zhao, Yefeng Liu, Chenyu Zhu, Ruizhe Li, Jiahui Geng, Qing Li, Yu Tong, Longyue Wang, Weihua Luo, Kaifu Zhang
| Challenge: | Existing datasets for instruction-following are monolingual and centered on English . existing data are unable to capture linguistic and cultural subtle differences . |
| Approach: | They propose an extension of IFEval to a localized multilingual version called Marco-Bench-MIF . their benchmark addresses linguistic constraints and cultural references via translation and verification . |
| Outcome: | The proposed extension of IFEval to a localized multilingual version covers 30 languages with varying levels of localization. |
Retrieval Enhanced Model for Commonsense Generation (2021.findings-acl)
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| Challenge: | Existing frameworks for commonsense generation are lacking for pre-trained models. |
| Approach: | They propose a framework that uses concept matching to retrieve prototype sentences and trainable sentence retriever to enhance pre-training and fine-tuning. |
| Outcome: | The proposed framework achieves state-of-the-art on the large-scale Common-Gen benchmark. |
TransAdv: A Translation-based Adversarial Learning Framework for Zero-Resource Cross-Lingual Named Entity Recognition (2022.findings-emnlp)
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| Challenge: | Existing methods for named entity recognition are limited by noise in translation . Existing approaches to named entities recognition are mainly based on labeled data . |
| Approach: | They propose a framework to mitigate lexical and syntactic errors of translated data . they propose to use multi-level adversarial learning and multi-model knowledge distillation to mitigate noise . |
| Outcome: | The proposed framework mitigates lexical and syntactic errors of translated data . it achieves competitive performance to state-of-the-art models . |
A Simple and Effective Method to Improve Zero-Shot Cross-Lingual Transfer Learning (2022.coling-1)
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Kunbo Ding, Weijie Liu, Yuejian Fang, Weiquan Mao, Zhe Zhao, Tao Zhu, Haoyan Liu, Rong Tian, Yiren Chen
| Challenge: | Existing zero-shot cross-lingual transfer methods rely on parallel corpora or bilingual dictionaries . however, its effect is limited by the gap between embedding clusters of different languages . |
| Approach: | They propose Embedding-Push, Attention-Pull, and Robust targets to transfer English embeddings to virtual multilingual embedders without semantic loss. |
| Outcome: | Experimental results show that the proposed method outperforms existing methods on cross-lingual tasks and can achieve a better multilingual alignment. |
MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization (2021.naacl-main)
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| Challenge: | Existing datasets for dialogue summarization are limited to their small sizes and are built from a narrow domain. |
| Approach: | They propose a large-scale media interview dataset consisting of 463.6K transcripts with abstractive summaries. |
| Outcome: | The proposed dataset is larger and contains multi-party conversations from multiple domains. |
Think-Search-Patch: A Retrieval-Augmented Reasoning Framework for Repository-Level Code Repair (2025.emnlp-industry)
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Bojian Xiong, Yikun Lei, Xikai Liu, Shaowei Zhang, Pengyun Zhu, Yan Liu, Yongqi Leng, Ling Shi, Meizhi Zhong, Yurong Zhang, Yan Gao, null Yiwu, Yao Hu, Deyi Xiong
| Challenge: | Large language models suffer from multiple-file coding scenarios with strong inter-file dependencies . experimental results show that large language models exhibit inadequate performance in multi-file scenarios . |
| Approach: | They propose a retrieval-augmented reasoning framework for repository-level code repair . they use a dataset to generate standardized patches based on the key snippets . |
| Outcome: | The proposed framework improves retrieval accuracy and repair success on SWE-bench Lite . it surpasses models with larger size in managing extensive code contexts and fixing bugs spanning across multiple files. |
M³GQA: A Multi-Entity Multi-Hop Multi-Setting Graph Question Answering Benchmark (2025.acl-long)
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| Challenge: | GraphRAG systems have achieved remarkable progress in enhancing performance and reliability of large language models. |
| Approach: | They propose a GraphRAG benchmark focusing on multi-entity queries with six settings for comprehensive evaluation. |
| Outcome: | The proposed method can construct diverse data with semantically correct ground-truth reasoning paths. |
Inference-Time Decontamination: Reusing Leaked Benchmarks for Large Language Model Evaluation (2024.findings-emnlp)
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| Challenge: | Current LLMs are achieving better performance on various benchmarks, but their performance in practical applications does not always match their benchmark results. |
| Approach: | They propose to detect and rewrite leaked benchmarks without altering their difficulties by using Inference-Time Decontamination (ITD) to mitigate performance inflation caused by memorizing leaked samples. |
| Outcome: | The proposed method reduces inflated accuracy by 22.9% on GSM8K and 19.0% on MMLU. |
Bit-by-Bit: Progressive QAT Strategy with Outlier Channel Splitting for Stable Low-Bit LLMs (2026.acl-long)
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Binxing Xu, Hao Gu, Lujun Li, Hao Wang, Bei Liu, Jiacheng Liu, Qiyuan Zhu, Xintong Yang, Chao Li, Sirui Han, Yike Guo
| Challenge: | Existing approaches to training LLMs at ultra-low precisions suffer from convergence instability and substantial training costs. |
| Approach: | They propose a progressive QAT framework with outlier channel splitting to address these issues . they use nested structure of integer quantization grids to enable a "train once, deploy any precision" paradigm . |
| Outcome: | The proposed framework outperforms baselines on both Llama2/3 and W2A16, with an 11 speedup over BF16. |
Discontinuous Named Entity Recognition as Maximal Clique Discovery (2021.acl-long)
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| Challenge: | Existing methods for named entity recognition break the recognition process into several sequential steps. |
| Approach: | They propose a method that breaks the recognition process into several sequential steps . they construct a segment graph for each sentence and a grid tagging scheme to learn it . |
| Outcome: | Experiments show that the proposed method outperforms the state-of-the-art model and achieves 5x speedup over the SOTA model. |
Combining Curriculum Learning and Knowledge Distillation for Dialogue Generation (2021.findings-emnlp)
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| Challenge: | Existing studies have shown that curriculum learning facilitates dialogue generation tasks while knowledge distillation can yield significant performance boosts for student models. |
| Approach: | They propose a combination of curriculum learning and knowledge distillation for dialogue generation models . they cluster training cases according to their complexity and employ an adversarial training strategy . |
| Outcome: | The proposed model improves compared with baselines. |
Stealthy Jailbreak Attacks on Large Language Models via Benign Data Mirroring (2025.naacl-long)
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Honglin Mu, Han He, Yuxin Zhou, Yunlong Feng, Yang Xu, Libo Qin, Xiaoming Shi, Zeming Liu, Xudong Han, Qi Shi, Qingfu Zhu, Wanxiang Che
| Challenge: | Existing black-box jailbreak methods often rely on model feedback . existing methods may be intercepted by content moderators during the search process . |
| Approach: | They propose a method that guides malicious prompt construction by local training a mirror model of the target black-box model through benign data distillation. |
| Outcome: | The proposed method achieves a 92% attack success rate and 80% stealth rate on a subset of AdvBench. |
CE-VDG: Counterfactual Entropy-based Bias Reduction for Video-grounded Dialogue Generation (2024.lrec-main)
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| Challenge: | Existing methods to reduce question-related bias in video-grounded dialogue generation (VDG) however, the dataset often contains inherent bias, which can cause VDG models to learn spurious correlations between questions and answers. |
| Approach: | They propose to extend the counterfactual reasoning from the information entropy perspective to the generative task, which can effectively reduce the question-related bias in the auto-regressive generation task. |
| Outcome: | The proposed method can reduce question-related bias in the auto-regressive generation task by using counterfactual entropy as an external loss. |
HyperText: Endowing FastText with Hyperbolic Geometry (2020.findings-emnlp)
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| Challenge: | Empirically, we show that HyperText outperforms FastText on a range of text classification tasks with much reduced parameters. |
| Approach: | They propose a model that uses hyperbolic geometry to model tree-like hierarchies in natural language sentences by embedding words or ngrams in hyperbolical space. |
| Outcome: | Empirically, the proposed model outperforms FastText on a range of text classification tasks with much reduced parameters. |
Detect, Disambiguate, and Translate: On-Demand Visual Reasoning for Multimodal Machine Translation with Large Vision-Language Models (2025.naacl-long)
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Danyang Liu, Fanjie Kong, Xiaohang Sun, Dhruva Patil, Avijit Vajpayee, Zhu Liu, Vimal Bhat, Najmeh Sadoughi
| Challenge: | Multimodal machine translation (MMT) aims to leverage additional modalities beyond text . current MMT systems rely heavily on monolingual English captioning data . |
| Approach: | They propose a reasoning-based framework to leverage large-scale vision-language models for MMT . they propose Detect, Disambiguate, and Translate framework to detect ambiguity in input sentence . |
| Outcome: | The proposed framework outperforms state-of-the-art models in disambiguation accuracy and translation quality. |
Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)
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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions? |
| Approach: | They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values. |
| Outcome: | The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure. |
Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents (2026.acl-long)
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Yuanchen Bei, Tianxin Wei, Xuying Ning, Yanjun Zhao, Zhining Liu, Xiao Lin, Yada Zhu, Hendrik Hamann, Jingrui He, Hanghang Tong
| Challenge: | Existing benchmarks evaluate multi-session memory in text-only conversations or assess multimodal understanding within localized contexts. |
| Approach: | They propose a benchmark for evaluating multimodal long-term conversational memory in MLLM agents. |
| Outcome: | The proposed framework assesses key memory capabilities along three functional dimensions: memory extraction and test-time adaptation, memory reasoning, and memory knowledge management. |
FastCorrect 2: Fast Error Correction on Multiple Candidates for Automatic Speech Recognition (2021.findings-emnlp)
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Yichong Leng, Xu Tan, Rui Wang, Linchen Zhu, Jin Xu, Wenjie Liu, Linquan Liu, Xiang-Yang Li, Tao Qin, Edward Lin, Tie-Yan Liu
| Challenge: | Error correction is widely used in automatic speech recognition (ASR) to post-process the generated sentence. |
| Approach: | They propose a fast correction model that takes multiple ASR candidates as input for better correction accuracy. |
| Outcome: | The proposed model can reduce the word error rate (WER) with multiple candidates by 3.2% and 2.6%. |
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation (2024.emnlp-main)
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Yuan Ge, Yilun Liu, Chi Hu, Weibin Meng, Shimin Tao, Xiaofeng Zhao, Mahong Xia, Zhang Li, Boxing Chen, Hao Yang, Bei Li, Tong Xiao, JingBo Zhu
| Challenge: | Existing methods for instruction data selection have limitations such as relying on fragile external APIs, being affected by biases in GPT models, or reducing the diversity of the selected instruction dataset. |
| Approach: | They propose an industrial-friendly, expert-aligned and diversity-preserved instruction data selection method: Clustering and Ranking (CaR). |
| Outcome: | The proposed method outperforms Alpaca's existing methods by 32.1% in GPT-4 evaluations. |
Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots (2020.findings-emnlp)
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| Challenge: | Experimental results show that FIRE outperforms previous methods for building knowledge-grounded retrieval-based chatbots . a method called Filtering before iteratively referring is used to ground a conversation on background knowledge . |
| Approach: | They propose a method for grounding conversation on background knowledge . they use context filter and knowledge filter to make context and knowledge aware . experimental results show that FIRE outperforms previous methods . |
| Outcome: | The proposed method outperforms previous methods on two datasets. |
Post-Training Dialogue Summarization using Pseudo-Paraphrasing (2022.findings-naacl)
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| Challenge: | Existing approaches to dialogue summarization use dialogue-specific features that require additional knowledge to recognize or make the models harder to tune. |
| Approach: | They propose to post-train pretrained language models to rephrase from dialogue to narratives and fine-tune them as usual. |
| Outcome: | The proposed approach outperforms existing models by summary quality and implementation costs. |
CRAFTQA: A Code-Driven Adaptive Framework for Complex Structured Data Reasoning (2026.findings-acl)
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| Challenge: | Existing unified structured data question answering methods rely on a set of predefined functions, which restricts their ability to perform complex reasoning beyond these predefined operations. |
| Approach: | They propose a novel adaptive code-driven framework that generates code-based reasoning operations based on a question. |
| Outcome: | The proposed framework improves on multiple structured datasets on real-world scenarios. |
Bypassing Neural Evaluations for Fast Audio Editing via Adaptive Trajectory Extrapolation (2026.findings-acl)
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Xiaoqian Liu, Zhengkun Ge, Jianjin Wang, Haoran Zhang, Yuan Ge, Kaiyan Chang, Chen Xu, Tong Xiao, Zhengtao Yu, Linfeng Zhang, JingBo Zhu
| Challenge: | Recent advances in audio diffusion models have significantly improved text-to-audio editing via inversion techniques, but these models typically rely on dense, fixed-step sampling trajectories to maintain structural integrity. |
| Approach: | They propose a model-agnostic Adaptive Trajectory Extrapolation framework that accelerates inversion-based editing process by dynamically evaluating only the most critical generative phases. |
| Outcome: | The proposed framework achieves a 3.9 speedup with negligible loss in fidelity. |
PersLEARN: Research Training through the Lens of Perspective Cultivation (2023.acl-demo)
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Yu-Zhe Shi, Shiqian Li, Xinyi Niu, Qiao Xu, Jiawen Liu, Yifan Xu, Shiyu Gu, Bingru He, Xinyang Li, Xinyu Zhao, Zijian Zhao, Yidong Lyu, Zhen Li, Sijia Liu, Lin Qiu, Jinhao Ji, Lecheng Ruan, Yuxi Ma, Wenjuan Han, Yixin Zhu
| Challenge: | PersLEARN is a tool designed to facilitate the cultivation of scientific perspectives . junior researchers struggle to identify the perspectives reflected in the literature and struggle to develop their own viewpoints. |
| Approach: | They propose a tool to facilitate the cultivation of scientific perspectives by interacting with a prompt-based model and allowing students to develop their own perspectives explicitly. |
| Outcome: | The proposed tool outperforms baseline approaches across multiple domains of literature from different perspectives. |
A Systematic Assessment of Language Models with Linguistic Minimal Pairs in Chinese (2026.tacl-1)
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Yikang Liu, Yeting Shen, Hongao Zhu, Lilong Xu, Zhiheng Qian, Siyuan Song, Kejia Zhang, Jialong Tang, Pei Zhang, Baosong Yang, Rui Wang, Hai Hu
| Challenge: | Using sub-linear length normalized log-probabilities (SLLN-LP), we find unequal lengths of sentences in minimal pairs difficult for LMs even up to 32B parameters. |
| Approach: | They propose to use ZhoBLiMP as a linguistic minimal pair benchmark for Chinese language models to mitigate biases. |
| Outcome: | The proposed metric mitigates biases in Chinese language models with over 100 paradigms . Anaphor, Quantifiers, and Ellipsis are difficult for LMs even up to 32B parameters . |
FormulaReasoning: A Dataset for Formula-Based Numerical Reasoning (2026.findings-acl)
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| Challenge: | Existing datasets for numerical reasoning often lack explicit knowledge of formulas . current datasets do not provide process supervision information, resulting in incomplete reasoning . |
| Approach: | They propose a benchmark for formula-based numerical reasoning with 5,324 questions . they provide annotations in English and Chinese and a formula database as an external knowledge source . |
| Outcome: | The proposed model includes 5,324 questions requiring calculations grounded in external physics principles. |
SimVBG: Simulating Individual Values by Backstory Generation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) have strong human-like capabilities, but rarely simulating individualized human values. |
| Approach: | They propose a framework that simulates individual values based on individual backstories . they use structured data on an individual to transform their backstoried information to a backstory . |
| Outcome: | The proposed framework improves top-1 accuracy by more than 10% over retrieval-augmented generation methods. |
Beyond Full Fine-tuning: Harnessing the Power of LoRA for Multi-Task Instruction Tuning (2024.lrec-main)
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Chunlei Xin, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning algorithm for large-scale language models. |
| Approach: | They conduct a systematic study of Low-Rank Adaptation (LoRA) on diverse tasks and rich resources with different learning capacities. |
| Outcome: | The proposed algorithm can achieve remarkable performance in high-resource and multi-task scenarios, even comparable to full fine-tuning. |
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (D18-1)
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| Challenge: | Empirical results show that even the most competitive few-shot learning models struggle on this task, especially as compared with humans. |
| Approach: | They propose a Few-Shot Relation Classification Dataset consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. |
| Outcome: | The proposed methods perform well on the most competitive few-shot learning models, especially as compared with humans. |
Towards IP Intelligence: Benchmarking Large Language Models on Intellectual Property Knowledge and Practice (2026.findings-acl)
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Qiyao Wang, Guhong Chen, Hongbo Wang, Huaren Liu, Minghui Zhu, Zhifei Qin, Li Linwei, Yilin Yue, Shiqiang Wang, Jiayan Li, Wu Yihang, Ziqiang Liu, Longze Chen, Run Luo, Liyang Fan, Jiaming Li, Lei Zhang, Kan Xu, Hamid Alinejad-Rokny, Chengming Li, Shiwen Ni, Yuan Lin, Min Yang
| Challenge: | Existing datasets and benchmarks focus only on patents or cover limited aspects of the IP field, lacking alignment with real-world scenarios. |
| Approach: | They propose a bilingual IP task taxonomy and a large-scale bilingual benchmark to evaluate LLMs in real-world IP practice. |
| Outcome: | The proposed model achieves only 75.8% accuracy, indicating room for improvement . open-source IP and law-oriented models lag behind closed-source general-purpose models . |
Reinforcement Learning on Pre-Training Data (2026.acl-long)
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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang
| Challenge: | Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings. |
| Approach: | They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data. |
| Outcome: | Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base. |
Noise-injected Consistency Training and Entropy-constrained Pseudo Labeling for Semi-supervised Extractive Summarization (2022.coling-1)
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| Challenge: | Existing studies on semi-supervised learning methods focus on how to effectively utilize abundant unlabeled data. |
| Approach: | They propose a semi-supervised consistency training method to regularize model predictions and a pseudo-labeling strategy to obtain high-confidence labels from unlabeled predictions. |
| Outcome: | The proposed method improves extractive summarization over an insufficient labeled dataset. |
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 . |
On the Emotion Understanding of Synthesized Speech (2026.acl-long)
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Yuan Ge, Haishu Zhao, AoKai Hao, Junxiang Zhang, Bei Li, Xiaoqian Liu, Chenglong Wang, Jianjin Wang, Bingsen Zhou, Bingyu Liu, JingBo Zhu, Zhengtao Yu, Tong Xiao
| Challenge: | Existing models for emotion understanding do not capture fundamental features of synthesized speech. |
| Approach: | They evaluate emotion recognition models on synthesized speech using SER models and generative models. |
| Outcome: | The proposed model can't generalize to synthesized speech because of speech token prediction . generative models tend to infer emotion from textual semantics while ignoring paralinguistic cues. |
RePair: Automated Program Repair with Process-based Feedback (2024.findings-acl)
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| Challenge: | Commercial-scale language models (LMs) have taken APR to unprecedented levels, but they are limited by parameters and humans interact with them through explicit prompts. |
| Approach: | They propose a method that utilizes process supervision to improve program repair by allowing users to input feedback from compilers and test cases. |
| Outcome: | The proposed method outperforms large outcome-based generation methods and is inspired by strategies used in programming competitions. |
Seer Self-Consistency: Advance Budget Estimation for Adaptive Test-Time Scaling (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated exceptional performance in reasoning tasks, particularly in mathematics. |
| Approach: | They propose a dynamic self-consistency framework that integrates System 1 and System 2 reasoning to improve token efficiency and latency. |
| Outcome: | The proposed method outperforms existing methods, achieving up to 47% reduction in token consumption and 43% reduction in inference latency without significant performance loss. |
Towards an On-device Agent for Text Rewriting (2024.findings-naacl)
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Yun Zhu, Yinxiao Liu, Felix Stahlberg, Shankar Kumar, Yu-Hui Chen, Liangchen Luo, Lei Shu, Renjie Liu, Jindong Chen, Lei Meng
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities for text rewriting, however creating a smaller yet potent language model presents two formidable challenges: costly data collection and absence of emergent capabilities. |
| Approach: | They propose a new instruction tuning method to develop a mo-bile text rewriting model that leverages LLM-generated data and heuristic reinforcement learning, eliminating the need for human data collection. |
| Outcome: | The proposed model surpasses the current state-of-the-art LLMs in text rewriting while maintaining a significantly reduced model size using public benchmark EditEval and our new benchmark. |
Intent Contrastive Learning Based on Multi-view Augmentation for Sequential Recommendation (2025.coling-main)
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Bo Pei, Yingzheng Zhu, Guangjin Wang, Huajuan Duan, Wenya Wu, Fuyong Xu, Yizhao Zhu, Peiyu Liu, Ran Lu
| Challenge: | Existing work on intent-related models fails to capture long-term dependencies in user behavior and fails to effectively utilize item relevance. |
| Approach: | They propose a sequential recommendation framework that combine temporal variability with position encoding that has extrapolation properties to encode sequences, thereby expanding the model’s view of user behavior. |
| Outcome: | The proposed model improves on three real datasets by 0.8% to 14.7% compared to baselines. |
Can Large Language Models Effectively Support Decision-Making in Sudden Emergencies? (2026.findings-acl)
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| Challenge: | Existing research has focused on the earlier stages of emergency response . lack of suitable datasets for reliable and compliance-aware decision-oriented modeling and evaluation is limiting current research . |
| Approach: | They propose a first real-world emergency decision-making dataset EDM-Bench . they propose 'rule-enhanced reasoning framework' that integrates external regulatory knowledge with constrained inference mechanisms to improve both decision safety and interpretability. |
| Outcome: | The proposed framework improves decision safety and interpretability by integrating regulatory knowledge with constrained inference mechanisms. |
Semantic Parsing with Dual Learning (P19-1)
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| Challenge: | Existing approaches to parse natural language queries are limited by lack of labeled data and constrained decoding. |
| Approach: | They propose a semantic parsing framework with the dual learning algorithm that makes full use of data through a dual-learning game. |
| Outcome: | The proposed approach achieves state-of-the-art performance on ATIS dataset and gets competitive performance on overnight dataset. |
ClinAlign: Scaling Healthcare Alignment from Clinician Preference (2026.findings-acl)
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Shiwei Lyu, Xidong Wang, Hao Zhu, Lei Liu, Chaohe Zhang, Jian Wang, Jinjie Gu, Benyou Wang, Yue Shen
| Challenge: | Existing methods for aligning open-ended outputs with fine-grained clinician preferences are weakly grounded in professional guidelines. |
| Approach: | They propose a framework to align large language models' outputs with fine-grained clinician preferences . they propose 119 broadly reusable, clinically grounded principles organized by clinical dimensions . |
| Outcome: | The proposed framework outperforms existing models on HealthBench-Hard and Deepseek-R1 and o3. |
WinoLogic: A Zero-Shot Logic-based Diagnostic Dataset for Winograd Schema Challenge (2021.emnlp-main)
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| Challenge: | Recent success of neural language models on the Winograd Schema Challenge has called for further investigation of commonsense reasoning ability of these models. |
| Approach: | They propose a logic-based framework that focuses on high-quality commonsense knowledge. |
| Outcome: | The proposed framework focuses on high-quality commonsense knowledge. |
Fantastic Semantics and Where to Find Them: Investigating Which Layers of Generative LLMs Reflect Lexical Semantics (2024.findings-acl)
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| Challenge: | Existing research suggests that contextual representations of large language models exhibit subpar performance in downstream tasks, struggling to fully capture the semantic nuances of words. |
| Approach: | They investigate the bottom-up evolution of lexical semantics for a popular LLM . they probing its hidden states at the end of each layer using a contextualized word identification task . |
| Outcome: | The proposed model is able to encode lexical semantics in lower layers while achieving weaker induction in higher layers. |
Quite Good, but Not Enough: Nationality Bias in Large Language Models - a Case Study of ChatGPT (2024.lrec-main)
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| Challenge: | Nationality is a key demographic element that enhances the performance of large language models, but it has received less scrutiny regarding inherent biases. |
| Approach: | They investigated nationality bias in ChatGPT, a large language model for text generation. |
| Outcome: | The proposed model generates 4,680 discourses about nationality in Chinese and English, with 195 countries, 4 temperature settings, and 3 prompt types. |
Agentic-R: Learning to Retrieve for Agentic Search (2026.findings-acl)
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| Challenge: | Existing retrievers for single-turn retrieval-augmented generation (RAG) rely on similarity-based retrievers, but similar passages are not always useful for final answer generation. |
| Approach: | They propose a retrieval-augmented-generation retriever that integrates reasoning with retrieval . they use local query-passage relevance and global answer correctness to measure passage utility . |
| Outcome: | The proposed retriever outperforms existing retrievers on QA benchmarks on seven single-hop and multi-hop searches. |
PychoAgent: Psychology-driven LLM Agents for Explainable Panic Prediction on Social Media during Sudden Disaster Events (2025.emnlp-main)
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Mengzhu Liu, Zhengqiu Zhu, Chuan Ai, Chen Gao, Xinghong Li, Lingnan He, Kaisheng Lai, Yingfeng Chen, Xin Lu, Yong Li, Quanjun Yin
| Challenge: | Social media's rich information content and spatiotemporal granularity provide unique opportunities for emotion prediction and management. |
| Approach: | They propose a Psychology-driven generative Agent framework for explainable panic prediction based on emotion arousal theory. |
| Outcome: | The proposed framework improves panic emotion prediction performance by 13% to 21% compared to baseline models. |
Aligning Retrieval with Reader Needs: Reader-Centered Passage Selection for Open-Domain Question Answering (2025.coling-main)
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Chunlei Xin, Shuheng Zhou, Xuanang Chen, Yaojie Lu, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing retrieval methods aim to gather relevant passages but fail to prioritize consistent and useful information for the reader. |
| Approach: | They propose a novel method which re-ranks passages based on the reader's prediction probability distribution and clusters passage according to the predicted answers. |
| Outcome: | The proposed method improves the quality of evidence passages under zero-shot scenarios. |
Exploring the Capability Boundaries of LLMs in Mastering of Chinese Chouxiang Language (2026.findings-acl)
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Dianqing Lin, Tian Lan, Jiali Zhu, Jiang Li, Wei Chen, Xu Liu, null Aruukhan, Xiangdong Su, Hongxu Hou, Guanglai Gao
| Challenge: | Current state-of-the-art LLMs exhibit clear limitations on multiple tasks, while performing well on tasks that involve contextual semantic understanding. |
| Approach: | They propose a mouse-based benchmark to evaluate LLMs' performance on NLP tasks involving Chouxiang Language. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs on six NLP tasks involving Chouxiang Language. |
Enhancing Chinese Pre-trained Language Model via Heterogeneous Linguistics Graph (2022.acl-long)
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| Challenge: | Experimental results show that pre-trained Chinese language models ignore linguistics knowledge to learn representations. |
| Approach: | They propose a task-free enhancement module to integrate linguistics knowledge into Chinese pre-trained language models. |
| Outcome: | The proposed model improves Chinese pre-trained language models on 6 tasks with 10 benchmark datasets. |
CTC-based Non-autoregressive Speech Translation (2023.acl-long)
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Chen Xu, Xiaoqian Liu, Xiaowen Liu, Qingxuan Sun, Yuhao Zhang, Murun Yang, Qianqian Dong, Tom Ko, Mingxuan Wang, Tong Xiao, Anxiang Ma, Jingbo Zhu
| Challenge: | End-to-end speech translation (E2E ST) and non-autoregressive (NAR) generation are promising in language and speech processing for their advantages of less error propagation and low latency. |
| Approach: | They develop a model that uses connectionist temporal classification to predict the source and target texts. |
| Outcome: | The proposed model achieves an average BLEU score of 29.5 with a speed-up of 5.67. |
BCL: Bayesian In-Context Learning Framework for Information Extraction (2026.findings-acl)
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Haoliang Liu, Chengkun Cai, Xu Zhao, Han Zhu, Shizhou Huang, Xinglin Zhang, Tao Chen, Jenq-Neng Hwang, Zhang Huaping, Lei Li
| Challenge: | Existing information extraction (IE) tasks rely on in-context learning with large language models. |
| Approach: | They propose a Bayesian-based in-context learning framework that refines label representations across IE tasks using particle filtering and Bayes updates. |
| Outcome: | The proposed framework improves performance over existing methods (up to 30%) it underperforms one-shot prompting by a substantial margin on NER tasks and CodeIE fails on RE tasks with near-zero micro-F1. |
Multilingual Contrastive Decoding via Language-Agnostic Layers Skipping (2024.findings-emnlp)
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| Challenge: | Decoding by contrasting layers (DoLa) is designed to improve the generation quality of large language models (LLMs) however, this approach does not work well on non-English tasks. |
| Approach: | They propose a contrastive decoding algorithm that uses amateur logits to contrast with the output of an expert model's early exit logits. |
| Outcome: | The proposed method outperforms baselines and significantly improves chain-of-thought reasoning accuracy across 11 languages. |
Incorporating Chinese Characters of Words for Lexical Sememe Prediction (P18-1)
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| Challenge: | Existing methods of lexical sememe prediction rely on external context information of words to represent meaning. |
| Approach: | They propose a character-enhanced sememe prediction framework for Chinese language that takes advantage of internal character information and external context information. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on a Chinese sememe knowledge base and maintains robust performance even for low-frequency words. |
ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)
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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang
| Challenge: | Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE). |
| Approach: | They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE. |
| Outcome: | The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency. |
Leveraging Explicit Lexico-logical Alignments in Text-to-SQL Parsing (2022.acl-short)
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| Challenge: | Text-to-SQL parsing aims to parse natural language questions into SQL queries . current attention-based approaches can only model alignments at the token level . |
| Approach: | They propose a method to leverage explicit lexico-logical alignments by identifying possible phrase-level alignments and injecting them as additional contexts into the parsing procedure. |
| Outcome: | The proposed approach improves performance by 3.4% on Squall. |
Retrieval-Enhanced Adversarial Training for Neural Response Generation (P19-1)
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| Challenge: | Existing approaches to dialogue systems are labor-intensive and difficult to scale up. |
| Approach: | They propose a Retrieval-Enhanced Adversarial Training method for neural response generation that leverages an adversarial training paradigm while taking advantage of N-best response candidates from a retrieval-based system to construct the discriminator. |
| Outcome: | The proposed method outperforms the vanilla Seq2Seq model and conventional adversarial training approach on a large scale dataset. |
Sherry: Hardware-Efficient 1.25-Bit Ternary Quantization via Fine-grained Sparsification (2026.acl-long)
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| Challenge: | ternary quantization is a powerful solution for resource-constrained edge devices . current implementations suffer from a fundamental misalignment with commodity hardware . |
| Approach: | They propose a hardware-efficient ternary quantization framework that packs weights into five bits to restore power-of-two alignment. |
| Outcome: | The proposed framework reduces weights to -1, 0, +1 while preserving power-of-two alignment. |
Abstract Meaning Representation-Based Logic-Driven Data Augmentation for Logical Reasoning (2024.findings-acl)
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Qiming Bao, Alex Peng, Zhenyun Deng, Wanjun Zhong, Gael Gendron, Timothy Pistotti, Neset Tan, Nathan Young, Yang Chen, Yonghua Zhu, Paul Denny, Michael Witbrock, Jiamou Liu
| Challenge: | Empirical evidence shows that our proposed method improves performance across seven downstream tasks. |
| Approach: | They propose a logic-driven data augmentation approach that converts text into AMR graphs and converts them back into text to create augmented data. |
| Outcome: | The proposed method leads on the ReClor leaderboard and improves on seven downstream tasks. |
T2A-Feedback: Improving Basic Capabilities of Text-to-Audio Generation via Fine-grained AI Feedback (2025.acl-long)
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Zehan Wang, Ke Lei, Chen Zhu, Jiawei Huang, Sashuai Zhou, Luping Liu, Xize Cheng, Shengpeng Ji, Zhenhui Ye, Tao Jin, Zhou Zhao
| Challenge: | Text-to-audio (T2A) models still struggle to satisfy human preferences for prompt-following and acoustic quality when generating complex multi-event audio. |
| Approach: | They propose to use AI feedback learning to enhance basic capabilities of text-to-audio models . they use a large audio preference dataset to evaluate the model's capabilities . |
| Outcome: | The proposed model improves in simple and complex scenarios with AI feedback learning. |
Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph (2025.findings-acl)
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| Challenge: | Existing methods to address toxicity issues with large language models are inadequate . lack of domain-specific knowledge leads to false negatives and excessive sensitivity to toxic speech limits freedom of speech. |
| Approach: | They propose a method that leverages graph search on a meta-toxic knowledge graph to enhance hatred and toxicity detection. |
| Outcome: | The proposed method lowers false positive rate and improves toxicity detection performance in out-of-domain scenarios. |
HEAL: A Hypothesis-Based Preference-Aware Analysis Framework (2025.findings-emnlp)
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Yifu Huo, Chenglong Wang, Qiren Zhu, Shunjie Xing, Tong Xiao, Chunliang Zhang, Tongran Liu, JingBo Zhu
| Challenge: | Preference optimization methods like DPO are often evaluated on a single response, overlooking other outputs. |
| Approach: | They propose a Hypothesis-based PrEference-aware AnaLysis Framework that formulates preference alignment as a re-ranking process within hypothesis spaces. |
| Outcome: | The proposed evaluation paradigm re-ranks preference alignment as a reranking process within hypothesis spaces. |
Position IDs Matter: An Enhanced Position Layout for Efficient Context Compression in Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods to compress context information ignore holistic contextual dependencies. |
| Approach: | They propose a method that adjusts position encodings to minimize the distance between context tokens and special tokens. |
| Outcome: | Enhanced Position Layout (EPL) improves compression of context information in large language models. |
MuSC: Improving Complex Instruction Following with Multi-granularity Self-Contrastive Training (2025.acl-long)
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| Challenge: | Existing methods for complex instruction-following with elaborate constraints rely on a weaker model, especially GPT-4, limiting their application. |
| Approach: | They propose a Multi-granularity Self-Contrastive Training framework to improve instruction alignment without relying on a stronger model. |
| Outcome: | The proposed framework improves instruction-following with elaborate constraints without external supervision on coarse and fine granularity. |
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification (D19-1)
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| Challenge: | Few-shot domain adaptation and NOTA detection are two real-world challenges for few-shot relation classification models. |
| Approach: | They propose a task to investigate two aspects of few-shot relation classification models . they build upon the FewRel dataset by adding a new test set in a different domain . |
| Outcome: | The proposed task can evaluate few-shot domain adaptation and few- shot none-of-the-above detection on a new domain and NOTA relation choice. |
R3Mem: Bridging Memory Retention and Retrieval via Reversible Compression (2025.findings-acl)
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| Challenge: | Existing memory solutions that store information via parameters struggle with reliable retrieval. |
| Approach: | They propose a memory network that optimizes both information Retention and Retrieval through Reversible context compression. |
| Outcome: | The proposed memory network outperforms conventional memory modules in long-horizon interaction tasks like conversational agents and achieves state-of-the-art performance in language modeling and retrieval-augmented generation tasks. |
OPAL: Ontology-Aware Pretrained Language Model for End-to-End Task-Oriented Dialogue (2023.tacl-1)
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| Challenge: | Existing task-oriented dialogue systems lack ontology-aware pretraining methods for task-orientated dialogue. |
| Approach: | They propose an ontology-aware pretrained language model (OPAL) for end-to-end task-oriented dialogue (TOD) . they propose to pretrain on large-scale contextual text data to bridge the gap between the pretraining method and downstream tasks. |
| Outcome: | The proposed model achieves an exciting boost and obtains competitive performance even without any TOD data on CamRest676 and MultiWOZ benchmarks. |
What Did You Refer to? Evaluating Co-References in Dialogue (2021.findings-acl)
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| Challenge: | Existing neural end-to-end dialogue models have limitations on exactly interpreting the linguistic structures in dialogue history context. |
| Approach: | They propose to directly measure the capability of neural end-to-end dialogue models on understanding the entity-oriented structures via question answering. |
| Outcome: | The proposed model can understand large-scale English and Chinese human human dialogues using a large-format dataset. |
MdEval: Massively Multilingual Code Debugging (2026.findings-acl)
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Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li
| Challenge: | Existing benchmarks primarily focus on Python and are limited in terms of language diversity. |
| Approach: | They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions. |
| Outcome: | The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task. |
DGLF: A Dual Graph-based Learning Framework for Multi-modal Sarcasm Detection (2024.emnlp-main)
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Zhihong Zhu, Kefan Shen, Zhaorun Chen, Yunyan Zhang, Yuyan Chen, Xiaoqi Jiao, Zhongwei Wan, Shaorong Xie, Wei Liu, Xian Wu, Yefeng Zheng
| Challenge: | Existing methods for multimodal sarcasm detection neglect high-order relationships and underestimate high-frequency messages. |
| Approach: | They propose a Dual Graph-based Learning Framework to capture inter-modal inconsistencies . they propose combining a hypergraph and a vanilla graph to achieve enhanced propagation . |
| Outcome: | The proposed model outperforms existing state-of-the-art methods on two benchmark datasets. |
Cross-lingual Lexical Sememe Prediction (D18-1)
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| Challenge: | Sememes are defined as the minimum semantic units of human languages . but most languages do not have sememe-based linguistic knowledge bases . a new framework is proposed to predict sememes for words in other languages based on semems . |
| Approach: | They propose a framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction. |
| Outcome: | The proposed model improves on baseline methods on real-world datasets. |
EMTIR-GRPO: Efficient Multi-Tool Augmented Large Language Models via Reinforcement Learning (2026.findings-acl)
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| Challenge: | Tool-integrated reasoning (TIR) enables large language models to invoke external tools for tasks beyond their internal capacity but often suffers from tool overuse. |
| Approach: | They propose an algorithm that uses a composite reward to model tool costs and tool efficiency. |
| Outcome: | The proposed algorithm models heterogeneous tool costs and encourages more cost-effective tool-use strategies. |
ARTIST: A Transformer-based Chinese Text-to-Image Synthesizer Digesting Linguistic and World Knowledge (2022.findings-emnlp)
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| Challenge: | Text-to-Image Synthesis (TIS) is a popular task to convert natural language texts into realistic images. |
| Approach: | They propose a transformer-based Chinese text-to-image synthesizer for high-resolution image generation that incorporates linguistic and relational knowledge facts into the model to ensure better performance without the usage of ultra-large models. |
| Outcome: | The proposed model outperforms existing models in Chinese with linguistic and relational knowledge facts. |
Benchmarking Long-Context Language Models on Long Code Understanding (2025.acl-long)
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Jia Li, Xuyuan Guo, Lei Li, Kechi Zhang, Ge Li, Jia Li, Zhengwei Tao, Fang Liu, Chongyang Tao, Yuqi Zhu, Zhi Jin
| Challenge: | Currently, long-context language models are limited by the lack of a rigorous evaluation framework for long code understanding. |
| Approach: | They propose to use a long code understanding benchmark LongCodeU to evaluate LCLMs' long code comprehension ability for practical applications. |
| Outcome: | The proposed benchmarks show that current LCLMs are limited in their long code understanding ability, particularly when the long code length is greater than 32K, falling far short of their claimed 128K to 1M context windows. |
ParaLS: Lexical Substitution via Pretrained Paraphraser (2023.acl-long)
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| Challenge: | Lexical substitution (LS) is an extremely powerful technology that can be used as a backbone of various NLP applications such as writing assistance. |
| Approach: | They propose two simple decoding strategies that focus on the variations of the target word during decoding to generate substitutes from a paraphraser. |
| Outcome: | The proposed methods outperform state-of-the-art LS methods based on pre-trained language models on three benchmarks. |
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. |
Unsupervised Hierarchical Topic Modeling via Anchor Word Clustering and Path Guidance (2024.findings-emnlp)
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| Challenge: | Existing hierarchical topic models often ignore the role of anchor words that guide text generation. |
| Approach: | They propose to use a clustering algorithm to detect anchor words that are highly consistent with every topic and add a causal path to the popular Variational Auto-Encoder framework. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three datasets. |
Confidence-Calibrated Small-Large Language Model Collaboration for Cost-Efficient Reasoning (2026.eacl-long)
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| Challenge: | Large language models (LLMs) have superior reasoning capabilities compared to small language models, but incur substantially higher inference costs. |
| Approach: | They propose a system that cascades an LLM with an SLM to achieve a balance between accuracy and cost in complex reasoning tasks. |
| Outcome: | The proposed system improves the SLM’s reasoning ability and confidence calibration across diverse datasets and model backbones. |
Improving Grammatical Error Correction via Contextual Data Augmentation (2024.findings-acl)
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| Challenge: | Increasing use of synthetic data due to inconsistent error distribution and noisy labels is limiting the use of these data. |
| Approach: | They propose a method for augmentation of synthetic data with a more consistent error distribution. |
| Outcome: | The proposed method outperforms strong baselines and achieves state-of-the-art with only a few synthetic data. |
UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot Summarization (2023.acl-long)
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| Challenge: | a new benchmark summarization model is being developed to train few-shot summarizers . a large number of summarizing tasks are required to perform well in heterogeneous datasets. |
| Approach: | They propose a few-shot summarization model pre-trained with multiple summarizing tasks . they propose 'uniSumm' to be prefix-tuned to excel at any few-shot summarisation task . |
| Outcome: | The proposed model outperforms baseline models under automatic and human evaluations and achieves comparable results in human evaluation. |
Few-Shot Table Understanding: A Benchmark Dataset and Pre-Training Baseline (2022.coling-1)
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| Challenge: | Pre-trained language models have demonstrated their effectiveness for few-shot table understanding, but few-shoot table understanding is rarely explored due to the deficiency of public table pre-training corpus and well-defined downstream benchmark tasks. |
| Approach: | They establish a benchmark dataset and use it to explore few-shot table understanding in Chinese. |
| Outcome: | The proposed model improves the few-shot table understanding in Chinese. |
Automated Generation of Accurate & Fluent Medical X-ray Reports (2021.emnlp-main)
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| Challenge: | Existing medical report generation efforts focus on producing human-readable reports, yet the generated text may not be well aligned to the clinical facts. |
| Approach: | They propose to automate the generation of medical reports from chest X-ray image inputs . medical reports are the primary medium, which physicians communicate findings from scans - authors say . |
| Outcome: | The proposed method achieves fluency and clinical accuracy on common metrics. |
A Top-down Graph-based Tool for Modeling Classical Semantic Maps: A Case Study of Supplementary Adverbs (2025.naacl-long)
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| Challenge: | Semantic map models (SMMs) construct a network-like conceptual space from cross-linguistic instances or forms based on the connectivity hypothesis. |
| Approach: | They propose a graph-based algorithm that automatically generates conceptual spaces and SMMs in a top-down manner. |
| Outcome: | The proposed model is compared with human annotations and other automated methods on cross-linguistic supplementary adverbs. |
MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks (2026.acl-long)
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Junhao Ruan, Abudukeyumu Abudula, Bei Li, Yongjing Yin, Xinyu Liu, Kechen Jiao, Xin Chen, Jingang Wang, Xunliang Cai, Tong Xiao, JingBo Zhu
| Challenge: | Existing conversational retrieval benchmarks suffer from costly, sparse human annotation or rigid, unnatural automated heuristics. |
| Approach: | They propose a framework for auditing, synthesizing, and benchmarking conversational retrieval. |
| Outcome: | The proposed framework is based on three LLM-based auditors and a multi-agent system . it mimics production-style challenges (hard topic switching, verbosity) and offers superior discriminative power. |
IIET: Efficient Numerical Transformer via Implicit Iterative Euler Method (2025.emnlp-main)
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Xinyu Liu, Bei Li, Jiahao Liu, Junhao Ruan, Kechen Jiao, Hongyin Tang, Jingang Wang, Tong Xiao, JingBo Zhu
| Challenge: | High-order numerical methods enhance performance in tasks like NLP but introduce a performance-efficiency trade-off due to increased computational overhead. |
| Approach: | They propose an iterative implicit Euler Transformer which simplifies high-order numerical methods by iterating implicit Eule. |
| Outcome: | The proposed method improves accuracy and reduces inference overhead by 55% while retaining 99.4% of the original task accuracy. |
kNN-BOX: A Unified Framework for Nearest Neighbor Generation (2024.eacl-demo)
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| Challenge: | kNN-BOX enables quick development and visualization for novel generation paradigm . Currently, knn-BOx has provided implementation of seven popular kN-MT variants . |
| Approach: | They propose a framework which decomposes the datastore-augmentation approach into three modules . they apply kNN-BOX to machine translation and three other tasks . |
| Outcome: | The proposed framework decomposes the datastore-augmentation approach into three modules . it provides implementation of seven popular kNN-MT variants, covering research from performance enhancement to efficiency optimization. |
FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrieval (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) are proficient at retrieving single facts from extended contexts, but struggle with tasks requiring simultaneous retrieval of multiple facts. |
| Approach: | They propose a method that refines context through successive rounds of rewriting to address this problem by finding all Crucial Texts (FACT) |
| Outcome: | The proposed method improves multi-fact retrieval performance across tasks, though improvements are less notable in general-purpose QA scenarios. |
A Survey on Natural Language Processing for Programming (2024.lrec-main)
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| Challenge: | Natural language processing for programming is a field of NLP and software engineering . it is used to assist programming, and is increasingly prevalent for its effectiveness in improving productivity. |
| Approach: | They propose to use NLP techniques to assist programming by obtaining a structure-based representation and a functionality-oriented algorithm. |
| Outcome: | The proposed approach could relieve developers from laborious work while improving efficiency for non-professional users. |
Question Tells You Where the Answer Is: Intention-aware Long-Context KV Cache Compression (2026.acl-long)
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Liang Zhao, Xiaocheng Feng, Weihong Zhong, Lei Huang, Kun Zhu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | Recent methods to reduce the KV cache size fail to identify crucial KVs for generation while excluding others accurately, resulting in severe information loss. |
| Approach: | They propose an intention-aware KV cache eviction method that identifies and retains crucial KVs according to the attention distribution of intention, which semantically reflects the user’s goal and determines which part of the context is relevant. |
| Outcome: | The proposed method can maintain the model performance while reducing the KV cache size from 128K to 2K, leading to a 6.3x increase in decoding speed and 7.8x enhancement in memory efficiency compared to the default setting. |
Sliding Windows Are Not the End: Exploring Full Ranking with Long-Context Large Language Models (2025.acl-long)
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| Challenge: | Existing methods for listwise passage ranking use sliding window approach, which is inefficient as it requires repetitive and serialized processing. |
| Approach: | They propose a listwise label construction approach and importance-aware learning objective for full ranking. |
| Outcome: | The proposed method outperforms existing methods in listwise ranking tasks. |
RelCLIP: Adapting Language-Image Pretraining for Visual Relationship Detection via Relational Contrastive Learning (2022.emnlp-main)
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| Challenge: | Existing visual relationship detection models only use numeric ids of relation labels for training, but ignore semantic correlation between labels. |
| Approach: | They propose a visual Relationship prediction framework that transfers natural language knowledge from Contrastive Language-Image Pre-training models to enhance the relationship prediction. |
| Outcome: | The proposed framework improves visual relationship prediction by matching semantic correlations with relation triplets. |
FaStFact: Faster, Stronger Long-Form Factuality Evaluations in LLMs (2025.findings-emnlp)
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Yingjia Wan, Haochen Tan, Xiao Zhu, Xinyu Zhou, Zhiwei Li, Qingsong Lv, Changxuan Sun, Jiaqi Zeng, Yi Xu, Jianqiao Lu, Yinhong Liu, Zhijiang Guo
| Challenge: | Prior evaluation pipelines fail to evaluate factuality of long-form LLMs due to inefficiency and costly human assessment. |
| Approach: | They propose a fast and strong evaluation pipeline that can evaluate factuality of long-form LLMs . they propose 'faStFact' to reduce cost of web searching and inference calling . |
| Outcome: | The proposed evaluation pipeline achieves highest alignment with human evaluation and efficiency among existing baselines. |
GLTW: Joint Improved Graph Transformer and LLM via Three-Word Language for Knowledge Graph Completion (2025.findings-acl)
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Kangyang Luo, Yuzhuo Bai, Cheng Gao, Shuzheng Si, Zhu Liu, Yingli Shen, Zhitong Wang, Cunliang Kong, Wenhao Li, Yufei Huang, Ye Tian, Xuantang Xiong, Lei Han, Maosong Sun
| Challenge: | Existing knowledge graphs lack the ability to integrate structural information into LLMs and output predictions deterministically. |
| Approach: | They propose a method which encodes structural information of KGs and merges it with LLMs to enhance KGC performance. |
| Outcome: | The proposed method improves the performance of KG Completion datasets on KGs by integrating structural information with LLMs. |
TwinVoice: A Multi-dimensional Benchmark Towards Digital Twins via LLM Persona Simulation (2026.findings-acl)
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Bangde Du, Minghao Guo, Songming He, Ziyi Ye, Xi Zhu, Weihang Su, Shuqi Zhu, Yujia Zhou, Yongfeng Zhang, Qingyao Ai, Yiqun Liu
| Challenge: | Existing studies show that advanced LLMs produce text indistinguishable from human writing. |
| Approach: | They propose a benchmark to assess persona simulation across diverse contexts by decomposing the evaluation into six fundamental capabilities including opinion consistency, memory recall, logical reasoning, persona tone, and syntactic style. |
| Outcome: | The proposed model achieves moderate accuracy but falls short of the basic capabilities needed to simulate personas in real-world contexts. |
MACSum: Controllable Summarization with Mixed Attributes (2023.tacl-1)
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Yusen Zhang, Yang Liu, Ziyi Yang, Yuwei Fang, Yulong Chen, Dragomir Radev, Chenguang Zhu, Michael Zeng, Rui Zhang
| Challenge: | Existing work on controllable summarization with mixed attributes lacks designated annotations. |
| Approach: | They propose a human-annotated summarization benchmark for controllable summarizing with mixed attributes based on news and dialogue sources . |
| Outcome: | The proposed dataset contains human-annotated summarization datasets with mixed attributes . hard prompt models yield the best performance on most metrics and human evaluations . mixed-attribute control is still challenging for summarizing tasks . |
Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospects (2025.findings-emnlp)
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| Challenge: | Multimodal Emotion Recognition in Conversations (MERC) is a new way to enhance human-computer interaction. |
| Approach: | This survey offers a systematic overview of Multimodal Emotion Recognition in Conversations . it examines motivations, core tasks, representative methods, and evaluation strategies . |
| Outcome: | The survey examines the effectiveness of MERC and its evaluation strategies. |
DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships (2023.acl-long)
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| Challenge: | Existing models that describe concepts in everyday situations are difficult to summarize in a single sentence. |
| Approach: | They propose DimonGen, which generates sentences describing concept relationships in everyday scenarios. |
| Outcome: | The proposed model outperforms baseline models in terms of quality and diversity of generated sentences. |
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching (2022.findings-naacl)
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Kunbo Ding, Weijie Liu, Yuejian Fang, Zhe Zhao, Qi Ju, Xuefeng Yang, Rong Tian, Zhu Tao, Haoyan Liu, Han Guo, Xingyu Bai, Weiquan Mao, Yudong Li, Weigang Guo, Taiqiang Wu, Ningyuan Sun
| Challenge: | Existing studies have shown that cross-lingual knowledge distillation can improve the performance of pre-trained models for cross-linguistic similarity matching tasks. |
| Approach: | They propose a multi-stage distillation framework for constructing a small-size but high-performance cross-lingual model using contrastive learning, bottleneck, and parameter recurrent strategies. |
| Outcome: | The proposed model can compress the size of XLM-R and MiniLM by more than 50% while the performance is only reduced by about 1%. |
Dialogue is Better Than Monologue: Instructing Meidcal LLMs via Strategic Conversations (2026.findings-eacl)
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Zijie Liu, Xinyu Zhao, Jie Peng, Jinhao Duan, Zhuangdi Zhu, Qingyu Chen, Kaidi Xu, Xia Hu, Tianlong Chen
| Challenge: | Existing tuning methods for medical AI models are monologue-based . existing benchmarks are based on licensing exams or research articles . |
| Approach: | They propose a benchmark to expose limitations of monologue-based tuning for medical AI models . they use a large dialogue dataset to capture stepwise diagnostic reasoning . |
| Outcome: | The proposed model outperforms monologue-tuned models on a medical question answering task and improves accuracy on standard medical QA benchmarks. |
Program Enhanced Fact Verification with Verbalization and Graph Attention Network (2020.emnlp-main)
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| Challenge: | Existing methods for fact verification based on structured data are challenging and require further study. |
| Approach: | They propose a program-enhanced verbalization and a graph attention network to integrate programs and execution into textual inference models. |
| Outcome: | The proposed framework achieves a new state-of-the-art accuracy on a benchmark dataset . it is compared with existing frameworks on symbolic and informal inference models . |
XMC-Agent : Dynamic Navigation over Scalable Hierarchical Index for Incremental Extreme Multi-label Classification (2024.findings-acl)
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Yanjiang Liu, Tianyun Zhong, Yaojie Lu, Hongyu Lin, Ben He, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing methods for XMC struggle with the growing set of labels due to their static label assumptions, and embedding-based methods struggle with complex mapping relationships due to late interaction paradigm. |
| Approach: | They propose a large language model (LLM) powered agent framework for extreme multi-label classification, XMC-Agent, which can effectively learn, manage and predict the extremely large and dynamically increasing set of labels. |
| Outcome: | The proposed framework can learn, manage and predict the extremely large and dynamically growing set of labels and achieves state-of-the-art performance on three standard datasets. |
Astra: Activation-Space Tail-Eigenvector Low-Rank Adaptation of Large Language Models (2026.findings-acl)
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| Challenge: | Existing methods for fine-tuning pre-trained models are limited due to suboptimal activation subspaces. |
| Approach: | They propose a method that leverages tail eigenvectors of model output activations to construct low-rank adapters. |
| Outcome: | The proposed method outperforms existing methods across 16 benchmarks and surpasses full fine-tuning in certain scenarios. |
Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs (2026.acl-long)
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| Challenge: | Experiments on 13 Omni-LLMs reveal systematic weaknesses in cross-modal coreference . cross-module coreference is a crucial missing piece for advancing robust omni-modal reasoning. |
| Approach: | They propose a cross-modal coreference problem to evaluate and enhance Omni-LLMs' reasoning capabilities. |
| Outcome: | Experiments on 13 Omni-LLMs show they lack coreference-aware thinking patterns . the CROSSOMNI dataset yields significant performance gains and generalizes well to collaborative reasoning tasks. |
Context-Sensitive Generation of Open-Domain Conversational Responses (C18-1)
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| Challenge: | Existing studies on single-turn conversation generation focus on coherence and context-sensitive generation of open-domain conversational responses. |
| Approach: | They propose static and dynamic attention based approaches for context-sensitive generation of open-domain conversational responses. |
| Outcome: | The proposed model outperforms all baselines on automatic and human evaluation on two public datasets. |
Make Large Language Model a Better Ranker (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) demonstrate robust capabilities across various fields . current list-wise approaches fail in ranking tasks due to misalignment between ranking objectives and next-token prediction . |
| Approach: | They propose a large language model framework with Aligned Listwise Ranking Objectives (ALRO) this framework provides explicit feedback in a listwise manner by introducing soft lambda loss . |
| Outcome: | The proposed model outperforms existing recommendation methods and embedding-based recommendations without additional computational burdens. |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
MoE Adapter for Large Audio Language Models: Sparsity, Disentanglement, and Gradient-Conflict-Free (2026.findings-acl)
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Yishu Lei, Shuwei He, Hu Jing, Dan Zhang, Xianlong Luo, Danxiang Zhu, Shikun Feng, Rui Liu, Jingzhou HE, Yu Sun, Hua Wu, Haifeng Wang
| Challenge: | Existing research on Large Language Models (LLMs) limited to textual input modality . acoustic information is intrinsically heterogeneous, entangling attributes such as speech, music, and environmental context. |
| Approach: | They propose a sparse Mixture-of-Experts architecture to decouple acoustic information by routing audio tokens to specialized experts. |
| Outcome: | The proposed architecture outperforms existing models on audio semantic and paralinguistic tasks while retaining shared experts for global context. |
Debiasing In-Context Learning by Instructing LLMs How to Follow Demonstrations (2024.findings-acl)
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Lvxue Li, Jiaqi Chen, Xinyu Lu, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | In-context learning (ICL) has gained considerable attention due to its data efficiency and task adaptability. |
| Approach: | They propose to de-biase demonstration bias in in-context learning by focusing on semantic ambiguity induced by demonstrations and reducing the semantic hazard. |
| Outcome: | The proposed methods significantly improve performance on six datasets. |
Single LLM, Multiple Roles: A Unified Retrieval-Augmented Generation Framework Using Role-Specific Token Optimization (2025.emnlp-main)
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| Challenge: | Existing studies have optimized retrieval-augmented generation (RAG) across sub-tasks, but integrating these optimizations into a unified framework remains challenging. |
| Approach: | They propose a unified retrieval-augmented generation framework that optimizes role-specific tokens for multi-task processing. |
| Outcome: | The proposed framework achieves efficient multi-task processing through role-specific token optimization. |
PedagogyBench: A Cognitive-Driven Benchmark for Multimodal Instructional Video Understanding (2026.findings-acl)
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| Challenge: | Existing video understanding benchmarks do not adequately capture the pedagogical logic embedded in instructional videos. |
| Approach: | They propose a pedagogy-driven segmentation strategy and a dual-stream semantic injection pipeline that combines machine pre-annotation with expert refinement. |
| Outcome: | The proposed model performs well on discriminative tasks but degrades on higher-order pedagogical diagnosis, relying on parametric memory rather than grounded visual perception. |
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. |
A Multi-Expert Structural-Semantic Hybrid Framework for Unveiling Historical Patterns in Temporal Knowledge Graphs (2025.findings-acl)
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Yimin Deng, Yuxia Wu, Yejing Wang, Guoshuai Zhao, Li Zhu, Qidong Liu, Derong Xu, Zichuan Fu, Xian Wu, Yefeng Zheng, Xiangyu Zhao, Xueming Qian
| Challenge: | Existing methods focus on graph structure learning or semantic reasoning, lacking the capability to capture the inherent differences between historical and non-historical events. |
| Approach: | They propose a temporal knowledge graph reasoning framework that integrates both structural and semantic information to guide the reasoning process for different events. |
| Outcome: | The proposed framework integrates structural and semantic information to predict future events . it can provide evidence for many downstream tasks, including situation analysis and political decision making . |
ImCoref-CeS: An Improved Lightweight Pipeline for Coreference Resolution with LLM-based Checker-Splitter Refinement (2026.acl-long)
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Kangyang Luo, Yuzhuo Bai, Shuzheng Si, Cheng Gao, Zhitong Wang, Yingli Shen, Wenhao Li, Zhu Liu, Yufeng Han, Jiayi Wu, Cunliang Kong, Maosong Sun
| Challenge: | Existing supervised neural methods for coreference resolution are underexplored . current methods rely on small language models, but their potential is underexploited . |
| Approach: | They propose a framework that integrates an enhanced supervised model with LLM-based reasoning. |
| Outcome: | The proposed method surpasses existing state-of-the-art methods in coreference resolution. |
Fusion-Eval: Integrating Assistant Evaluators with LLMs (2024.emnlp-industry)
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| Challenge: | Recent studies have employed large language models (LLMs) as reference-free metrics for NLG evaluation, enhancing adaptability to new tasks tasks. |
| Approach: | They propose a method that leverages large language models to integrate insights from various assistant evaluators. |
| Outcome: | The proposed approach achieves a 0.962 system-level Kendall-Tau correlation with humans on SummEval and a 0.7444 turn-level Spearman correlation on TopicalChat, which is significantly higher than baseline methods. |
Forgetting Curve: A Reliable Method for Evaluating Memorization Capability for Long-Context Models (2024.emnlp-main)
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Xinyu Liu, Runsong Zhao, Pengcheng Huang, Chunyang Xiao, Bei Li, Jingang Wang, Tong Xiao, JingBo Zhu
| Challenge: | Existing methods, tasks and benchmarks to measure model’s effective memory length are limited. |
| Approach: | They propose a method called forgetting curve to measure the memorization capability of long-context models. |
| Outcome: | The proposed method is robust to the tested corpus and experimental settings, and can be applied to any model size. |
CoV: Chain-of-View Prompting for Spatial Reasoning (2026.findings-acl)
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Haoyu Zhao, Akide Liu, Zeyu Zhang, Weijie Wang, Feng Chen, Ruihan Zhu, Gholamreza Haffari, Bohan Zhuang
| Challenge: | Embodied question answering requires collecting context that is distributed across multiple viewpoints . most recent vision–language models (VLMs) are constrained to a fixed and finite set of input views . |
| Approach: | They propose a training-free, test-time reasoning framework that transforms a VLM into an active viewpoint reasoner through a coarse-to-fine exploration process. |
| Outcome: | The proposed framework improves LLM-Match performance by 11.98% on four mainstream VLMs. |
DYNTEXT: Semantic-Aware Dynamic Text Sanitization for Privacy-Preserving LLM Inference (2025.findings-acl)
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Juhua Zhang, Zhiliang Tian, Minghang Zhu, Yiping Song, Taishu Sheng, Siyi Yang, Qiunan Du, Xinwang Liu, Minlie Huang, Dongsheng Li
| Challenge: | Existing methods to protect privacy of sensitive data are differential privacy (DP) and DP is used to protect users from privacy leakage. |
| Approach: | They propose an LDP-based Dynamic Text sanitization for privacy-preserving LLM inference that dynamically constructs semantic-aware adjacency lists of sensitive tokens to sample non-sensitive tokens for perturbation. |
| Outcome: | The proposed model excels on three datasets. |
Learning Goal-oriented Dialogue Policy with opposite Agent Awareness (2020.aacl-main)
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| Challenge: | Existing approaches for goal-oriented dialogue policy learning focus on the target agent policy and treat the opposite agent policy as part of the environment. |
| Approach: | They propose a framework for policy learning in goal-oriented dialogues that uses the opposite agent's policy estimation to improve the target agent by regarding it as part of the target policy. |
| Outcome: | The proposed framework shows superior performance over state-of-the-art models on cooperative and competitive dialogue tasks. |
A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction (2022.coling-1)
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| Challenge: | Existing methods are poor at detecting complicated relations between aspects and opinions . detecting unclear boundaries of multi-word aspects and opinion is also a challenge . |
| Approach: | They propose a multi-task dual-tree network to extract triplets from a given sentence . they employ a constituency tree and a modified dependency tree to enhance the interaction . |
| Outcome: | The proposed model extracts triplets from a given sentence, and it is effective on four datasets. |
LAM SIMULATOR: Advancing Data Generation for Large Action Model Training via Online Exploration and Trajectory Feedback (2025.findings-acl)
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Thai Quoc Hoang, Kung-Hsiang Huang, Shirley Kokane, Jianguo Zhang, Zuxin Liu, Ming Zhu, Jake Grigsby, Tian Lan, Michael S Ryoo, Chien-Sheng Wu, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong, Juan Carlos Niebles
| Challenge: | Large Action Models (LAMs) face challenges due to the need for high-quality training data, especially for multi-steps tasks that involve planning, executing tool calls, and responding to feedback. |
| Approach: | They propose a framework for online exploration of agentic tasks with high-quality feedback . they use a dynamic task query generator and an extensive collection of tools to create a high-level feedback environment for LLM Agents. |
| Outcome: | The proposed framework achieves 49.3% performance improvement over baselines on toolbench and CRMArena. |
Machine Translation With Weakly Paired Documents (D19-1)
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| Challenge: | Recent studies explore the possibility of unsupervised machine translation with monolingual data only. |
| Approach: | They propose a method to mine bilingual sentences from weakly paired documents . they use word distribution-level alignments to constrain word distributions of two weakly-paired documents. |
| Outcome: | The proposed method outperforms previous results on six translation tasks using weakly paired bilingual documents and a large number of bilingual sentences. |
MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)
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Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang
| Challenge: | Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences. |
| Approach: | They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner. |
| Outcome: | The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner. |
Mem2Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation (2026.acl-long)
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Zihao Cheng, Zeming Liu, Yingyu Shan, Xinyi Wang, Xiangrong Zhu, Yunpu Ma, Hongru Wang, Yuhang Guo, Wei Lin, Yunhong Wang
| Challenge: | Existing frameworks that focus on static tools and static assets are ineffective for self-evolving agents. |
| Approach: | They propose a paradigm of co-evolutionary Capability Expansion and Experience Distillation that leverages accumulated experience to guide dynamic creation of assets. |
| Outcome: | The proposed framework improves performance in single-task and cross-task settings by 18.53% over standard LLMs, 11.80% over agents evolving solely through experience, and 6.46% over those evolving solelly through asset creation. |
Rethinking and Refining the Distinct Metric (2022.acl-short)
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| Challenge: | Existing methods for calculating distinct scores have evident biases that assign higher penalties to longer sequences. |
| Approach: | They propose to scale the number of distinct tokens based on their expectations. |
| Outcome: | The proposed metric removes evident biases in the original distinct score . the proposed meter correlates better with human judgment in evaluating response diversity . |
David vs. Goliath: Cost-Efficient Financial QA via Cascaded Multi-Agent Reasoning (2025.findings-emnlp)
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| Challenge: | Large language models have demonstrated remarkable reasoning capabilities, but performance in FQA remains limited. |
| Approach: | They propose a low-cost yet effective framework that enables small LLMs to perform complex reasoning tasks without expensive models. |
| Outcome: | The proposed framework outperforms the best open-source model on BizBench by 10.46% and achieves competitive performance to GPT-3.5 using significantly fewer parameters. |
Graph Neural Networks with Generated Parameters for Relation Extraction (P19-1)
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| Challenge: | Existing graph neural networks can only process multi-hop relational reasoning on pre-defined graphs and cannot be directly applied in natural language relational reasoning. |
| Approach: | They propose a graph neural network with generated parameters using natural language sentences as inputs. |
| Outcome: | The proposed model can process relational reasoning on graphs and in natural language processing tasks. |
The State of the Art of Large Language Models on Chartered Financial Analyst Exams (2024.emnlp-industry)
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Mahmoud Mahfouz, Ethan Callanan, Mathieu Sibue, Antony Papadimitriou, Zhiqiang Ma, Xiaomo Liu, Xiaodan Zhu
| Challenge: | Chartered Financial Analyst (CFA) program is widely recognized globally . study compares state-of-the-art large language models with open-source models . proprietary models pass levels I and II, but fail at level III due to essay questions . |
| Approach: | They benchmark five leading proprietary models and eight open-source models on mock CFA exams to provide an overview of their financial analysis capabilities. |
| Outcome: | The models on the mock CFA exams pass the highest scores, but fail at the lowest levels due to essay questions. |
SmartTrim: Adaptive Tokens and Attention Pruning for Efficient Vision-Language Models (2024.lrec-main)
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Zekun Wang, Jingchang Chen, Wangchunshu Zhou, Haichao Zhu, Jiafeng Liang, Liping Shan, Ming Liu, Dongliang Xu, Qing Yang, Bing Qin
| Challenge: | Experimental results show that SmartTrim accelerates the original model by 2-3 times with minimal performance degradation. |
| Approach: | They propose an adaptive acceleration framework which prunes redundant token representations and attention heads within each layer of the original model. |
| Outcome: | The proposed framework accelerates the original model by 2-3 times with minimal performance degradation across vision-language tasks. |
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding (2022.coling-1)
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Zhaoye Fei, Yu Tian, Yongkang Wu, Xinyu Zhang, Yutao Zhu, Zheng Liu, Jiawen Wu, Dejiang Kong, Ruofei Lai, Zhao Cao, Zhicheng Dou, Xipeng Qiu
| Challenge: | Existing methods to learn downstream tasks by stitches skill block lack rationality and interpretation. |
| Approach: | They propose a hierarchical framework with a coarse-to-fine paradigm for generalized text representations from the large-scale corpus. |
| Outcome: | The proposed model learns basic language properties from all tasks and boosts performance on relevant tasks. |
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database (2024.lrec-main)
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| Challenge: | Existing benchmarks for textual question answering only focus on single-chain or single-hop retrieval . Existing approaches to answer complex questions have limitations . |
| Approach: | They propose to conduct Graph-Hop, a novel multi-chains and multi-hops retrieval paradigm in complex question answering. |
| Outcome: | The proposed model provides explicit and fine-grained evidence graphs for complex question to support comprehensive and detailed reasoning. |
InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning (2026.acl-long)
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| Challenge: | Existing data selection methods suffer from severe domain specificity . existing methods for general instruction-following fail on reasoning tasks . |
| Approach: | They propose a framework that operationalizes contrastive entropy as a domain-adaptive selection criterion through warmup calibration, bi-directional NLL filtering, and entropic-based ranking. |
| Outcome: | Experiments show that InstructDiff outperforms baseline training on reasoning tasks while using only 10% of the data. |
Task-aware Contrastive Mixture of Experts for Quadruple Extraction in Conversations with Code-like Replies and Non-opinion Detection (2025.emnlp-main)
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| Challenge: | Applying Large Language Models (LLMs) for this specific task presents two primary challenges: the accurate extraction of multiple elements and the understanding of complex dialogue reply structure. |
| Approach: | They propose a novel LLM-based multi-task approach to extract sentiment quadruples from conversations by integrating expert-level contrastive loss within task-oriented mixture of experts layer. |
| Outcome: | The proposed method outperforms existing fine-tuning techniques in terms of accuracy and computational efficiency. |
Modeling Multi-turn Conversation with Deep Utterance Aggregation (C18-1)
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| Challenge: | Existing work on retrieval-based context modeling for multi-turn conversation ignores interactions among previous utterances. |
| Approach: | They propose retrieval-based response matching for multi-turn conversation . they propose to combine previous utterances into context using a deep utterrance aggregation model . |
| Outcome: | The proposed model outperforms state-of-the-art methods on three multi-turn conversation benchmarks including an e-commerce dialogue corpus. |
Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis (2024.findings-naacl)
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Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Shujian Huang, Lingpeng Kong, Jiajun Chen, Lei Li
| Challenge: | Existing studies show that large language models (LLMs) can handle multilingual machine translation (MMT) However, the multilingual translation ability of LLMs remains under-explored. |
| Approach: | They evaluate eight popular LLMs including ChatGPT and GPT-4 to determine their performance in multilingual machine translation. |
| Outcome: | The proposed model can generate moderate translation even on zero-resource languages and cross-lingual exemplars can provide better task guidance for low-resourced translation than exemplar in the same language pairs. |
Dynamic Multistep Reasoning based on Video Scene Graph for Video Question Answering (2022.naacl-main)
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| Challenge: | Existing video QA models lack the capacity for deep video understanding and flexible multistep reasoning. |
| Approach: | They propose a video question answering model which performs dynamic multistep reasoning between questions and videos. |
| Outcome: | The proposed model improves on three widely used video QA datasets and displays better interpretability by backtracing along with the attention mechanisms to the video scene graphs. |
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 . |
DyBBT: Dynamic Balance via Bandit-inspired Targeting for Dialog Policy with Cognitive Dual Systems (2026.acl-long)
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| Challenge: | Task oriented dialog systems often rely on static exploration strategies that do not adapt to dynamic dialog contexts. |
| Approach: | They propose a dialog policy learning framework that formalizes the exploration challenge through a structured cognitive state space C. |
| Outcome: | The proposed framework achieves SOTA performance in success rate, efficiency, and generalization. |
Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System (2022.naacl-industry)
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Tao Zhu, Zhe Zhao, Weijie Liu, Jiachi Liu, Yiren Chen, Weiquan Mao, Haoyan Liu, Kunbo Ding, Yudong Li, Xuefeng Yang
| Challenge: | Existing continual learning methods use data replay, parameter isolation and regularization to mitigate catastrophic forgetting. |
| Approach: | They propose a parameter-efficient continual learning framework that updates parameters offline and then trains using an online regularization method. |
| Outcome: | The proposed framework reduces catastrophic forgetting and saves the model with the changed parameters instead of all parameters. |
Aspect and Sentiment Aware Abstractive Review Summarization (C18-1)
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| Challenge: | Abstractive summarization is a task that generates short and concise summaries of user generated reviews. |
| Approach: | They propose an interactive attention mechanism to learn the representations of context and aspect words within reviews, acted as an encoder. |
| Outcome: | The proposed model achieves impressive results compared to other strong competitors on a real-life dataset. |
Auto-Instruct: Automatic Instruction Generation and Ranking for Black-Box Language Models (2023.findings-emnlp)
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Zhihan Zhang, Shuohang Wang, Wenhao Yu, Yichong Xu, Dan Iter, Qingkai Zeng, Yang Liu, Chenguang Zhu, Meng Jiang
| Challenge: | Large language models can perform a wide range of tasks by following natural language instructions without task-specific fine-tuning. |
| Approach: | They propose a method to automatically improve the quality of LLM instructions . they leverage the generative ability of LMS to generate diverse candidate instructions based on a scoring model trained on 575 existing NLP tasks. |
| Outcome: | The proposed method surpasses human-written and LLM-generated instructions on 118 out-of-domain tasks. |
Evaluating Moral Beliefs across LLMs through a Pluralistic Framework (2024.findings-emnlp)
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| Challenge: | Proper moral beliefs are fundamental for language models, yet assessing these beliefs poses a significant challenge. |
| Approach: | They propose a framework to evaluate the moral beliefs of four large language models . they use a dataset containing 472 moral choice scenarios in Chinese . |
| Outcome: | The proposed framework evaluates the moral beliefs of four large language models. |
Learning to Refuse: Towards Mitigating Privacy Risks in LLMs (2025.coling-main)
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| Challenge: | Large language models (LLMs) exhibit remarkable capabilities in understanding and generating natural languages, but can inadvertently memorize private information, posing significant privacy risks. |
| Approach: | They propose to use a dataset to evaluate machine unlearning methods for protecting personal data in a realistic scenario. |
| Outcome: | The proposed model outperforms baseline methods by 5.65 points and protects target individuals’ personal data while maintaining general capabilities. |
Advancing Topic Segmentation and Outline Generation in Chinese Texts: The Paragraph-level Topic Representation, Corpus, and Benchmark (2024.lrec-main)
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| Challenge: | Compared with sentence-level topic structure, paragraph-level topics can grasp and understand the context of a document from a higher level. |
| Approach: | They propose a hierarchical paragraph-level topic structure representation with three layers to guide corpus construction. |
| Outcome: | The proposed method achieves the largest Chinese paragraph-level topic structure corpus, achieving high quality. |
Understanding and Improving Hidden Representations for Neural Machine Translation (N19-1)
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| Challenge: | Existing studies have explored some methods for understanding hidden representations, but they have not sought to improve the translation quality rationally according to their understanding. |
| Approach: | They propose to construct a sequence of nested relative tasks and measure the feature generalization ability of the learned hidden representation over these tasks. |
| Outcome: | The proposed methods achieve consistent improvements (up to +1.3 BLEU) on two widely-used datasets. |
Length Controlled Generation for Black-box LLMs (2025.acl-long)
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Yuxuan Gu, Wenjie Wang, Xiaocheng Feng, Weihong Zhong, Kun Zhu, Lei Huang, Ting Liu, Bing Qin, Tat-Seng Chua
| Challenge: | Existing length control methods involve fine-tuning the parameters of LLMs, which is inefficient and suboptimal for practical use. |
| Approach: | They propose an iterative sampling framework that regulates LLMs to generate length-constrained text without modifying the underlying parameters. |
| Outcome: | The proposed method achieves 100% success rates on Llama3.1 tasks with minimal additional computational overhead. |
Learning Architectures from an Extended Search Space for Language Modeling (2020.acl-main)
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Yinqiao Li, Chi Hu, Yuhao Zhang, Nuo Xu, Yufan Jiang, Tong Xiao, Jingbo Zhu, Tongran Liu, Changliang Li
| Challenge: | Neural architecture search (NAS) has advanced in recent years but most NAS systems restrict search to learning architectures of a recurrent or convolutional cell. |
| Approach: | They propose a general approach to learn both intra-cell and inter-cell architectures . they implement their approach in a differentiable architecture search system . |
| Outcome: | The proposed approach outperforms the baseline on PTB and WikiText data and shows good transferability to other systems. |
WebCPM: Interactive Web Search for Chinese Long-form Question Answering (2023.acl-long)
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Yujia Qin, Zihan Cai, Dian Jin, Lan Yan, Shihao Liang, Kunlun Zhu, Yankai Lin, Xu Han, Ning Ding, Huadong Wang, Ruobing Xie, Fanchao Qi, Zhiyuan Liu, Maosong Sun, Jie Zhou
| Challenge: | Long-form question answering requires two procedures: information retrieval and information synthesis. |
| Approach: | They propose a Chinese long-form question answering dataset called WebCPM . the dataset is based on a web search interface that engages with a search engine in real time . |
| Outcome: | The proposed dataset generates answers that are no worse than human-written ones . the dataset is the first Chinese LFQA dataset . |
Fine-grained Artificial Neurons in Audio-transformers for Disentangling Neural Auditory Encoding (2023.findings-acl)
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Mengyue Zhou, Xu Liu, David Liu, Zihao Wu, Zhengliang Liu, Lin Zhao, Dajiang Zhu, Lei Guo, Junwei Han, Tianming Liu, Xintao Hu
| Challenge: | Existing studies treat each transformer encoding layer as a single artificial neuron . layer-level embeddings aggregate multiple types of contextual attention captured by multiple head modules . |
| Approach: | They propose to embed each transformer encoding layer as a single artificial neuron . they propose to couple those ANs with their biological-neuron counterparts in the human brain . |
| Outcome: | The proposed models can be used to link representations to brain activity, the authors say . their results show that the proposed models carry meaningful neurolinguistic information . |
Neural Topic Modeling via Contextual and Graph Information Fusion (2025.emnlp-main)
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| Challenge: | Existing topic models generate uninformative and incoherent topics that hinder interpretable insights from managing textual data. |
| Approach: | They propose to incorporate contextual and graph information to improve the variational autoencoder framework by combining contextual and bag-of-words information. |
| Outcome: | The proposed framework generates more coherent and diverse topics on three benchmark datasets and achieves strong performance on automatic and manual evaluations. |
TVQACML: Benchmarking Text-Centric Visual Question Answering in Multilingual Chinese Minority Languages (2025.emnlp-main)
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| Challenge: | Existing TEC-VQA benchmarks focus on high-resource languages like English and Chinese . existing benchmarks have a "visual-textual misalignment" problem resulting in unreliable evaluation results . |
| Approach: | They propose a benchmark that expands multilingual QA pairs in non-text-centric datasets through translation to eight languages, including Standard Chinese, Korean, and six minority languages. |
| Outcome: | The proposed benchmarks are contamination-free and more challenging . they include eight languages including Chinese, Korean, and six minority languages . |
TARN-VIST: Topic Aware Reinforcement Network for Visual Storytelling (2024.lrec-main)
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| Challenge: | Existing methods for visual storytelling ignore latent topic information. |
| Approach: | They propose a topic-aware reinforcement network for VIsual StoryTelling that takes topic information into account to generate a coherent story. |
| Outcome: | The proposed method outperforms most of the competing models across multiple evaluation metrics. |
MoPE: Mixture of Prefix Experts for Zero-Shot Dialogue State Tracking (2024.lrec-main)
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| Challenge: | Existing zero-shot dialogue state tracking models suffer from domain transferring and partial prediction problems. |
| Approach: | They propose to establish connections between similar slots in different domains to improve model transfer performance in unseen domains. |
| Outcome: | Empirical results show that the proposed model achieves the goal accuracy of 57.13% on MultiWOZ2.1 and 55.4. |
LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference (2024.findings-emnlp)
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| Challenge: | Long-context Multimodal Large Language Models (MLLMs) require substantial computational resources for inference . the growth of their multimodal Key-Value (KV) cache challenges memory and time efficiency. |
| Approach: | They propose a fine-tuning-free approach that efficiently reduces the multimodal KV cache size while maintaining performance comparable to a full cache. |
| Outcome: | The proposed method reduces the multimodal KV cache size while maintaining performance comparable to a full cache. |
GenWebNovel: A Genre-oriented Corpus of Entities in Chinese Web Novels (2025.coling-main)
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| Challenge: | Existing literature on nested entity recognition is insufficient partly due to insufficient annotated data. |
| Approach: | They propose a method that utilizes a pre-trained language model as an In-context learning example retriever to boost the performance of large language models. |
| Outcome: | The proposed method significantly enhances entity recognition, matching state-of-the-art (SOTA) models without additional training data. |
Knowledge Graph-Enhanced Large Language Models via Path Selection (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) have shown unprecedented performance in various real-world applications, but they are known to generate factually inaccurate outputs. |
| Approach: | They propose a framework to integrate external knowledge extracted from Knowledge Graphs (KGs) they propose to generate scores for knowledge paths with input texts via latent semantic matching. |
| Outcome: | Experiments on real-world datasets validate the effectiveness of a framework to extract knowledge from Knowledge Graphs (KGs) incorporating external knowledge has become a promising strategy to improve the factual accuracy of LLM-generated outputs. |
LENS: Learning Entities from Narratives of Skin Cancer (2025.coling-demos)
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| Challenge: | Learning entities from narratives of skin cancer (LENS) is an automatic entity recognition system built on colloquial writings from skin cancer-related forums. |
| Approach: | They propose to use reddit forums to create an automatic entity recognition system that can be used to predict skin cancer outcomes. |
| Outcome: | LENS achieves an overall entity-level F1 score of 0.561 . other notable results include “CANC_T” (0.747), “STG” (0.888), “POB” (0.914), “GENDER” (0.750), “A/G” (00.646), “EMO” (0.619), and “MHD” (0.503). |
CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling (2026.acl-long)
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Runsong Zhao, Shilei Liu, Jiwei Tang, Langming Liu, Haibin Chen, Weidong Zhang, Yujin Yuan, Tong Xiao, JingBo Zhu, Wenbo Su, Bo Zheng
| Challenge: | a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity is a major barrier to long-context processing. |
| Approach: | They propose a novel architecture that enables LLMs to handle arbitrarily long sequences with constant memory usage and linear time complexity. |
| Outcome: | The proposed architecture can handle arbitrarily long sequences with constant memory usage and linear time complexity. |
HyperMR: Hyperbolic Hypergraph Multi-hop Reasoning for Knowledge-based Visual Question Answering (2024.lrec-main)
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| Challenge: | Existing studies on knowledge-based visual question answering (KBVQA) describe the semantic distance using the actual Euclidean distance between two nodes, which leads to distortion in modeling knowledge graphs with hierarchical and scale-free structure. |
| Approach: | They propose to use the actual Euclidean distance between two nodes to solve a problem of hierarchical and free-scale knowledge graphs. |
| Outcome: | Extensive experiments on the KVQA, PQ and PQL datasets demonstrate the effectiveness of HyperMR for strong-hierarchy knowledge graphs. |
Document-level Relationship Extraction by Bidirectional Constraints of Beta Rules (2023.emnlp-main)
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| Challenge: | Document-level Relation Extraction (DocRE) aims to extract relations among entity pairs in documents. |
| Approach: | They propose a logic constraint framework that uses bidirectional constraints to model rules by beta contribtion and reconstruct rule consistency loss by bidirectional constraint. |
| Outcome: | The proposed framework outperforms existing models in relation extraction performance and logical consistency. |
VIEWS: Entity-Aware News Video Captioning (2024.emnlp-main)
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Hammad Ayyubi, Tianqi Liu, Arsha Nagrani, Xudong Lin, Mingda Zhang, Anurag Arnab, Feng Han, Yukun Zhu, Xuande Feng, Kevin Zhang, Jialu Liu, Shih-Fu Chang
| Challenge: | Existing video captioning benchmarks and models produce generic captions for videos that lack specific identification of individuals, locations, or organizations. |
| Approach: | They propose a task of directly summarizing news videos into captions that are entity-aware . they validate the effectiveness of their approach across three video captioning models . |
| Outcome: | The proposed approach is effective across three video captioning models. |
M3TQA: Massively Multilingual Multitask Table Question Answering (2026.findings-acl)
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Daixin Shu, Jian Yang, Zhenhe Wu, Xianjie Wu, Xianfu Cheng, Guan Xiangyuan, Yanghai Wang, Pengfei Wu, Tingyang Yang, Hualei Zhu, Wei Zhang, Ge Zhang, Jiaheng Liu, Zhoujun Li
| Challenge: | Existing multilingual table benchmarks suffer from geolinguistic imbalance - overrepresenting certain languages and lacking sufficient scale for rigorous cross-lingual analysis. |
| Approach: | They propose a framework for massively multilingual table question answering that includes tables expanded to 97 languages from Chinese and English sources. |
| Outcome: | Experiments on state-of-the-art LLMs show that synthetically generated training data significantly boosts performance, especially for low-resource languages. |
MMAPG: A Training-Free Framework for Multimodal Multi-hop Question Answering via Adaptive Planning Graphs (2025.emnlp-main)
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| Challenge: | Existing multimodal question answering models rely on sequential retrieval and reasoning, but this single-path paradigm makes them vulnerable to errors due to misleading intermediate steps. |
| Approach: | They propose a multimodal multi-hop question answering framework guided by an Adaptive Planning Graph . they propose modality-specific strategies that dynamically adapt to distinct data types . |
| Outcome: | The proposed framework outperforms existing models that rely on training. |
Mitigating Lost-in-Retrieval Problems in Retrieval Augmented Multi-Hop Question Answering (2025.acl-long)
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| Challenge: | Empirical results show that ChainRAG consistently outperforms baselines in both effectiveness and efficiency. |
| Approach: | They propose a method which sequentially handles each sub-question by completing missing key entities and retrieving relevant sentences from a sentence graph for answer generation. |
| Outcome: | The proposed method outperforms baselines on three multi-hop QA datasets. |
Zero-shot Faithfulness Evaluation for Text Summarization with Foundation Language Model (2023.emnlp-main)
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| Challenge: | Existing work evaluates faithfulness using models trained on related tasks or in-domain synthetic data. |
| Approach: | They propose to do zero-shot faithfulness evaluation with a foundation language model. |
| Outcome: | The proposed model outperforms ChatGPT on faithfulness and inconsistency detection with 24x fewer parameters and is competitive with existing models. |
RSDA: Restoring Stale Data Affinity via Dynamic Renovation Strategy for Mitigating Data Scarcity (2026.acl-long)
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Yidan Liang, Jia Zhu, Weijie Shi, Hanghui Guo, Yue Cui, Jiawei Shen, Guoqing Ma, Jingjiang Liu, Qingyu Niu, Yilin Wang, Shimin Di, Jiajie Xu
| Challenge: | High-quality data is the cornerstone of advancing large language models, but the supply of premium data is nearing depletion, while vast stale corpora remain underutilized. |
| Approach: | They propose a framework to restore stale data affinity by quantifying the latent value of samples and employing a dynamic renovation strategy selection mechanism to determine the optimal component-level strategy. |
| Outcome: | The proposed framework achieves performance improvements using less than 10% of the data volume, underscoring that the latent potential of stale corpora remains largely untapped. |
The Sonar Moment: An Audio Geo-Localization Benchmark for Audio-Language Models (2026.findings-acl)
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| Challenge: | AGL1K is the first audio geo-localization benchmark for audio language models (ALMs) it is based on a crowd-sourced platform and is available in 72 countries and territories. |
| Approach: | They propose a benchmark for audio geo-localization that quantifies the informativeness of each recording and a metric that quantizes the information of each audio clip. |
| Outcome: | The proposed benchmarks cover 72 countries and territories and can be used to improve audio geo-localization. |
ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model (2025.emnlp-main)
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Zhongyi Zhou, Yichen Zhu, Minjie Zhu, Junjie Wen, Ning Liu, Zhiyuan Xu, Weibin Meng, Yaxin Peng, Chaomin Shen, Feifei Feng, Yi Xu
| Challenge: | Recent advances in vision-language-action models prioritize robotic action mastery . however, models trained on visual-text pairs struggle to interpret multimodal data . |
| Approach: | They propose a framework that integrates multimodal data after initial control mastery and a Mixture-of-Experts architecture to minimize task interference. |
| Outcome: | The proposed framework surpasses state-of-the-art vision-language-action (VLA) methods on multimodal understanding benchmarks and achieves six times higher performance on visual question-answering datasets. |
An Efficient Conversational Smart Compose System (2023.acl-demo)
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Yun Zhu, Xiayu Chen, Lei Shu, Bowen Tan, Xinying Song, Lijuan Liu, Maria Wang, Jindong Chen, Ning Ruan
| Challenge: | a cloud-based smart compose system is designed to improve human-to-human conversation efficiency. |
| Approach: | They propose a cloud-based smart compose system to improve conversation efficiency . they propose heuristics to achieve the best trade-off between quality and latency . |
| Outcome: | The proposed system reduces latency without losing composing quality further. |
Beat LLMs at Their Own Game: Zero-Shot LLM-Generated Text Detection via Querying ChatGPT (2023.emnlp-main)
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Biru Zhu, Lifan Yuan, Ganqu Cui, Yangyi Chen, Chong Fu, Bingxiang He, Yangdong Deng, Zhiyuan Liu, Maosong Sun, Ming Gu
| Challenge: | Large language models (LLMs) are capable of performing tasks but are likely to be misused. |
| Approach: | They propose a zero-shot black-box method to detect LLM-generated texts . they revise the text to be detected using the ChatGPT model . |
| Outcome: | The proposed method can detect LLM-generated texts with a zero-shot black-box model . it is based on intuition that the model will make fewer revisions to LLMs than to human-written texts . |
Counterfactual Off-Policy Training for Neural Dialogue Generation (2020.emnlp-main)
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| Challenge: | Existing models for open-domain dialogue generation suffer from data insufficiency . a potential response inferred in hindsight is called a counterfactual reasoning . |
| Approach: | They propose to explore potential responses by counterfactual reasoning . given an observed response, the model automatically infers the outcome of an alternative policy that could have been taken . |
| Outcome: | The proposed model outperforms the HRED model and conventional learning frameworks on the DailyDialog dataset. |
STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion (2023.emnlp-main)
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Xurui Li, Yue Qin, Rui Zhu, Tianqianjin Lin, Yongming Fan, Yangyang Kang, Kaisong Song, Fubang Zhao, Changlong Sun, Haixu Tang, Xiaozhong Liu
| Challenge: | Commercial news provides rich semantics and timely information for automated financial risk detection. |
| Approach: | They propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph to endorse the risk detection enhancement. |
| Outcome: | The proposed model outperforms existing models in terms of generalization and semantics and annotation. |
SPS: Steering Probability Squeezing for Better Exploration in Reinforcement Learning for Large Language Models (2026.findings-acl)
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Yifu Huo, Chenglong Wang, Ziming Zhu, Shunjie Xing, Peinan Feng, Tongran Liu, Qiaozhi He, Tian Hua Zhou, null Changxiaojia, JingBo Zhu, Zhengtao Yu, Tong Xiao
| Challenge: | Reinforcement learning (RL) training typically improves single-sample success rates but limited exploration of diverse reasoning trajectories. |
| Approach: | They propose a training paradigm that interleaves conventional RL with inverse reinforcement learning (IRL) they propose 'Steering Probability Squeezing' to enhance exploration without external supervision . |
| Outcome: | The proposed training paradigm improves Pass@k and improves exploration of diverse reasoning trajectories without external supervision. |
Knowledge Representation Learning with Contrastive Completion Coding (2021.findings-emnlp)
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| Challenge: | Existing knowledge representation learning methods suffer from immaturity on tackling potentially-imperfect knowledge graphs and highly-imbalanced positive-negative instances during training. |
| Approach: | They propose a framework for knowledge representation learning that incorporates two functional components to achieve robust embedding for each entity/relation. |
| Outcome: | The proposed framework achieves better convergence against state-of-the-art methods on several benchmarks. |
ICG: Improving Cover Image Generation via MLLM-based Prompting and Personalized Preference Alignment (2025.emnlp-main)
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Zhipeng Bian, Jieming Zhu, Qijiong Liu, Wang Lin, Guohao Cai, Zhaocheng Du, Jiacheng Sun, Zhou Zhao, Zhenhua Dong
| Challenge: | Large language models and diffusion models have opened new possibilities for AI-generated content . personalized cover image generation remains underexplored despite its critical role in boosting user engagement on digital platforms. |
| Approach: | They propose a framework that integrates MLLM-based prompting with personalized preference alignment to generate high-quality, contextually relevant covers. |
| Outcome: | The proposed framework improves image quality, semantic fidelity, and personalization, leading to stronger user appeal and offline recommendation accuracy in downstream tasks. |
Quantifying Similarity between Relations with Fact Distribution (P19-1)
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| Challenge: | a conceptually simple and effective method to quantify the similarity between relations is presented . identifying relations is a crucial problem for several information extraction tasks. |
| Approach: | They propose a method to quantify the similarity between relations in knowledge bases . they use a neural network to parameterize conditional probability distributions over entity pairs . |
| Outcome: | The proposed method significantly correlates with human judgments, the authors show . it could be incorporated into negative sampling and softmax classification to alleviate these mistakes. |
Chinese Morph Resolution in E-commerce Live Streaming Scenarios (2025.naacl-industry)
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| Challenge: | Live morph resolution task is used to detect e-commerce live streaming violations . morphs are used to evade scrutiny and engage in false advertising . |
| Approach: | They propose a task to detect morph violations in live streaming scenarios . they use large language models to generate additional training data . |
| Outcome: | The proposed method improves performance and improves live streaming regulation. |
FocalOrder: Focal Preference Optimization for Reading Order Detection (2026.acl-long)
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Fuyuan Liu, Dianyu Yu, He Ren, Nayu Liu, Xiaomian Kang, Delai Qiu, Fa Zhang, Genpeng Zhen, Shengping Liu, Liang Jiaen, null Weihuang, Yining Wang, Junnan Zhu
| Challenge: | Existing methods for document comprehension rely on uniform supervision, resulting in a performance degradation in the intermediate sections. |
| Approach: | They propose a framework driven by Focal Preference Optimization to detect reading order in document layouts. |
| Outcome: | The proposed framework outperforms competing baselines and surpasses large-scale general VLMs. |
Detecting AI-Generated Content on Social Media with Multi-modal Language Models (2026.acl-industry)
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Chenyang Yang, Shen Yan, Yibo Yang, Litao Hu, Yuchen Liu, Yuan Zeng, Hanchao Yu, Yinan Zhu, Sumedha Singla, Brian Vanover, Huijun Qian, Zihao Wang, Fujun Liu, Aashu Singh, Jianyu Wang, Xuewen Zhang
| Challenge: | Existing methods for AI-generated content detection face poor generalization to newer models, reliance on single modalities, and lack of interpretable explanations. |
| Approach: | They propose a model that curates diverse social media data and trains a vision-language model for detection and explanation. |
| Outcome: | The proposed model achieves state-of-the-art detection performance on public benchmarks and observes positive downstream impacts on user engagement. |
LLaSE-G1: Incentivizing Generalization Capability for LLaMA-based Speech Enhancement (2025.acl-long)
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Boyi Kang, Xinfa Zhu, Zihan Zhang, Zhen Ye, Mingshuai Liu, Ziqian Wang, Yike Zhu, Guobin Ma, Jun Chen, Longshuai Xiao, Chao Weng, Wei Xue, Lei Xie
| Challenge: | Recent advances in language models have demonstrated strong capabilities in semantic understanding and contextual modeling. |
| Approach: | They propose a LLaMA-based language model that incentivizes generalization capabilities for speech enhancement. |
| Outcome: | The proposed language model outperforms prior task-specific discriminative and generative models in acoustic enhancement tasks. |
Neural Stylistic Response Generation with Disentangled Latent Variables (2021.acl-long)
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| Challenge: | Existing parallel datasets for creating stylistic responses are not stylistically consistent. |
| Approach: | They propose to disentangle the content and style in latent space by diluting sentence-level information in style representations. |
| Outcome: | The proposed approach achieves a higher BERT-based style intensity score and comparable BLEU scores, compared with baselines. |
Less Is More: Domain Adaptation with Lottery Ticket for Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Existing domain adaptation paradigms for reading comprehension require large amounts of annotation data to achieve the desired task performance. |
| Approach: | They propose a few-shot domain adaptation paradigm for reading comprehension . they introduce self-attention attribution to weigh parameters and refine the lottery subnetwork . |
| Outcome: | The proposed model outperforms the full model fine-tuning adaptation on four out of five domains with a small amount of data available for adaptation. |
KCVR: Knowledge-Centric Video Reconstruction for Structured Pedagogical Summarization via Dynamic Graph Planning (2026.acl-long)
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Jingjiang Liu, Jia Zhu, Hanghui Guo, Weijie Shi, Yue Cui, Xiaokang Jin, Yilin Wang, Qingyu Niu, Jiawei Shen, Guoqing Ma, Yidan Liang, Shimin Di, Jiajie Xu
| Challenge: | Existing summarization methods compress content for gist browsing, but they break prerequisite logic in instructional videos. |
| Approach: | They propose a framework that decouples epistemic planning from content generation. |
| Outcome: | The proposed framework outperforms strong end-to-end baselines on Knowledge Progression Consistency and Learning Objective Coverage. |
Generating Attribution Reports for Manipulated Facial Images: A Dataset and Baseline (2026.acl-long)
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| Challenge: | Existing facial forgery detection methods focus on binary classification or pixel-level localization, providing little semantic insight into the nature of the manipulation. |
| Approach: | They propose a multimodal task that localizes forged regions and generates natural language explanations grounded in editing process. |
| Outcome: | The proposed task localizes forged regions and generates natural language explanations grounded in editing process. |
QaRL: Rollout-Aligned Quantization-Aware RL for Fast and Stable Training under Training–Inference Mismatch (2026.findings-acl)
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Hao Gu, Hao Wang, Jiacheng Liu, Lujun Li, Qiyuan Zhu, Bei Liu, Binxing Xu, Lei Wang, Xintong Yang, Sida Lin, Sirui Han, Yike Guo
| Challenge: | Recent work has shown that reinforcement learning with simple rule-based reward functions (RLVR) can induce emergent reasoning behaviors and yield gains in challenging domains such as math problem solving. |
| Approach: | They propose a rollout-alignment-quantization-aware RL which aligns training-side forward with the quantized rollout to minimize mismatch. |
| Outcome: | The proposed approach outperforms quantized-rollout training by +5.5 on Qwen3-30B-A3B MoE for math problems while maintaining low-bit throughput. |
TheraAgent: Self-Improving Therapeutic Agent for Precise and Comprehensive Treatment Planning (2026.findings-acl)
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| Challenge: | Existing large language models rely on one-shot output without explicit verification, resulting in rough, incomplete, and potentially unsafe treatment plans. |
| Approach: | They propose an agentic framework that replaces one-shot generation with an iterative generate-judge-refine pipeline. |
| Outcome: | The proposed framework achieves state-of-the-art results on HealthBench, leading in Accuracy and Completeness. |
UniRAG: Unified Query Understanding Method for Retrieval Augmented Generation (2025.acl-long)
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| Challenge: | Existing query augmentation methods face knowledge update lag and hallucinations in large language models (LLMs) Existing methods face two key challenges: (1) separation of query augmented and encoding tasks, which hinders information sharing and introduces cumulative errors; (2) difficulty of selecting optimal augmentation strategy for different scenarios. |
| Approach: | They propose a unified framework for query understanding in RAG that integrates internal and external knowledge to enhance query augmentation and encoding tasks. |
| Outcome: | The proposed framework outperforms traditional query augmentation methods in five knowledge-intensive benchmark tasks in both closed and open domain question answering. |
Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning (2025.emnlp-main)
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| Challenge: | a shortage of medical doctors limits access to timely and reliable healthcare . authors propose a multi-turn LLM-based medical assistant for medical inquiries . |
| Approach: | They propose a multi-turn LLM-based medical assistant that asks patients with patience . they compare it with SOTA one-shot and multi-turned LLMs to evaluate its performance . |
| Outcome: | The proposed medical assistant improves diagnostic accuracy, reduces uncertainty and enhances user experience. |
Advancing Parameter Efficiency in Fine-tuning via Representation Editing (2024.acl-long)
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Muling Wu, Wenhao Liu, Xiaohua Wang, Tianlong Li, Changze Lv, Zixuan Ling, Zhu JianHao, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Parameter Efficient Fine-Tuning (PEFT) has gained significant attention for its ability to achieve competitive results while updating only a small subset of trainable parameters. |
| Approach: | They propose a new approach to fine-tuning neural models that scales and biases the representation produced at each layer. |
| Outcome: | The proposed approach reduces the number of trainable parameters by a factor of 25,700 compared to full parameter fine-tuning and by . 32 compared with LoRA. |
DREAM: Disentangling Risks to Enhance Safety Alignment in Multimodal Large Language Models (2025.naacl-long)
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Jianyu Liu, Hangyu Guo, Ranjie Duan, Xingyuan Bu, Yancheng He, Shilong Li, Hui Huang, Jiaheng Liu, Yucheng Wang, Chenchen Jing, Xingwei Qu, Xiao Zhang, Pei Wang, Yanan Wu, Jihao Gu, Yangguang Li, Jianke Zhu
| Challenge: | Multimodal Large Language Models (MLLMs) pose unique safety challenges due to their integration of visual and textual data. |
| Approach: | They propose a method to disentangle risks through step-by-step reasoning within multimodal inputs. |
| Outcome: | The proposed approach improves safety alignment in MLLMs by fine-tuning and iterative Reinforcement Learning from AI feedback. |
SGDPO: Self-Guided Direct Preference Optimization for Language Model Alignment (2025.findings-acl)
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| Challenge: | Existing methods for aligning Large Language Models with human values are limited and results of DPO are not resilient. |
| Approach: | They propose a self-guided direct preference optimization algorithm that incorporates a pilot term to steer the gradient flow during the optimization process. |
| Outcome: | The proposed method can generate human-preferred response up to 9.19% higher than previous methods. |
Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding (2022.findings-emnlp)
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Jiahao Zhu, Daizong Liu, Pan Zhou, Xing Di, Yu Cheng, Song Yang, Wenzheng Xu, Zichuan Xu, Yao Wan, Lichao Sun, Zeyu Xiong
| Challenge: | Existing methods for temporal sentence grounding ignore two crucial issues . 1) Boundary-bias: the video downsampling process may lose these two frames . 2) Reasoning-biases: such incorrect new boundary frames lead to the reasoning bias . |
| Approach: | They propose a siamese sampling mechanism to generate additional contextual frames . they use a reasoning strategy to learn the inter-relationship among these frames a . |
| Outcome: | Extensive experiments demonstrate the effectiveness of a new siamese sampling network on three challenging datasets. |
Uncertainty-Guided Modal Rebalance for Hateful Memes Detection (2024.acl-long)
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| Challenge: | Existing methods for integrating hate information from different modalities ignore the modality uncertainty caused by the contribution degree of each modality to hate sentiment. |
| Approach: | They propose an Uncertainty-guided Modal Rebalance framework for hateful memes detection . they propose to combine cross-modal fusion features with unimodal features . |
| Outcome: | The proposed framework produces state-of-the-art performance on four widely-used datasets. |
EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs (2023.emnlp-main)
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| Challenge: | Recent work has shown that large language models are superior to conventional methods in various tasks. |
| Approach: | They propose a data-independent quantization algorithm that leaves outliers in the weight and quantization ranges . they find the algorithm runs over 10 times faster than the data-dependent methods . |
| Outcome: | The proposed method runs over 10 times faster than the data-dependent methods. |
Naive Bayes-based Context Extension for Large Language Models (2024.naacl-long)
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| Challenge: | Large Language Models (LLMs) have shown promising in-context learning abilities, but are often impeded by length limitations of transformer architecture. |
| Approach: | They propose a framework to enable existing Large Language Models to perform ICL with an increased number of demonstration examples by significantly expanding their context size. |
| Outcome: | The proposed framework outperforms existing methods in in-context learning with an increased number of demonstration examples while preserving linear efficiency. |
DASA-Trans-STM: Adaptive Efficient Transformer for Short Text Matching using Data Augmentation and Semantic Awareness (2025.emnlp-main)
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| Challenge: | Recent advances in large language models have shown impressive versatility across various tasks. |
| Approach: | They propose a novel adaptive Transformer for Chinese short text matching using data augmentation and semantic awareness. |
| Outcome: | The proposed model can deal with word ambiguity in Chinese on four available datasets. |
PARA: Parameter-Efficient Fine-tuning with Prompt-Aware Representation Adjustment (2024.emnlp-industry)
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| Challenge: | Existing methods for parameter-efficient fine-tuning excel in the context of single-backbone multi-tenant applications. |
| Approach: | They propose to integrate a lightweight vector generator within each Transformer layer to improve prompt-aware representation adjustment. |
| Outcome: | The proposed method surpasses current benchmarks in terms of performance despite having a similar number of adjustable parameters. |
MPBoCo: Multimodal Prompt-based Boundary-enhanced Continual Framework for Joint Entity and Relation Extraction (2026.acl-long)
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| Challenge: | Existing methods struggle to balance real-time adaptability and computational efficiency in continual learning scenarios. |
| Approach: | They propose a Continual Multimodal Entity and Relation Joint Extraction task and a Multimodal Prompt-based Boundary-enhanced Continuum framework that stores task-specific knowledge via learnable multimodal prompts. |
| Outcome: | The proposed framework outperforms baseline methods in real-world scenarios by 5.5% and 7.2%. |
CARFT: Boosting LLM Reasoning via Contrastive Learning with Annotated Chain-of-Thought-based Reinforced Fine-Tuning (2025.emnlp-main)
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| Challenge: | Existing methods to improve the reasoning performance of Large Language Models (LLMs) ignore annotated Chain-of-Thought (CoT) and incorporate unstable reasoning path sampling. |
| Approach: | They propose a Contrastive learning with annotated CoT-based Reinforced Fine-Tuning approach to enhance the reasoning performance of Large Language Models. |
| Outcome: | The proposed approach exploits annotated CoT and stabilizes the fine-tuning procedure by incorporating an additional unsupervised learning signal. |
Attribute-Controlled Translation with Preference Optimization (2026.findings-eacl)
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| Challenge: | Attribute-controlled translation (ACT) is a natural language processing task that produces translations that satisfy specific constraints on linguistic and stylistic attributes. |
| Approach: | They propose to leverage the contrastive nature of ACT tasks with preference optimization . they also propose to exploit knowledge distillation with synthetically-generated training samples . |
| Outcome: | The proposed approach improves attribute matching and translation quality in small-medium size models. |
Noise Learning for Text Classification: A Benchmark (2022.coling-1)
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| Challenge: | Existing noise learning methods for text classification are underdeveloped . authors propose a noise learning benchmark for text classification . |
| Approach: | They propose to use four state-of-the-art methods of noise learning from the image domain to classify text. |
| Outcome: | The proposed benchmark of noise learning for text classification is based on four methods and five noise modes. |
Shallow-to-Deep Training for Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Experimental results show that deep training is 1:4 faster than training from scratch. |
| Approach: | They propose a shallow-to-deep training method that learns deep models by stacking shallow models. |
| Outcome: | The proposed method is 1:4 faster than training from scratch and achieves BLEU scores of 30:33 and 43:29 on two translation tasks. |
COSMOS: Connectivity-Oriented Submodular Maximization for Optimal Subgraph Retrieval (2026.acl-long)
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| Challenge: | Existing paradigms treat facts independently or employ myopic search, failing to optimize collective subgraph utility. |
| Approach: | They propose a framework that formalizes evidence retrieval as a constrained submodular maximization problem. |
| Outcome: | The proposed framework captures the trade-off between information relevance and structural complexity. |
OmniDiagram: Advancing Unified Diagram Code Generation via Visual Interrogation Reward (2026.findings-acl)
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| Challenge: | Existing studies on programmable diagram generation focus on a narrow set of tasks and languages. |
| Approach: | They propose a unified framework that integrates diverse diagram code languages and task definitions. |
| Outcome: | The proposed framework can bridge complex visual information with executable code across diverse tasks and languages. |
DialogueMMT: Dialogue Scenes Understanding Enhanced Multi-modal Multi-task Tuning for Emotion Recognition in Conversations (2025.coling-main)
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| Challenge: | Existing ERC methods fail to handle emotional cues from both visual sources and discourse structures due to the complexity of visual scenes and contextual dependencies in conversations. |
| Approach: | They propose a framework for Emotion Recognition in conversations that utilizes multi-task instruction tuning to enhance the model's understanding of multi-modal dialogue scenes. |
| Outcome: | The proposed framework outperforms existing state-of-the-art models on three benchmark ERC datasets and is based on a video-language connector and a chain-of thought strategy. |
AdaPrompt: Adaptive Model Training for Prompt-based NLP (2022.findings-emnlp)
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| Challenge: | Prompt-based learning can tackle zero-shot and few-shot NLP tasks . authors propose a method that makes use of pre-trained language models . |
| Approach: | They propose to map NLP tasks into natural language prompts, which are then filled by pre-trained language models. |
| Outcome: | The proposed method outperforms standard prompt-based methods in few-shot settings. |
Path Spuriousness-aware Reinforcement Learning for Multi-Hop Knowledge Graph Reasoning (2023.eacl-main)
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| Challenge: | Multi-hop reasoning is a common approach for query answering, but can be biased to spurious paths which coincidentally lead to the correct answer with poor explanation. |
| Approach: | They propose a method that quantitatively estimates to what extent a path is spurious by a metric called Path Spuriousness (PS) they propose KG reasoning, which infers new facts along existing paths in KGs. |
| Outcome: | The proposed model significantly improves the agent’s ability to prevent spurious paths while keeping comparable to state-of-the-art performance. |
Enhancing Neural Machine Translation Through Target Language Data: A kNN-LM Approach for Domain Adaptation (2025.acl-long)
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Abudurexiti Reheman, Hongyu Liu, Junhao Ruan, Abudukeyumu Abudula, Yingfeng Luo, Tong Xiao, JingBo Zhu
| Challenge: | Neural machine translation (NMT) has made significant progress in recent years, yet often suffers from translating in new domains, which is called domain adaptation. |
| Approach: | They propose a method that leverages semantically similar target language sentences in the kNN framework and generates a probability distribution over these sentences during decoding. |
| Outcome: | The proposed method generates a probability distribution over similar target language sentences and then interpolates with the model’s distribution. |
Small Models are Valuable Plug-ins for Large Language Models (2024.findings-acl)
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| Challenge: | Large-scale pre-trained language models are difficult to fine-tune due to their huge weights and limited context length. |
| Approach: | They propose an approach which allows black-box LLMs to work with locally fine-tuned smaller models, resulting in superior performance on supervised tasks. |
| Outcome: | The proposed approach overcomes the challenges of poor performance and instability of In-Context Learning (ICL) while reducing the complexity of in-context learning. |
MR-ALIGN: Meta-Reasoning Informed Factuality Alignment for Large Reasoning Models (2026.findings-acl)
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Xinming Wang, Jian Xu, Bin Yu, Sheng Lian, yi Chen, Boran Wang, Yingjian Zhu, Hongzhu Yi, Hong-Ming Yang, Han Hu, Cheng-Lin Liu, Xu-Yao Zhang
| Challenge: | Large reasoning models (LRMs) show strong capabilities in complex reasoning, yet their marginal gains on evidence-dependent factual questions are limited. |
| Approach: | They propose a Meta-Reasoning informed alignment framework that quantifies state-transition probabilities along the model’s thinking process and constructs a transition-aware implicit reward that reinforces beneficial reasoning patterns while suppressing defective ones at the atomic thinking segments. |
| Outcome: | Empirical evaluations of four factual QA datasets and one long-form factuality benchmark show that MR-ALIGN consistently improves accuracy and truthfulness while reducing misleading reasoning. |
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. |
In-sample Curriculum Learning by Sequence Completion for Natural Language Generation (2023.acl-long)
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| Challenge: | Existing work on curriculum learning rely on task-specific expertise and cannot generalize to different tasks. |
| Approach: | They propose to do in-sample curriculum learning for natural language generation tasks using human-crafted rules and a numeric score for each sample based on domain expertise to rank the model. |
| Outcome: | The proposed learning strategy generalizes well to different tasks and achieves significant improvements over baselines. |
CrisPrune: Combining Contextual Relevance and Intrinsic Saliency for Efficient Visual Token Pruning in MLLMs (2026.findings-acl)
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| Challenge: | Existing methods for visual token pruning compromise the integrity of visual understanding in pursuit of efficiency. |
| Approach: | They propose a model-agnostic method that integrates visual saliency and text relevance to reconcile efficiency with understanding by integrating visual salions and text relevant. |
| Outcome: | The proposed method outperforms state-of-the-art methods on LLaVA-NeXT . it achieves 13 decrease in FLOPs while maintaining 97% of original performance . |
RecBase: Generative Foundation Model Pretraining for Zero-Shot Recommendation (2025.emnlp-main)
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Sashuai Zhou, Weinan Gan, Qijiong Liu, Ke Lei, Jieming Zhu, Hai Huang, Yan Xia, Ruiming Tang, Zhenhua Dong, Zhou Zhao
| Challenge: | Existing methods for addressing item-level user interests are lacking in cross-domain generalization . RecBase model is domain-agnostic and can be used to enhance recommender systems' effectiveness . |
| Approach: | They propose a domain-agnostic foundational model pretrained with a recommendation-oriented objective that leverages a large-scale, heterogeneous, cross-domain corpus with unified textual representations and feature mappings to enhance cross- domain generalization. |
| Outcome: | The proposed model matches or surpasses baselines in zero-shot and cross-domain recommendation tasks on eight real-world datasets. |
A Batch Normalized Inference Network Keeps the KL Vanishing Away (2020.acl-main)
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| Challenge: | Variational Autoencoder (VAE) is widely used to approximate a model’s posterior on latent variables. |
| Approach: | They propose to let the Kullback–Leibler divergence individual follow a distribution across the whole dataset and analyze that it is sufficient to prevent posterior collapse by keeping the expectation of the KL’s distribution positive. |
| Outcome: | The proposed approach can avoid posterior collapse effectively and efficiently without introducing any new model component or modifying the objective. |
Probing Social Identity Bias in Chinese LLMs with Gendered Pronouns and Social Groups (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly deployed in user-facing applications, raising concerns that they reflect and amplify social biases. |
| Approach: | They propose a Mandarin-specific evaluation framework to examine social identity biases in Chinese LLMs using Mandarin-based prompts. |
| Outcome: | The proposed framework compares ingroup (“We”) and outgroup (“They”) framings across 240 social groups salient in the Chinese context, using a two-tiered measurement framework that assesses both sentiment and toxicity. |
HeteroRAG: A Heterogeneous Retrieval-Augmented Generation Framework for Medical Vision Language Tasks (2026.findings-acl)
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| Challenge: | Medical large vision-language models suffer from factual inaccuracies and unreliable outputs. |
| Approach: | They propose a framework that enhances Med-LVLMs through heterogeneous knowledge sources. |
| Outcome: | The proposed framework improves Med-LVLMs through heterogeneous knowledge sources. |
AceGPT, Localizing Large Language Models in Arabic (2024.naacl-long)
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Huang Huang, Fei Yu, Jianqing Zhu, Xuening Sun, Hao Cheng, Song Dingjie, Zhihong Chen, Mosen Alharthi, Bang An, Juncai He, Ziche Liu, Junying Chen, Jianquan Li, Benyou Wang, Lian Zhang, Ruoyu Sun, Xiang Wan, Haizhou Li, Jinchao Xu
| Challenge: | Significant concerns emerge when addressing cultural sensitivity and local values. |
| Approach: | They propose a localized Large Language Model (LLM) specifically for Arabic, a language imbued with unique cultural characteristics inadequately addressed by current mainstream models. |
| Outcome: | The proposed model sets the state-of-the-art standard for open Arabic LLMs across various benchmarks. |
Deep Span Representations for Named Entity Recognition (2023.findings-acl)
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| Challenge: | Existing span-based NER systems shallowly aggregate token representations to span representations, causing performance degradations. |
| Approach: | They propose a method that shallowly aggregates token representations to span representations . DSpERT produces span representation of deep semantics with weight initialization . |
| Outcome: | The proposed model performs better than or competitive with state-of-the-art systems on six NER benchmarks. |
Theorem-Validated Reverse Chain-of-Thought Problem Generation for Geometric Reasoning (2025.emnlp-main)
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Deng Linger, Linghao Zhu, Yuliang Liu, Yu Wang, Qunyi Xie, Jingjing Wu, Gang Zhang, Yingying Zhu, Xiang Bai
| Challenge: | Existing methods for generating geometric reasoning data through Chain-of-Thought (CoT) frameworks face three fundamental limitations: 1) lack of high-quality annotations and domain-specific expertise to ensure theorem-grounded diagrams. 2) lack of a coherent model; 3) lack of coherent model. |
| Approach: | They propose a two-stage Theorem-Validated Reverse Chain-of-Thought Reasoning Synthesis framework that synthesizes theorematic diagrams with structured descriptions and properties. |
| Outcome: | The proposed framework expands theorem-type coverage, corrects misunderstandings, and enhances geometric reasoning. |
DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models (2022.emnlp-main)
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| Challenge: | a comprehensive evaluation of QM models should be conducted on natural texts, not on artificial adversarial examples . ral models are often not robust to adversarials, which means they predict unexpected outputs . |
| Approach: | They use a Chinese dataset to evaluate the robustness of QM models . they show that the effect of artificial adversarial examples does not work on natural texts . |
| Outcome: | The proposed model is more robust than other models on natural questions with 32 linguistic perturbations. |
How Can Synthetic Data Improve Multilingual Language Model Pretraining? A Data Quality Perspective (2026.acl-long)
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| Challenge: | Low-resource languages are a long-tail problem for multilingual LLMs due to limited high-quality training data. |
| Approach: | They propose a method that translates high-quality, knowledge-rich English data into low-resource languages . they propose SynRank, which leverages synthetic data as positive samples to train a classifier . |
| Outcome: | The proposed method matches handcrafted rule-based filtering by human experts and significantly improves knowledge-intensive tasks with less data. |
Emergent Misalignment via In-Context Learning: Narrow in-context examples can produce broadly misaligned LLMs (2026.acl-long)
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Nikita Afonin, Nikita Andriianov, Vahagn Hovhannisyan, Nikhil Bageshpura, Kyle Liu, Kevin Zhu, Sunishchal Dev, Ashwinee Panda, Oleg Rogov, Elena Tutubalina, Alexander Panchenko, Mikhail Seleznyov
| Challenge: | Recent studies have documented emergent misalignment in language models adapted on narrow examples . Emergent misalignement occurs when models are trained on narrow set of misallocated examples resulting in harmful or misleading responses . |
| Approach: | They propose to explain in-context EM as conflict between safety objectives and context-following behavior. |
| Outcome: | The proposed model is adapted on 16 in-context examples and produces misaligned responses to benign queries. |
Enhancing Multi-party Dialogue Discourse Parsing with Explanation Generation (2025.coling-main)
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| Challenge: | Multi-party dialogue discourse parsing is an important and challenging task in natural language processing. |
| Approach: | They propose a model to integrate external knowledge from Large Language Models to analyze dialogue discourse structures and semantic relations between utterances in multi-party conversations. |
| Outcome: | The proposed model outperforms the state-of-the-art (SOTA) models on two public datasets. |
Exploring Hybrid Question Answering via Program-based Prompting (2024.acl-long)
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| Challenge: | Existing approaches to question answering over heterogeneous data are limited due to large scale of information and organic coupling of heterogenous data. |
| Approach: | They propose a program-based prompting framework for hybrid question answering tasks . it integrates various functions to perform hybrid information-seeking over data . |
| Outcome: | The proposed framework surpasses baseline systems and achieves the best performance under the fewshot settings. |
RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs (2024.naacl-demo)
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| Challenge: | Recent advances in machine learning (ML) are attributed to large language models (LLMs), but their escalating memory requirements require developers to partition a large model to distribute it across multiple GPUs or TPUs. |
| Approach: | They propose a lightweight and user-friendly tool to automate distributed training and inference for LLMs and to simplify ML pipeline development. |
| Outcome: | The proposed tool automates distributed training and inference for LLMs, and simplifies ML pipeline development. |
MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models (2026.findings-eacl)
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Siwei Wu, King Zhu, Yu Bai, Yiming Liang, Yizhi Li, Haoning Wu, Jiaheng Liu, Ruibo Liu, Xingwei Qu, Xuxin Cheng, Ge Zhang, Wenhao Huang, Chenghua Lin
| Challenge: | Current multimodal benchmarks focus on facts within individual images, but neglect associative relations among multiple images. |
| Approach: | They propose a multi-image relational association task and a MMRA benchmark to evaluate LVLMs. |
| Outcome: | The proposed benchmarks show that entity-level multi-image perception tasks pose greater challenges than image-level tasks. |
Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning (2026.findings-acl)
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| Challenge: | Existing methods for storing and retrieving memory are limited by shallow semantic retrieval. |
| Approach: | They propose a memory mechanism that organizes and retrieves past experiences to support decision-making. |
| Outcome: | Experiments on LoCoMo and NarrativeQA show that CompassMem improves retrieval and reasoning performance across multiple backbone models. |
Maximal Clique Based Non-Autoregressive Open Information Extraction (2021.emnlp-main)
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| Challenge: | Open Information Extraction (OpenIE) aims to discover textual facts from a given sentence. |
| Approach: | They propose a non-autoregressive framework that generates a fact graph and a graph with an edge linking two nodes that belong to the same fact. |
| Outcome: | The proposed framework outperforms current state-of-the-art methods on two benchmark datasets and significantly outperformed the existing ones. |
InheritSumm: A General, Versatile and Compact Summarizer by Distilling from GPT (2023.findings-emnlp)
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| Challenge: | Recent advances in large language models have revolutionized the way summarization is generated. |
| Approach: | They propose a summarization model derived from GPT-3.5 through distillation that is compact and has comparable summarizing capabilities to GPT-3. |
| Outcome: | The proposed model outperforms the established best small models in prefix-tuning and full-data fine-tuned scenarios. |
HAT: Hardware-Aware Transformers for Efficient Natural Language Processing (2020.acl-main)
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| Challenge: | Extensive experiments on four machine translation tasks demonstrate that HAT can discover efficient models for different hardware (CPU, GPU, IoT device). |
| Approach: | They propose to construct a large design space with arbitrary encoder-decoder attention and heterogeneous layers and then train a SuperTransformer that efficiently produces many SubTransformers with weight sharing. |
| Outcome: | The proposed framework can find efficient models for different hardware (CPU, GPU, IoT device) it achieves 3 speedup, 3.7 smaller size over baseline Transformer; 2.7 speed up, 3.6 smaller sizes over Evolved Transformer with 12,041 less search cost and no performance loss. |
Croppable Knowledge Graph Embedding (2025.acl-long)
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| Challenge: | Knowledge Graph Embedding (KGE) is a common approach for Knowledge Grasse (KGs) in AI tasks. |
| Approach: | They propose a new KGE training framework MED that allows one training to obtain a croppable KGE model for multiple scenarios with different dimensional needs. |
| Outcome: | The proposed framework improves low-dimensional sub-models and makes high-dimensional models retain the low-dimension sub-modells’ capacity. |
Unsupervised Conversation Disentanglement through Co-Training (2021.emnlp-main)
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| Challenge: | Existing work on conversation disentanglement relies heavily on human annotations, which is expensive to obtain in practice. |
| Approach: | They propose to train a conversation disentanglement model without referencing human annotations . they use a message-pair classifier and a session classifier to retrieve local relations . |
| Outcome: | The proposed method achieves competitive performance compared to previous methods on a large movie dialogue dataset. |
MTO: A Multi-turn Conversational Text-to-OverpassQL Dataset for Enhanced OpenStreetMap Query Generation (2026.findings-acl)
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| Challenge: | a framework for constructing multi-turn Text-to-OverpassQL dialogue datasets is proposed . a dataset of over 7,800 dialogues contains more than 20,000 individual utterances . |
| Approach: | They propose a framework for constructing multi-turn Text-to-OverpassQL dialogue datasets . they convert Overpass queries into syntax trees using a custom parser based on OverpassQl . |
| Outcome: | The proposed dataset includes over 7,800 dialogues, each containing 2 to 4 user utterances . it is the first multi-turn Text-to-OverpassQL dataset built upon the OverpassNL corpus . |
Language Modeling with Sparse Product of Sememe Experts (D18-1)
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| Challenge: | Existing language modeling methods rely on large-scale text data to learn the sequential patterns of words. |
| Approach: | They propose to use sememes to represent the implicit semantics behind words for language modeling . they propose to employ sememe-driven language models to fine-grained semem-level semantics . |
| Outcome: | Experiments on language modeling and the downstream application of headline generation show the effectiveness of SDLM. |
Chain-of-Rewrite: Aligning Question and Documents for Open-Domain Question Answering (2024.findings-emnlp)
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Chunlei Xin, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun
| Challenge: | Existing approaches to answer open-domain question have encountered term mismatch and limited interaction between IR systems and large language models. |
| Approach: | They propose a method which leverages the guidance and feedback gained from the analysis to provide faithful and consistent extensions for effective question answering. |
| Outcome: | Experiments on four open-domain question answering datasets show the proposed method performs well under zero-shot settings. |
HydraRAG: Structured Cross-Source Enhanced Large Language Model Reasoning (2025.emnlp-main)
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| Challenge: | Current RAG system retrieves evidence from knowledge graphs and text documents but has limitations in multi-hop reasoning, multi-entity questions, and source verification. |
| Approach: | They propose a training-free framework that unifies graph topology, document semantics, and source reliability to support deep, faithful reasoning in large language models. |
| Outcome: | The proposed framework outperforms the current hybrid model-based model-driven system by 20.3% and 30.1% on seven benchmark datasets. |
LLMs + Persona-Plug = Personalized LLMs (2025.acl-long)
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Jiongnan Liu, Yutao Zhu, Shuting Wang, Xiaochi Wei, Erxue Min, Yu Lu, Shuaiqiang Wang, Dawei Yin, Zhicheng Dou
| Challenge: | Large language models (LLMs) have demonstrated extraordinary capabilities in natural language understanding, generation, and reasoning. |
| Approach: | They propose a plug-and-play LLM model that embeds a user-specific embedding for each individual by modeling her historical contexts through a lightweight plug-in user embedder module. |
| Outcome: | Experiments on various tasks in the language model personalization (LaMP) benchmark show that the proposed model significantly outperforms existing personalized LLM approaches. |
Learning the Beauty in Songs: Neural Singing Voice Beautifier (2022.acl-long)
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| Challenge: | Existing techniques for pitch correction are limited to intonation but ignore the overall aesthetic quality. |
| Approach: | They propose a novel time-warping approach for pitch correction to synchronize the amateur recording with the template pitch curve. |
| Outcome: | The proposed model improves intonation and vocal tone while keeping content and vocal timbre. |
ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models (2024.naacl-long)
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| Challenge: | Low-rank adaptation (LoRA) has demonstrated commendable performance as a popular method . however, it is implemented with a fixed intrinsic rank that might not be ideal for downstream tasks. |
| Approach: | They propose a method that estimates the importance score of each LoRA rank and prunes abundant LoRA ranks to improve performance. |
| Outcome: | The proposed method outperforms baselines on a variety of tasks with comparable parameters. |
Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge Injection (2026.acl-long)
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Yuwei Zhang, Wenhao Yu, Shangbin Feng, Yifan Zhu, Letian Peng, Jayanth Srinivasa, Gaowen Liu, Jingbo Shang
| Challenge: | Existing knowledge injection benchmarks for large language models lack standardized testing grounds. |
| Approach: | They propose a knowledge injection benchmark that leverages recently-added and expert-curated facts from Wikipedia’s “Did You Know...” entries. |
| Outcome: | The proposed framework improves reliability accuracy by 29.1%. |
CoreCodeBench: Decoupling Code Intelligence via Fine-Grained Repository-Level Tasks (2026.acl-long)
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Lingyue Fu, Hao Guan, Bolun Zhang, Haowei Yuan, Yaoming Zhu, Lin Qiu, ZongYu Wang, Xuezhi Cao, Xunliang Cai, Weiwen Liu, Weinan Zhang, Yong Yu
| Challenge: | Existing large language models for software engineering rely on coarse-grained pass rates obscuring specific cognitive bottlenecks. |
| Approach: | They propose a repository-level benchmark that dissects coding capabilities through atomized tasks. |
| Outcome: | The proposed framework achieves a 78.55% validity yield, surpassing the 31.7% retention rate of SWE-bench-Verified. |
LMGQS: A Large-scale Dataset for Query-focused Summarization (2023.findings-emnlp)
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Ruochen Xu, Song Wang, Yang Liu, Shuohang Wang, Yichong Xu, Dan Iter, Pengcheng He, Chenguang Zhu, Michael Zeng
| Challenge: | Lack of large-scale datasets for query-focused summarization hinders model development . lack of data limits the ability of QFS models to train robust neural models . |
| Approach: | They propose to generate a query for each summary sentence in a generic summarization annotation using a pretrained language model. |
| Outcome: | The proposed model achieves state-of-the-art zero-shot and supervised performance on multiple existing QFS benchmarks. |
Faster and Better LLMs via Latency-Aware Test-Time Scaling (2025.findings-emnlp)
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| Challenge: | Existing research has overlooked the efficiency of TTS from a latency-sensitive perspective. |
| Approach: | They propose two approaches to achieve latency-optimal TTS by branch-wise parallelism and sequence-wise parallelism. |
| Outcome: | The proposed approach achieves latency-optimal TTS for large models . branch-wise parallelism and sequence-wise parallelism are key approaches . |
Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation (2022.coling-1)
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| Challenge: | Personalized news recommendation is a ubiquitous channel in various online applications, such as Google News and MSN News. |
| Approach: | They propose a plug-and-play pre-trainer to learn both user and news encoders through multi-task pre-training. |
| Outcome: | The proposed model improves on existing models and improves inference and updating time. |
Soft Contextual Data Augmentation for Neural Machine Translation (P19-1)
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| Challenge: | Existing methods for enhancing training data are limited in natural language tasks due to text characteristics. |
| Approach: | They propose a data augmentation method that softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words. |
| Outcome: | The proposed method outperforms baseline methods on small and large scale machine translation datasets. |
FlipGuard: Defending Preference Alignment against Update Regression with Constrained Optimization (2024.emnlp-main)
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| Challenge: | Recent advances in preference alignment have significantly improved Large Language Models' ability to generate texts that align with human preferences and values. |
| Approach: | They propose a constrained optimization approach to detect and mitigate update regression with focal attention. |
| Outcome: | The proposed approach detects and mitigates update regression with focal attention while maintaining excellent overall performance. |
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue (2023.acl-long)
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| Challenge: | Existing studies show that multilingual models are less robust for semantic parsing compared to other tasks. |
| Approach: | They propose a constrained optimization technique to optimize multilingual parsing systems for multilingual use. |
| Outcome: | The proposed technique outperforms XLM-R and mT5-Large on three benchmarks and significantly outperformed other models. |
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. |
Query-LIFE: Query-aware Language Image Fusion Embedding for E-Commerce Relevance (2025.coling-industry)
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| Challenge: | Relevance module is responsible for selecting relevant products based on user queries. |
| Approach: | They propose Query-aware Language Image Fusion Embedding to address these challenges . they propose query-based multimodal fusion to integrate image and title based on product types . |
| Outcome: | The proposed model outperforms baselines in e-commerce searches . it incorporates image and title based on product types and improves performance . |
HeteroSpec: Leveraging Contextual Heterogeneity for Efficient Speculative Decoding (2026.acl-long)
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| Challenge: | Autoregressive decoding limits the inference throughput of Large Language Models due to its sequential dependency. |
| Approach: | They propose a framework that allocates verification effort in proportion to candidate uncertainty. |
| Outcome: | Speculative decoding achieves an average speedup over state-of-the-art methods . a small subset of high-confidence predictions accounts for most successful verifications . |
GenProve: Learning to Generate Text with Fine-Grained Provenance (2026.acl-long)
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| Challenge: | Existing methods for large language models (LLMs) are coarse-grained and fail to distinguish between direct quotes and complex reasoning. |
| Approach: | They propose a framework that combines supervised fine-tuning and group relative policy optimization to generate fluent answers while simultaneously producing sentence-level provenance triples. |
| Outcome: | The proposed framework outperforms 14 strong large language models in joint evaluation. |
ModalPrompt: Towards Efficient Multimodal Continual Instruction Tuning with Dual-Modality Guided Prompt (2025.emnlp-main)
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| Challenge: | Existing MCIT methods do not fully exploit the unique attribute of Large Multimodal Models and often gain performance at the expense of efficiency. |
| Approach: | They propose a multimodal continual instruction learning framework that exploits the ability of LMMs to learn mixed instruction datasets and prompts for each task. |
| Outcome: | The proposed framework achieves +14.26% performance gain on MCIT benchmarks with remarkable x1.42 inference speed free from growing computation. |
Improving Pretrained Models for Zero-shot Multi-label Text Classification through Reinforced Label Hierarchy Reasoning (2021.naacl-main)
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| Challenge: | Existing zero-shot learning methods for multi-label text classification mostly learn a matching model between the feature space of text and the label space. |
| Approach: | They propose to use a graph encoder to incorporate label hierarchies to learn effective label representations on the zero-shot multi-label text classification problem. |
| Outcome: | The proposed approach outperforms previous non-pretrained methods on the zero-shot multi-label text classification task. |
SARA: Unlocking Multilingual Knowledge in Mixture-of-Experts via Semantically Anchored Routing Alignment (2026.findings-acl)
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Tianyu Dong, Yangyang Liu, Jiang Zhou, Xinwei Wu, Xiaohu Zhao, Hao Wang, Heng Liu, Linlong Xu, Longyue Wang, Weihua Luo, Shaolin Zhu, Deyi Xiong
| Challenge: | Low-resource language tokens are often routed to different experts than those activated by high-resourced inputs, which hinders their efficacy in multilingual contexts. |
| Approach: | They propose a framework to transfer specialized capabilities from high-resource languages as anchors to low-resourced languages by using a symmetric Jensen-Shannon constraint. |
| Outcome: | The proposed framework outperforms standard instruction tuning on 5 low-resource languages and 3 benchmarks. |
ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data (2025.acl-long)
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| Challenge: | Existing methods for extracting chemical procedures from literature are insufficient and low-quality due to the inherent ambiguity of chemical language and the high cost of human annotation. |
| Approach: | They propose a fully fine-tuned large language model (LLM) as a chemical executor to convert between unstructured experimental procedures and structured action sequences. |
| Outcome: | The proposed model outperforms the baseline model on R2D and D2A tasks by 10%. |
SentiLARE: Sentiment-Aware Language Representation Learning with Linguistic Knowledge (2020.emnlp-main)
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| Challenge: | Existing pre-trained models neglect to consider linguistic knowledge of texts . existing models neglect linguistic information, which is important for sentiment analysis . |
| Approach: | They propose a model that introduces word-level linguistic knowledge into pre-trained models to enhance sentiment analysis by querying SentiWordNet to acquire sentiment polarity. |
| Outcome: | The proposed model obtains state-of-the-art performance on a variety of sentiment analysis tasks. |
Can MLLMs Reason Beyond Language? VisReason: A Comprehensive Benchmark for Vision-Centric Reasoning (2026.findings-acl)
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| Challenge: | Recent advances in multimodal large language models demonstrate strong performance on visual reasoning benchmarks. |
| Approach: | They propose a benchmark for vision-centric reasoning that integrates visual and textual information for non-trivial reasoning. |
| Outcome: | The proposed benchmark exposes gaps between humans and current MLLMs and reveals limited benefits from test-time reasoning strategies. |
OAgents: An Empirical Study of Building Effective Agents (2025.findings-emnlp)
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He Zhu, Tianrui Qin, King Zhu, Heyuan Huang, Yeyi Guan, Jinxiang Xia, Hanhao Li, Yi Yao, Ningning Wang, Pai Liu, Tianhao Peng, Xin Gui, Li Xiaowan, Yuhui Liu, Xiangru Tang, Jian Yang, Ge Zhang, Xitong Gao, Yuchen Eleanor Jiang, Changwang Zhang, Jun Wang, Jiaheng Liu, Wangchunshu Zhou
| Challenge: | a recent study shows that agent research practices are far from standard, rigorous . lack of a standard evaluation protocol makes previous works not reproducible, authors say . |
| Approach: | They conduct an empirical study on the GAIA benchmark to investigate agent design choices . they find that lack of a standard evaluation protocol makes previous works not reproducible . |
| Outcome: | The proposed framework achieves state-of-the-art performance among open-source projects. |
A Survey of Large Language Model-Based Search Agents (2026.acl-long)
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Yunjia Xi, Jianghao Lin, Yongzhao Xiao, Zheli Zhou, Rong Shan, Te Gao, Jiachen Zhu, Weiwen Liu, Yong Yu, Weinan Zhang
| Challenge: | Large Language Models (LLMs) have revolutionized web search, but their integration is static and cannot handle complex contexts. |
| Approach: | They analyze existing research and analyze existing work from the perspectives of architecture, optimization, application, and evaluation. |
| Outcome: | The proposed models can comprehend user intentions and context and execute multi-turn retrieval with dynamic planning, extending search capabilities far beyond the web. |
Planning, Creation, Usage: Benchmarking LLMs for Comprehensive Tool Utilization in Real-World Complex Scenarios (2024.findings-acl)
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Shijue Huang, Wanjun Zhong, Jianqiao Lu, Qi Zhu, Jiahui Gao, Weiwen Liu, Yutai Hou, Xingshan Zeng, Yasheng Wang, Lifeng Shang, Xin Jiang, Ruifeng Xu, Qun Liu
| Challenge: | Existing benchmarks focus on simple synthesized queries that do not reflect real-world complexity, thereby offering limited perspectives in evaluating tool utilization. |
| Approach: | They propose a benchmark to evaluate LLMs’ ability in tool utilization within real-world scenarios. |
| Outcome: | The proposed benchmark improves LLMs’ ability in tool utilization within real-world scenarios and eliminates the restriction of pre-defined toolset. |
Put It Back: Entity Typing with Language Model Enhancement (D18-1)
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| Challenge: | Existing Entity typing models suffer from noisy labels due to distant supervision . |
| Approach: | They propose to enhance existing entity typing models with language model enhancement to measure compatibility between context sentences and labels. |
| Outcome: | The proposed model significantly outperforms the state-of-the-art model on benchmark datasets and is available on github. |
LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios (2025.acl-long)
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| Challenge: | Existing benchmarks rarely focus on instruction-following in long-context scenarios or stability on different inputs. |
| Approach: | They propose a scalable dataset to evaluate LLMs’ instruction-following capabilities and stability across long contexts. |
| Outcome: | The proposed method evaluates LLMs’ instruction-following capabilities and stability across long contexts. |
A Corpus of Adpositional Supersenses for Mandarin Chinese (2020.lrec-1)
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| Challenge: | Adpositions are frequent markers of semantic relations, but they are highly ambiguous and vary significantly from language to language. |
| Approach: | They propose to annotate Chinese adpositions in a corpus with all aforementioned supersenses . they adapt a framework that defined a set of supersens according to ostensibly language-independent criteria . |
| Outcome: | The proposed corpus is the first to be broadly annotated with adposition semantics in Chinese . it shows that the supersense categories are well-suited to Chinese adepositions despite syntactic differences from English . |
xLAM: A Family of Large Action Models to Empower AI Agent Systems (2025.naacl-long)
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Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, Zhiwei Liu, Yihao Feng, Tulika Manoj Awalgaonkar, Rithesh R N, Zeyuan Chen, Ran Xu, Juan Carlos Niebles, Shelby Heinecke, Huan Wang, Silvio Savarese, Caiming Xiong
| Challenge: | Autonomous agents powered by large language models (LLMs) have attracted significant research interest, but there are few standards for developing specialized models for agent tasks. |
| Approach: | They propose a series of large action models with dense and mixture-of-expert architectures that unifies, augments, and synthesizes diverse datasets to enhance agent generalizability and performance. |
| Outcome: | The proposed models outperform GPT-4, Claude-3, and many other models in terms of tool use and outperformed GPT-based models on multiple agent ability benchmarks. |
Avoiding Knowledge Edit Skipping in Multi-hop Question Answering with Guided Decomposition (2025.findings-emnlp)
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| Challenge: | Existing methods for knowledge editing fail to work in multi-hop question answering due to 'edit skipping' edit skipping occurs due to the mismatch between the granularity of LLMs in problem-solving and the facts in the edited memory. |
| Approach: | They propose a retrieval-augmented generation-based method that edits knowledge without modifying parameters without retraining LLMs. |
| Outcome: | The proposed method outperforms state-of-the-art methods for KE in multi-hop question answering. |
Unsupervised Multi-Granularity Summarization (2022.findings-emnlp)
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Ming Zhong, Yang Liu, Suyu Ge, Yuning Mao, Yizhu Jiao, Xingxing Zhang, Yichong Xu, Chenguang Zhu, Michael Zeng, Jiawei Han
| Challenge: | Experimental results confirm the substantial superiority of GranuSum on multi-granularity summarization over strong baselines. |
| Approach: | They propose to rank events by their salience and annotate a benchmark for GranuSum that contains multiple summaries at different granularities for each document cluster. |
| Outcome: | The proposed framework is capable of producing multi-granular summaries in unsupervised manner over strong baselines. |
Learn to Relax with Large Language Models: Solving Constraint Optimization Problems via Bidirectional Coevolution (2026.acl-long)
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| Challenge: | Large Language Model (LLM)-based optimization has shown promise for autonomous problem solving, but most approaches cast LLMs as passive constraint checkers rather than proactive strategy designers. |
| Approach: | They propose an end-to-end Automated Constraint Optimization method that tightly couples operations-research principles of constraint relaxation with LLM reasoning. |
| Outcome: | Extensive experiments on three challenging COP benchmarks validate AutoCO’s consistent effectiveness and superior performance, especially in hard regimes where current methods degrade. |
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. |
Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)
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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song
| Challenge: | Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions. |
| Approach: | They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions. |
| Outcome: | The proposed model outperforms state-of-the-art methods and shows its utility. |
RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment (2026.acl-industry)
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Yingfeng Luo, Hongyu Liu, DingYang Lin, Kaiyan Chang, Chenglong Wang, Bei Li, Quan Du, Tong Xiao, JingBo Zhu
| Challenge: | Existing routing strategies rely on heuristics, external predictors, or absolute quality estimation to capture whether the large model provides a worthwhile improvement over the small one. |
| Approach: | They propose a budget allocation problem for routing large model to large model . they propose heuristics, external predictors, or absolute quality estimation to determine the optimal signal for budgeted decisions. |
| Outcome: | The proposed model outperforms heuristics, quality/difficulty estimation baselines and achieves a superior quality–budget Pareto frontier. |
M2RC-EVAL: Massively Multilingual Repository-level Code Completion Evaluation (2025.acl-long)
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Jiaheng Liu, Ken Deng, Congnan Liu, Jian Yang, Shukai Liu, He Zhu, Peng Zhao, Linzheng Chai, Yanan Wu, JinKe JinKe, Ge Zhang, Zekun Moore Wang, Guoan Zhang, Yingshui Tan, Bangyu Xiang, Zhaoxiang Zhang, Wenbo Su, Bo Zheng
| Challenge: | Existing repository-level code completion benchmarks focus on a limited number of languages . existing benchmarks report overall average scores of different languages ignoring fine-grained abilities . |
| Approach: | They propose to use repository-level code completion benchmarks to evaluate general code intelligence abilities across languages for existing code Large Language Models. |
| Outcome: | The proposed benchmarks improve the code completion abilities of existing LLMs by using two types of annotations on the parsed syntax tree. |
Federated Incremental Named Entity Recognition (2025.coling-main)
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| Challenge: | Existing methods for named entity recognition are based on pre-fixed entity types, resulting in catastrophic forgetting. |
| Approach: | They propose a model which allows for catastrophic forgetting of old entity types . they propose adaptive pseudo labeling and a prototypical relation distillation loss . |
| Outcome: | The proposed model overcomes catastrophic forgetting problem on old entity types with semantic shift. |
StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing (2023.acl-long)
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| Challenge: | Existing studies on text style transfer neglect long style transfer at the discourse level. |
| Approach: | They propose a model that transfers text style into target styles with learnable style embeddings . they use a mask-and-fill framework to explicitly fuse style-specific keywords into generation . |
| Outcome: | The proposed model outperforms baselines in style transfer and content preservation. |
CONSTRUCTURE: Benchmarking CONcept STRUCTUre REasoning for Multimodal Large Language Models (2024.findings-emnlp)
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Zhiwei Zha, Xiangru Zhu, Yuanyi Xu, Chenghua Huang, Jingping Liu, Zhixu Li, Xuwu Wang, Yanghua Xiao, Bei Yang, Xiaoxiao Xu
| Challenge: | Existing multimodal large language models lack the ability to perceive the visual world with a deep concept structure cognition. |
| Approach: | They propose a concept-level benchmark to assess MLLMs’ hierarchical concept understanding and reasoning abilities. |
| Outcome: | The proposed model outperforms state-of-the-art models in concept structure reasoning evaluation. |
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. |
STAPO: Selective Trajectory-Aware Policy Optimization for LLM Agent Training (2026.acl-long)
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Qiuyi Qi, Tian Liang, Mutian Bao, Jinjian Zhang, Dongnan Liu, Wei Zhou, Linjian Mo, Ming Kong, Jie Liu, Feng Zhang, Qiang Zhu
| Challenge: | Prior work has explored step-level supervision using Shannon-entropy-based uncertainty signals, which conflate inherent state complexity with agent confidence. |
| Approach: | They propose a hierarchical group-based RL framework that leverages normalized entropy to locate outlier steps associated with trajectory neglect and optimizes them via a mechanism of trajectory-aware reward and trajectory-independent penalty. |
| Outcome: | Experiments on ALFWorld, WebShop, and Search-Augmented QA show that STAPO achieves state-of-the-art performance while substantially alleviating trajectory neglect. |
Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models? (2023.findings-eacl)
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Jielin Qiu, William Han, Jiacheng Zhu, Mengdi Xu, Michael Rosenberg, Emerson Liu, Douglas Weber, Ding Zhao
| Challenge: | Recent advances in Large Language Models (LLMs) have shown powerful ability in various downstream applications. |
| Approach: | They propose an approach for cardiovascular disease diagnosis and automatic ECG diagnosis report generation. |
| Outcome: | The proposed approach generates high-quality cardiac diagnosis reports and achieves competitive zero-shot classification performance even compared with supervised baselines. |
Task Compass: Scaling Multi-task Pre-training with Task Prefix (2022.findings-emnlp)
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Zhuosheng Zhang, Shuohang Wang, Yichong Xu, Yuwei Fang, Wenhao Yu, Yang Liu, Hai Zhao, Chenguang Zhu, Michael Zeng
| Challenge: | Existing studies show that multi-task learning with large-scale supervised tasks suffers from negative effects across tasks. |
| Approach: | They propose a task prefix guided multi-task pre-training framework to explore the relationships among tasks. |
| Outcome: | The proposed model can be used as a foundation backbone for a wide range of tasks and as augmentation tool for data augmentation with complementary tasks. |
BeamAggR: Beam Aggregation Reasoning over Multi-source Knowledge for Multi-hop Question Answering (2024.acl-long)
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Zheng Chu, Jingchang Chen, Qianglong Chen, Haotian Wang, Kun Zhu, Xiyuan Du, Weijiang Yu, Ming Liu, Bing Qin
| Challenge: | Large language models (LLMs) have demonstrated strong reasoning capabilities, but they still suffer from factual errors when tackling knowledge-intensive tasks. |
| Approach: | They propose a reasoning framework for knowledge-intensive multi-hop QA that prioritizes promising answers at each hop of question. |
| Outcome: | The proposed framework outperforms SOTA methods on four open-domain multi-hop reasoning datasets by 8.5%. |
Parsing Tweets into Universal Dependencies (N18-1)
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| Challenge: | a new tweet treebank for English is designed to analyze tweets with universal dependencies (UD). |
| Approach: | They extend the universal dependencies guidelines to include special constructions in tweets that affect tokenization, part-of-speech tagging, and labeled dependencies. |
| Outcome: | The proposed method outperforms state-of-the-art parsers on other treebanks in accuracy and speed. |
Target-based Sentiment Annotation in Chinese Financial News (2020.lrec-1)
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| Challenge: | Using a large corpus of 8,314 target-level sentiment annotations, sentiment classification on multiple opinion aspects/targets level is unsatisfactory. |
| Approach: | They propose to construct a large-scale target-based sentiment annotation corpus on Chinese financial news text. |
| Outcome: | The proposed corpus has 8,314 target-level sentiment annotations on Chinese financial news text. |
Enhancing Reinforcement Learning with Dense Rewards from Language Model Critic (2024.emnlp-main)
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| Challenge: | Reinforcement learning (RL) can align language models with non-differentiable reward signals, such as human preferences, but the sparsity of these signals can lead to inefficient and unstable learning. |
| Approach: | They propose a framework that utilizes the critique capability of Large Language Models to produce intermediate-step rewards during RL training. |
| Outcome: | The proposed framework improves sample efficiency and the overall performance of the policy model, supported by both automatic and human evaluation. |
A Contextual Alignment Enhanced Cross Graph Attention Network for Cross-lingual Entity Alignment (2020.coling-main)
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| Challenge: | Existing methods for cross-lingual entity alignment ignore useful pre-aligned links between two KGs. |
| Approach: | They propose a novel method that jointly learns embeddings in different KGs by propagating cross-KG information through pre-aligned seed alignments. |
| Outcome: | The proposed method achieves remarkable performance gains on three benchmark cross-lingual entity alignment datasets. |
LIME: Less Is More for MLLM Evaluation (2025.findings-acl)
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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang
| Challenge: | Existing MLLM benchmarks and unified evaluation frameworks cannot accurately and efficiently reflect the ability of MLMLs. |
| Approach: | They propose a semi-automated benchmark curated using a pipeline that filters out uninformative samples and eliminates answer leakage by focusing on tasks that require image-based understanding. |
| Outcome: | The proposed benchmark reduces the number of samples by 76% and evaluation time by 77% while it can more effectively distinguish different models’ abilities. |
ARGUS: Policy-Adaptive Ad Governance via Evolving Reinforcement with Adversarial Umpiring (2026.acl-industry)
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Deyi Ji, Junyu Lu, Xuanyi Liu, Liqun Liu, Hailong Zhang, Peng Shu, Huan Yu, Jie Jiang, Tianrun Chen, Lanyun Zhu
| Challenge: | Existing regulatory policies create label inconsistencies and reasoning ambiguities in historical datasets. |
| Approach: | They propose a policy-adaptive governance system that enables evolving reinforcement through multi-agent adversarial umpiring. |
| Outcome: | The proposed system outperforms fine-tuning baselines on industrial and public datasets . it enables evolving reinforcement through multi-agent adversarial umpiring . |
On-Policy Self-Distillation for Efficient Diffusion Language Models with Early-Stage Calibration (2026.findings-acl)
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Huaisheng Zhu, MingYu Liu, Junze Liu, Zhen Ge, Tian Wang, Jiri Gesi, Dakuo Wang, Weiqi Zhang, Houyu Zhang, Yufan Guo, Xian Li, Bing Yin, Sujay Sanghavi
| Challenge: | Recent studies have demonstrated that masked diffusion models (MDMs) can surpass autoregressive models (ARMs) in various tasks. |
| Approach: | They propose a method to calibrate early token predictions without demonstration data by distilling an unnormalized target distribution into the original model. |
| Outcome: | Experiments on math, planning, and RLHF tasks show that COPSD improves both effectiveness and efficiency, and further enhances performance when combined with supervised fine-tuning. |
Length Control in Abstractive Summarization by Pretraining Information Selection (2022.acl-long)
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| Challenge: | Existing length-controllable summarization models generate summaries as long as training data . current methods only control lengths at decoding stage, but adapt to desired lengths . |
| Approach: | They propose a length-aware attention mechanism to adapt the encoding of the source based on the desired length. |
| Outcome: | The proposed method produces high-quality summaries with desired lengths and even those short lengths never seen in the training data. |
SafeScientist: Enhancing AI Scientist Safety for Risk-Aware Scientific Discovery (2025.emnlp-main)
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Kunlun Zhu, Jiaxun Zhang, Ziheng Qi, Nuoxing Shang, Zijia Liu, Peixuan Han, Yue Su, Haofei Yu, Jiaxuan You
| Challenge: | Recent advances in large language model (LLM) agents have significantly accelerated scientific discovery automation, yet raised critical ethical and safety concerns. |
| Approach: | They propose a framework to enhance safety and ethical responsibility in AI-driven scientific exploration. |
| Outcome: | The proposed framework significantly improves safety performance by 35% compared to traditional frameworks. |
Large Language Models are Better Reasoners with Self-Verification (2023.findings-emnlp)
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| Challenge: | Existing methods to solve complex natural language processing tasks require multiple steps to verify the answers. |
| Approach: | They propose to use chain of thought prompting to solve reasoning tasks with large language models. |
| Outcome: | The proposed method can improve reasoning performance on arithmetic, commonsense, and logical reasoning datasets. |
TPLinker: Single-stage Joint Extraction of Entities and Relations Through Token Pair Linking (2020.coling-main)
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| Challenge: | Existing methods to extract entities and relations from unstructured text are susceptible to cascading errors due to the separation of entity detection and relation classification. |
| Approach: | They propose a one-stage joint extraction model that detects overlapping relations while being immune from exposure bias. |
| Outcome: | The proposed model can identify overlapping relations while being immune from exposure bias. |
Probing Language Models for Pre-training Data Detection (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) have shown impressive capabilities, while raising concerns about the data contamination due to privacy issues and leakage of benchmark datasets in the pre-training phase. |
| Approach: | They propose to utilize the probing technique to examine the model’s internal activations to detect pre-training data contamination by examining the model's internal activates. |
| Outcome: | The proposed method outperforms baselines and achieves state-of-the-art performance on both WikiMIA and ArxivMIA, with additional experiments confirming its efficacy. |
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. |
Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots (D19-1)
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| Challenge: | Existing models for personalized dialogues rank responses according to their semantic relevance with the given context. |
| Approach: | They propose a dually interactive matching network (DIM) for presenting personalities of dialogue agents in retrieval-based chatbots. |
| Outcome: | The proposed model outperforms the existing model by 14.5% and 27.7% on a PERSONA-CHAT dataset. |
G-Eval: NLG Evaluation using Gpt-4 with Better Human Alignment (2023.emnlp-main)
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| Challenge: | Conventional reference-based metrics have low correlation with human judgments, especially for open-ended generation tasks. |
| Approach: | They propose to use large language models as reference-free NLG evaluators to assess the quality of NLG outputs. |
| Outcome: | The proposed framework outperforms all previous methods in two generation tasks, and has a Spearman correlation of 0.514 with human on summarization task, and a large variance in human judgments. |
CodeMEM: AST-Guided Adaptive Memory for Repository-Level Iterative Code Generation (2026.findings-acl)
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| Challenge: | Existing memory management approaches show promise but remain limited by natural language-centric representations. |
| Approach: | They propose an AST-guided dynamic memory management system for repository-level iterative code generation that maintains and updates repository context through AST operations. |
| Outcome: | The proposed system improves instruction following by 12.2% and reduces interaction rounds by 2–3 while maintaining competitive inference latency and token efficiency. |
Detecting Speaker Personas from Conversational Texts (2021.emnlp-main)
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| Challenge: | Existing studies on personas are pre-defined and hard to obtain before a conversation . a new task aims to detect speaker persona based on conversational text . |
| Approach: | They propose a task to detect speaker personas based on conversational text . they build a dataset for SPD and propose utterance-to-profile matching networks . |
| Outcome: | The proposed task outperforms baseline models and utterance-to-profile (U2P) matching networks. |
MSMO: Multimodal Summarization with Multimodal Output (D18-1)
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| Challenge: | Existing studies show that multimodal summarization can improve user satisfaction for informativeness of summaries by using information in visual modality. |
| Approach: | They propose a task to generate text and select the most relevant image from the multimodal input and a novel multimodal automatic evaluation method to evaluate multimodal outputs. |
| Outcome: | The proposed method improves user satisfaction by 12.4% compared to the current system . |
LinguaGame: A Linguistically Grounded Game-Theoretic Paradigm for Multi-Agent Dialogue Generation (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have enabled Multi-Agent Systems (MASs) where agents interact through natural language to solve complex tasks or simulate multi-party dialogues. |
| Approach: | They propose a linguistically-grounded game-theoretic paradigm for multi-agent dialogue generation that uses a training-free equilibrium approximation algorithm to model dialogue over communicative intents and strategies. |
| Outcome: | The proposed framework improves agents’ communication efficiency by helping them convey their intended meaning more effectively through language. |
How Does In-Context Learning Help Prompt Tuning? (2024.findings-eacl)
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| Challenge: | a growing number of parameter-efficient adaptation methods are needed to fine-tune large language models. |
| Approach: | They propose a method that combines prompt tuning and in-context learning to improve prompt tuning by concatenating a natural language demonstration with learned prompt embeddings. |
| Outcome: | The proposed method outperforms prompt tuning and prompt tuning on five language generation tasks. |
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data (2022.acl-long)
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| Challenge: | Experimental results show that REtrieving from the traINing datA only can lead to significant gains on multiple NLG and NLU tasks. |
| Approach: | They propose to retrieve training instances from traINing datA and concatenate them with input to generate output. |
| Outcome: | The proposed method achieves state-of-the-art results on XSum, BigPatent, and CommonsenseQA. |
Controlling Length in Abstractive Summarization Using a Convolutional Neural Network (D18-1)
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| Challenge: | Convolutional neural networks (CNNs) can't generate summaries of desired lengths due to space or length constraints. |
| Approach: | They propose an approach to constrain the summary length by extending a convolutional sequence to sequence model. |
| Outcome: | The proposed model outperforms baseline models in terms of ROUGE score, length variations and semantic similarity. |
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA (2024.findings-emnlp)
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Zhu JianHao, Changze Lv, Xiaohua Wang, Muling Wu, Wenhao Liu, Tianlong Li, Zixuan Ling, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Existing federated learning frameworks require substantial data and computational resources to develop large language models. |
| Approach: | They propose a method that distributes a quantized version of the model’s parameters during training and combine it with a popular fine-tuning method to significantly reduce communication costs. |
| Outcome: | The proposed method enables accurate estimations for parameter updates while preventing clients from accessing a model whose performance is comparable to the centrally hosted one. |
Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization (D19-1)
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| Challenge: | Existing studies measure the superiority of DA methods in terms of their performance on a specific test set, but some do not exhibit consistent improvements across translation tasks. |
| Approach: | They propose to evaluate DA methods from two perspectives to determine their generalization ability . they find that DA method's test performance does not exhibit consistent improvements across translation tasks . |
| Outcome: | The proposed methods do not exhibit consistent improvements across translation tasks. |
MLAS-LoRA: Language-Aware Parameters Detection and LoRA-Based Knowledge Transfer for Multilingual Machine Translation (2025.acl-long)
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| Challenge: | Large language models (LLMs) have demonstrated strong performance even with limited parallel data. |
| Approach: | They propose a multiple language-aware LoRA knowledge transfer framework that selectively adapts LLMs to MT by transferring knowledge from a large teacher to a small student model. |
| Outcome: | The proposed framework outperforms baseline models on multilingual language pairs by +1.7 BLEU on average. |
VoiceCraft-X: Unifying Multilingual, Voice-Cloning Speech Synthesis and Speech Editing (2025.emnlp-main)
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Zhisheng Zheng, Puyuan Peng, Anuj Diwan, Cong Phuoc Huynh, Xiaohang Sun, Zhu Liu, Vimal Bhat, David Harwath
| Challenge: | Autoregressive language model for multilingual speech editing and zero-shot text-to-speech synthesis is available in 11 languages. |
| Approach: | They introduce an autoregressive neural codec language model which unifies multilingual speech editing and zero-shot text-to-speech synthesis across 11 languages. |
| Outcome: | The model generates high-quality, natural-sounding speech, even with limited per-language data . it shows robust performance in diverse linguistic settings, even in limited per language data compared to other models . |
BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis (2023.emnlp-industry)
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| Challenge: | Recent text-to-image models require multiple passes of prompt engineering by humans to produce satisfactory results for real-world applications. |
| Approach: | They propose a deep generative model to generate high-quality prompts from raw descriptions using visual feedback. |
| Outcome: | The proposed model produces high-quality prompts from simple raw descriptions . it can be integrated to a cloud-native AI platform to provide better image generation service in the cloud. |
TROJail: Trajectory-Level Optimization for Multi-Turn Large Language Model Jailbreaks with Process Rewards (2026.acl-long)
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| Challenge: | Existing approaches to training multi-turn attackers to probe model safety vulnerabilities rely on turn-level optimization, which is insufficient for learning long-term attack strategies. |
| Approach: | They propose a multi-turn reinforcement learning problem that optimizes the harmfulness of the final-turn response as the outcome reward. |
| Outcome: | The proposed approach improves attack success rates across multiple models and benchmarks, highlighting the effectiveness of the proposed approach. |
LC4EE: LLMs as Good Corrector for Event Extraction (2024.findings-acl)
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| Challenge: | Event extraction (EE) is a critical task in natural language processing, yet deploying a practical EE system remains challenging. |
| Approach: | They propose to leverage the superior extraction capability of LLMs and instruction-following ability of LRMs to construct a robust and highly available EE system. |
| Outcome: | The proposed method can identify and correct errors in SLMs predictions based on automatically generated feedback information and improve performance. |
StatsChartMWP: A Dataset for Evaluating Multimodal Mathematical Reasoning Abilities on Math Word Problems with Statistical Charts (2025.findings-emnlp)
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| Challenge: | StatsChartMWP is a dataset for evaluating visual mathematical reasoning abilities on math word problems with statistical charts. |
| Approach: | They propose a dataset for evaluating visual mathematical reasoning abilities on math word problems with statistical charts. |
| Outcome: | The proposed model is more effective than open-source approaches. |
Invisible Prompts, Visible Threats: Malicious Font Injection in External Resources for Large Language Models (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are increasingly equipped with capabilities of real-time web search and integrated with protocols like the Model Context Protocol (MCP). |
| Approach: | They investigate the vulnerability of Large Language Models to hidden adversarial prompts . they evaluate two critical attack scenarios: malicious content relay and sensitive data leakage . |
| Outcome: | The proposed extension could introduce new security vulnerabilities. |
Concise Math Reasoning via Difficulty-Aware Distillation (2026.findings-acl)
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Yifan Wu, Jingze Shi, Bingheng Wu, Jiayi Zhang, Xiaotian Lin, Yizhang Zhu, Zhaoyang Yu, Bang Liu, Chenglin Wu, Nan Tang, Yuyu Luo
| Challenge: | Human experts tackle difficult math problems by identifying and executing a few pivotal steps rather than listing every intermediate thought. |
| Approach: | They propose a method for producing training data that mirrors concise human reasoning by rewriting a problem's solution to retain only the essential steps. |
| Outcome: | The proposed method outperforms models trained on 800k long CoT and cuts training and inference costs. |
AMALGUM – A Free, Balanced, Multilayer English Web Corpus (2020.lrec-1)
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| Challenge: | a corpus of 4M tokens is available online with a large number of high-quality annotation layers. |
| Approach: | They propose to use a genre-balanced English web corpus with multiple annotation layers . they harness knowledge from multiple annotation layer to achieve a "better than NLP" benchmark . |
| Outcome: | The proposed corpus is genre-balanced and features high-quality automatic annotation layers. |
ASTRA: An Automated Framework for Strategy Discovery, Retrieval, and Evolution for Jailbreaking LLMs (2026.acl-long)
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| Challenge: | Existing methods lack the capability for continuous learning and self-evolution from interactions, limiting the diversity and adaptability of attack strategies. |
| Approach: | They propose an automated framework capable of discovering, retrieving, and evolving attack strategies. |
| Outcome: | The proposed framework outperforms existing baselines in a black-box setting. |
Reference-free Summarization Evaluation via Semantic Correlation and Compression Ratio (2022.naacl-main)
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| Challenge: | Existing evaluation metrics for summarization use human annotations as reference. |
| Approach: | They propose a new automatic reference-free evaluation metric that compares semantic distribution between source document and summary by pretrained language models and considers summary compression ratio. |
| Outcome: | The proposed metric is more consistent with human evaluation in terms of coherence, consistency, relevance and fluency. |
Proofread: Fixes All Errors with One Tap (2024.acl-demos)
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Renjie Liu, Yanxiang Zhang, Yun Zhu, Haicheng Sun, Yuanbo Zhang, Michael Huang, Shanqing Cai, Lei Meng, Shumin Zhai
| Challenge: | Extensive experiments on a human-labeled golden set showed our tuned PaLM2-XS model achieved 85.56% good ratio. |
| Approach: | They propose a two-stage tuning approach to acquire the dedicated Large Language Model for the feature, followed by a reinforcement learning approach for targeted refinement. |
| Outcome: | The proposed model achieves 85.56% good quality on Rewrite and proofread tasks on human-labeled golden sets. |