Papers by Zhu Xu
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. |
Text-to-Distribution Prediction with Quantile Tokens and Neighbor Context (2026.acl-long)
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Yilun Zhu, Yuan Zhuang, Nikhita Vedula, Dushyanta Dhyani, Shaoyuan Xu, Mohsen Bayati, Bryan Wang, Shervin Malmasi
| Challenge: | Existing methods for text regression lack local grounding and rely on shared representations. |
| Approach: | They propose a distributional regression model with quantile tokens that insert dedicated quantiles into the input sequence. |
| Outcome: | The proposed method outperforms baseline models on the inside Airbnb and StackSample datasets. |
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. |
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. |
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 . |
FuxiTranyu: A Multilingual Large Language Model Trained with Balanced Data (2024.emnlp-industry)
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Haoran Sun, Renren Jin, Shaoyang Xu, Leiyu Pan, null Supryadi, Menglong Cui, Jiangcun Du, Yikun Lei, Lei Yang, Ling Shi, Juesi Xiao, Shaolin Zhu, Deyi Xiong
| Challenge: | Large language models exhibit significant performance discrepancies between high- and low-resource languages. |
| Approach: | They present an open-source multilingual LLM with 8 billion parameters and a multilingual instruction dataset. |
| Outcome: | The proposed model achieves consistent multilingual representations across languages. |
JointCL: A Joint Contrastive Learning Framework for Zero-Shot Stance Detection (2022.acl-long)
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| Challenge: | Existing methods achieve promising performance in in-target stance detection when trained and tested on the same datasets. |
| Approach: | They propose a joint contrastive learning framework to generalize stance features for unseen targets. |
| Outcome: | The proposed framework achieves state-of-the-art on three benchmark datasets. |
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. |
Improving Event Coreference Resolution Using Document-level and Topic-level Information (2022.emnlp-main)
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| Challenge: | Experimental results show that our model outperforms the SOTA baselines due to the encoding length limitation. |
| Approach: | They propose a longformer-based encoder and an encoder with a trigger-mask mechanism to learn sentence-level embeddings based on local context. |
| Outcome: | The proposed model outperforms the baselines on the KBP 2017 dataset. |
Modality Adaption or Regularization? A Case Study on End-to-End Speech Translation (2023.acl-short)
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| Challenge: | End-to-end speech translation models have limited training data and are often inefficient due to the inconsistency of length and representation between speech and text. |
| Approach: | They find that the "modality gap" between speech and text data is not a major problem in E2E ST . they decouple the encoder to speech encoder and text encoder, and they find that there is a 'capacity gap' |
| Outcome: | The proposed model achieves 29.0 for en-de and 40.3 for fr on the MuST-C dataset. |
Building a Macro Chinese Discourse Treebank (L18-1)
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| Challenge: | Discourse structure analysis is an important research topic in natural language processing. |
| Approach: | They propose to construct a macro discourse structure framework and annotate 147 Newswire articles. |
| Outcome: | The proposed framework can lay the foundation for further analysis of macro discourse structure. |
CorefPrompt: Prompt-based Event Coreference Resolution by Measuring Event Type and Argument Compatibilities (2023.emnlp-main)
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| Challenge: | Existing methods for event coreference resolution (ECR) do not leverage human-summarized rules to guide the model. |
| Approach: | They propose to transform ECR into a cloze-style MLM task using a prompt-based approach . they introduce two auxiliary prompt tasks, event-type compatibility and argument compatibility . |
| Outcome: | The proposed method performs well in a state-of-the-art (SOTA) benchmark. |
Python is Not Always the Best Choice: Embracing Multilingual Program of Thoughts (2024.emnlp-main)
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Xianzhen Luo, Qingfu Zhu, Zhiming Zhang, Libo Qin, Xuanyu Zhang, Qing Yang, Dongliang Xu, Wanxiang Che
| Challenge: | Program of Thoughts (PoT) is an approach characterized by its executable intermediate steps, which ensure the accuracy of the logical calculations in the reasoning process. |
| Approach: | They propose a task and model agnostic approach which harnesses strength and diversity from various languages to achieve better performance across all tasks. |
| Outcome: | The proposed approach outperforms Python Self-Consistency in almost all tasks and models and achieves comparable or superior performance on ChatGPT. |
CLHA: A Simple Yet Effective Contrastive Learning Framework for Human Alignment (2024.lrec-main)
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Feiteng Fang, Liang Zhu, Xi Feng, Jinchang Hou, Qixuan Zhao, Chengming Li, Xiping Hu, Ruifeng Xu, Min Yang
| Challenge: | Large language models (LLMs) have attracted considerable attention from academic and industrial communities due to their outstanding performance in various natural language processing tasks. |
| Approach: | They propose a Contrastive Learning Framework for Human Alignment to evaluate the noise within the data and dynamically adjust the training process. |
| Outcome: | The proposed framework surpasses other algorithms in terms of reward model scores, automatic evaluations, and human assessments on the widely used dataset "Helpful and Harmless" |
STARS: A Unified Framework for Singing Transcription, Alignment, and Refined Style Annotation (2025.findings-acl)
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Wenxiang Guo, Yu Zhang, Changhao Pan, Zhiyuan Zhu, Ruiqi Li, ZheTao Chen, Wenhao Xu, Fei Wu, Zhou Zhao
| Challenge: | Existing automated singing annotation (ASA) methods tackle isolated aspects of the annotation pipeline. |
| Approach: | They propose a framework that addresses transcription, alignment, and refined style annotations. |
| Outcome: | The proposed framework delivers comprehensive multi-level annotations encompassing: (1) precise phoneme-audio alignment, (2) robust note transcription and temporal localization, (3) expressive vocal technique identification, and (4) global stylistic characterization including emotion and pace. |
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. |
Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)
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Yongqi Li, Shen Zhou, Xiaohu Li, Xin Miao, Jintao Wen, Mayi Xu, Jianhao Chen, Birong Pan, Hankun Kang, Yuanyuan Zhu, Ming Zhong, Tieyun Qian
| Challenge: | Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation. |
| Approach: | They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved. |
| Outcome: | The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment. |
LIFTED: Multimodal Clinical Trial Outcome Prediction via Large Language Models and Mixture-of-Experts (2025.findings-emnlp)
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| Challenge: | Clinical trials are costly and pivotal processes that require substantial expenses . a new approach to integrate multimodal data for clinical outcome prediction is needed . |
| Approach: | a proposed framework transforms modality-specific data into natural language descriptions . a sparse Mixture-of-Experts mechanism then identifies shared patterns across modalities . |
| Outcome: | a proposed framework outperforms baseline methods in predicting clinical trial outcomes . it transforms modality-specific data into natural language descriptions, encoded via unified encoders . |
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. |
Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration (2022.acl-long)
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| Challenge: | Existing methods of fine-tuning vision-language navigation models require extra human-labeled data and lack self-exploration capabilities in environments. |
| Approach: | They propose a method that can self-explore environments without human labeling . they use a large-scale cross-modal pretrained model to build an in-domain dataset . |
| Outcome: | The proposed model can self-explore environments without human labeling without human supervision and generates structured instructions without human intervention. |
Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent (2025.findings-acl)
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Xueyang Feng, Jingsen Zhang, Jiakai Tang, Wei Li, Guohao Cai, Xu Chen, Quanyu Dai, Yue Zhu, Zhenhua Dong
| Challenge: | Recent advances in Large Language Models (LLMs) have propelled the development of Conversational Recommendation Agents (CRAs). |
| Approach: | They propose a multi-turn preference optimization paradigm that leverages Expectation Confirmation Theory to explicitly model the evolution of user satisfaction throughout multi-turned dialogues. |
| Outcome: | The proposed paradigm eliminates the significant sampling overhead of existing MTPO methods while ensuring the optimization process drives meaningful improvements. |
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. |
Enhancing Model Privacy in Federated Learning with Random Masking and Quantization (2025.findings-emnlp)
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Zhibo Xu, Zhu JianHao, Jingwen Xu, Changze Lv, Zhenghua Wang, Zisu Huang, Xiaohua Wang, Muling Wu, Qi Qian, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | federated learning approaches are limited by the complexity of large language models and the need for specialized expertise to protect intellectual property. |
| Approach: | They propose a federated learning approach that leverages random masking to obscure a subnetwork of model parameters and applies quantization to the remaining parameters. |
| Outcome: | The proposed approach maintains strong model performance in federated learning settings and achieves enhanced protection of model parameters compared to baseline methods. |
Improving Image Captioning with Better Use of Caption (2020.acl-main)
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| Challenge: | Existing approaches to image captioning focus on visual attention, but many do not. |
| Approach: | They propose a framework that explores semantics available in captions and leverages that to enhance both image representation and caption generation. |
| Outcome: | The proposed framework outperforms baselines on the MSCOCO dataset and is state-of-the-art under a wide range of evaluation metrics. |
Bridging the Capability Gap: Joint Alignment Tuning for Harmonizing LLM-based Multi-Agent Systems (2025.findings-emnlp)
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Minghang Zhu, Zhengliang Shi, Zhiwei Xu, Shiguang Wu, Lingjie Wang, Pengjie Ren, Zhaochun Ren, Zhumin Chen
| Challenge: | Existing methods for fine-tuning agents are often inadequate . a multi-agent system can solve complex tasks by dividing responsibilities among specialized agents . |
| Approach: | a new framework is proposed to improve agents collaboration through iterative alignment. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks on held-in and held-out tasks. |
The Illusion of Specialization: Unveiling the Domain-Invariant "Standing Committee" in Mixture-of-Experts Models (2026.acl-long)
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| Challenge: | Mixture of Experts models are widely assumed to achieve domain specialization through sparse routing. |
| Approach: | They propose a framework that analyzes routing behavior at the level of expert groups rather than individual experts. |
| Outcome: | The proposed framework analyzes routing behavior at the level of expert groups rather than individual experts. |
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. |
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. |
CrowdAgent: Multi-Agent Managed Multi-Source Annotation System (2025.emnlp-demos)
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Maosheng Qin, Renyu Zhu, Mingxuan Xia, null Chenchenkai, Zhen Zhu, Minmin Lin, Junbo Zhao, Lu Xu, Changjie Fan, Runze Wu, Haobo Wang
| Challenge: | Recent approaches to annotate data focus on labeling, but lack holistic process control . a novel system that integrates task assignment, data annotation, and quality/cost management is needed . |
| Approach: | They propose a multi-agent system that integrates task assignment, data annotation, and quality/cost management. |
| Outcome: | The proposed system automates human management by using a collaborative multi-agent system. |
Exploring Schema Generalizability of Text-to-SQL (2023.findings-acl)
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| Challenge: | Existing text-to-SQL models are limited in their generalizability, despite their performance being over-estimated. |
| Approach: | They propose a framework to generate novel text-to-SQL data via automatic and synchronous (DS, SQL) pair altering. |
| Outcome: | The proposed framework generates text-to-SQL data via automatic and synchronous (DS, SQL) pair altering. |
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%. |
BMRetriever: Tuning Large Language Models as Better Biomedical Text Retrievers (2024.emnlp-main)
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Ran Xu, Wenqi Shi, Yue Yu, Yuchen Zhuang, Yanqiao Zhu, May Dongmei Wang, Joyce Ho, Chao Zhang, Carl Yang
| Challenge: | Developing effective biomedical retrieval models is important for excelling at knowledge-intensive biomedically tasks but still challenging due to the lack of sufficient publicly annotated biomedic data and computational resources. |
| Approach: | They propose a series of dense retrievers for enhancing biomedical retrieval via unsupervised pre-training on large biomedically corpora, followed by instruction fine-tuning on a combination of labeled datasets and synthetic pairs. |
| Outcome: | Experiments on 5 biomedical tasks across 11 datasets confirm the performance of the retrieval model on various biomedically demanding tasks. |
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. |
Abacus-SQL: A Text-to-SQL System Empowering Cross-Domain and Open-Domain Database Retrieval (2025.acl-demo)
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| Challenge: | Existing text-to-SQL systems often lack retrieval capabilities for open-domain databases, requiring users to manually filter relevant databases. |
| Approach: | They propose to use database retrieval technology to locate the required databases in an open-domain database environment and enhance system cross-domain transferability through data augmentation methods. |
| Outcome: | The proposed system performs excellently in multi-turn text-to-SQL tasks, validating the proposed approach’s effectiveness. |
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. |
StructMem: Structured Memory for Long-Horizon Behavior in LLMs (2026.acl-short)
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| Challenge: | Existing memory systems lack structure and efficiency in capturing relationships between events. |
| Approach: | They propose a structure-enriched hierarchical memory framework that preserves event-level bindings and induces cross-event connections. |
| Outcome: | The proposed framework preserves event-level bindings and induces cross-event connections while reducing token usage, API calls, and runtime compared to prior memory systems. |
3DS: Medical Domain Adaptation of LLMs via Decomposed Difficulty-based Data Selection (2025.emnlp-main)
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Hongxin Ding, Yue Fang, Runchuan Zhu, Xinke Jiang, Jinyang Zhang, Yongxin Xu, Weibin Liao, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Effective domain adaptation typically involves supervised fine-tuning on carefully selected instruction-tuned data. |
| Approach: | They propose a model-centric data selection framework that aligns data selection with the model’s knowledge distribution to improve model performance. |
| Outcome: | The proposed framework outperforms existing methods by up to 2.97% accuracy in the healthcare domain. |
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. |
TLSA: LLM-Guided Text-Label Space Alignment with Contrastive Learning for Generalized Category Discovery (2026.acl-long)
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| Challenge: | Existing methods for generalized category discovery suffer from weak text–label alignment, inconsistent objectives across known and novel categories, and poor discrimination of semantically similar clusters. |
| Approach: | They propose a unified framework that enforces contrastive alignment between text and label representations within a shared semantic space. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on four benchmark datasets. |
CityEQA: A Hierarchical LLM Agent on Embodied Question Answering Benchmark in City Space (2025.emnlp-main)
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Yong Zhao, Kai Xu, Zhengqiu Zhu, Yue Hu, Zhiheng Zheng, Yingfeng Chen, Yatai Ji, Chen Gao, Yong Li, Jincai Huang
| Challenge: | Embodied Question Answering (EQA) tasks are primarily focused on indoor environments, leaving the complexities of urban settings unexplored. |
| Approach: | They propose a task where an embodied agent answers open-vocabulary questions in dynamic city spaces. |
| Outcome: | The proposed agent achieves 60.7% of human-level answering accuracy compared to baselines . the proposed agent outperforms existing agents in open-ended city spaces . |
EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records (2024.emnlp-main)
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Wenqi Shi, Ran Xu, Yuchen Zhuang, Yue Yu, Jieyu Zhang, Hang Wu, Yuanda Zhu, Joyce Ho, Carl Yang, May Dongmei Wang
| Challenge: | EHRAgent enables clinicians to interact with EHRs using natural language . reliance on rule-based conversion systems often necessitates additional training or effort from data engineers. |
| Approach: | They propose a large language model agent that generates and executes code in natural language to facilitate clinicians in directly interacting with EHRs. |
| Outcome: | The proposed agent outperforms the strongest baseline by up to 29.6% in success rate on three real-world EHR datasets. |
Self-Error-Instruct: Generalizing from Errors for LLMs Mathematical Reasoning (2025.acl-long)
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Erxin Yu, Jing Li, Ming Liao, Qi Zhu, Boyang Xue, Minghui Xu, Baojun Wang, Lanqing Hong, Fei Mi, Lifeng Shang
| Challenge: | Existing approaches to learning from errors synthesize training data by extrapolating from isolated bad cases, thereby failing to generalize the extensive patterns inherent within these cases. |
| Approach: | They propose a framework that synthesizes more generalized training data from isolated bad cases by extrapolating from isolated cases. |
| Outcome: | The proposed framework synthesizes more generalized training data to address these model weaknesses. |
Learning to Match Representations is Better for End-to-End Task-Oriented Dialog System (2024.findings-emnlp)
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| Challenge: | Existing systems for task-oriented dialogue lack belief states as supervisory signals. |
| Approach: | They propose a method for knowledge retrieval driven by matching representations . they use a matching signal extractor to extract matching representation between contexts and entities . |
| Outcome: | Experiments on three standard benchmarks show that the proposed method performs better than existing approaches. |
FinSight: Towards Real-World Financial Deep Research (2026.acl-long)
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| Challenge: | FinSight is the first multi-agent framework for automating end-to-end professional, multimodal financial reports. |
| Approach: | They propose a code agent with variable memory architecture that unifies data, tools, and agents into a programmable variable space. |
| Outcome: | The proposed framework outperforms leading deep research systems in factual accuracy, analytical depth, and presentation quality. |
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 . |
Enhancing Speech Large Language Models with Prompt-Aware Mixture of Audio Encoders (2025.emnlp-main)
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Weiqiao Shan, Yuang Li, Yuhao Zhang, Yingfeng Luo, Chen Xu, Xiaofeng Zhao, Long Meng, Yunfei Lu, Min Zhang, Hao Yang, Tong Xiao, JingBo Zhu
| Challenge: | Existing work on integrating audio encoders with large language models (LLMs) has focused on semantic understanding tasks, but different tasks may require distinct features that emphasize either semantic or acoustic aspects. |
| Approach: | They propose to use a prompt-aware mixture to enhance the Speech LLM that uses multiple audio encoders to extract different features based on the prompt. |
| Outcome: | The proposed approach outperforms all single-encoder Speech LLMs on ASR, speaker number verification, and AC tasks. |
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. |
How is BERT surprised? Layerwise detection of linguistic anomalies (2021.acl-long)
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| Challenge: | a number of studies have shown that transformer-based language models detect when a word is anomalous in context, but likelihood scores do not tell the cause of the anomaly. |
| Approach: | They propose to use Gaussian models for density estimation at intermediate layers of three language models to evaluate grammaticality. |
| Outcome: | The proposed method on BLiMP shows that language models employ different mechanisms to detect different types of linguistic anomalies. |
Sparse Activation Editing for Reliable Instruction Following in Narratives (2025.emnlp-main)
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| Challenge: | Existing benchmarks fail to capture the challenges of instruction following in complex narrative contexts. |
| Approach: | They propose a training-free framework that identifies and edits instruction-relevant neurons using only natural language instructions without requiring labelled data. |
| Outcome: | The proposed framework improves instruction following by identifying and editing instruction-relevant neurons using only natural language instructions, without requiring labelled data. |
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)
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Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang
| Challenge: | i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data. |
| Approach: | They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data. |
| Outcome: | i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks. |
Have my arguments been replied to? Argument Pair Extraction as Machine Reading Comprehension (2022.acl-short)
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| Challenge: | Existing studies identify argument pairs indirectly by predicting sentence-level relations between two documents, neglecting the holistic argument-level interactions. |
| Approach: | They propose to use machine reading comprehension to extract argument pairs from two documents . they propose to employ an AM query to identify all arguments in two documents, then an APE query to extract its paired arguments from another document. |
| Outcome: | The proposed method outperforms the state-of-the-art method by 7.11% in F1 score. |
Employing Text Matching Network to Recognise Nuclearity in Chinese Discourse (C18-1)
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| Challenge: | Experimental results show that nuclearity recognition is a challenging task in Chinese discourse parsing due to the need for more deep semantic information. |
| Approach: | They propose a text matching network that encodes discourse units and paragraphs by combining Bi-LSTM and CNN to capture global dependency information and local n-gram information. |
| Outcome: | The proposed model outperforms baselines on the Chinese Discourse TreeBank . the proposed model is based on a novel text matching network . |
Empowering Language Models with Knowledge Graph Reasoning for Open-Domain Question Answering (2022.emnlp-main)
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| Challenge: | Existing Language Models lack the power to store all required knowledge, resulting in a lack of ability to infer out-of-context knowledge. |
| Approach: | They propose a Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
| Outcome: | The proposed model can be plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. |
Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives (2024.findings-acl)
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| Challenge: | Existing datasets for narrative understanding fail to represent complexity and uncertainty of relationships in real-life social scenarios. |
| Approach: | They propose a benchmark for extracting and analysing intricate character relation graphs from detective narratives using large-scale large-language models. |
| Outcome: | The proposed dataset extracts and analyses character relation graphs from detective narratives using advanced Large Language Models like GPT-3.5, GPT-4, and Llama2 . |
Table-as-Search: Agentic Information Seeking is Table Completion (2026.findings-acl)
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Tian Lan, Felix Henry, Bin Zhu, Qianghuai Jia, Junyang Ren, Qihang PU, Haijun Li, Longyue Wang, Zhao Xu, Weihua Luo
| Challenge: | Current Information Seeking (InfoSeeking) agents struggle to maintain focus and coherence during long-horizon exploration, as tracking search states within one plain-text context is inherently fragile. |
| Approach: | They propose a structured planning framework that reformulates the InfoSeeking task as a Table Completion task. |
| Outcome: | The proposed framework outperforms state-of-the-art frameworks across three kinds of benchmarks, including multi-agent framework and commercial systems. |
Automated Creativity Evaluation for Large Language Models: A Reference-Based Approach (2025.findings-emnlp)
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| Challenge: | Existing methods for evaluating creativity of machine-generated texts rely on costly manual annotations or fail to align closely with human assessments. |
| Approach: | They propose an automated method based on the Torrance Test of Creative Writing (TTCW) . |
| Outcome: | The proposed method improves the alignment between LLM evaluations and human assessments. |
Sparsity-Accelerated Training for Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated proficiency across various NLP tasks but often require additional training, such as continual pre-training and supervised fine-tuning. |
| Approach: | They propose to leverage sparsity in pre-trained LLMs to accelerate training by disregarding computations for unimportant neurons. |
| Outcome: | The proposed framework achieves comparable or superior performance to standard training while significantly accelerating the process. |
ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser (2021.naacl-main)
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| Challenge: | Existing semantic parsing models struggle to adapt to unseen database schemas . a new architecture, ShadowGNN, processes schemas at abstract and semantic levels . |
| Approach: | They propose a new architecture which processes schemas at abstract and semantic levels. |
| Outcome: | The proposed architecture outperforms state-of-the-art models on a text-to-sql benchmark . it uses domain-independent representations to extract logical linking between question and schema . |
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. |
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. |
DIDS: Domain Impact-aware Data Sampling for Large Language Model Training (2025.emnlp-main)
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Weijie Shi, Jipeng Zhang, Yaguang Wu, Jingzhi Fang, Shibo Zhang, Yao Zhao, Hao Chen, Ruiyuan Zhang, Yue Cui, Jia Zhu, Sirui Han, Jiajie Xu, Xiaofang Zhou
| Challenge: | Existing approaches for optimizing domain-level sampling strategies struggle with maintaining intra-domain consistency and accurately measuring domain impact. |
| Approach: | They propose to use a Fisher-Information Matrix-guided metric to measure domain impact to ensure intra-domain consistency and accuracy. |
| Outcome: | The proposed model achieves 3.4% higher average performance while maintaining comparable training efficiency. |
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. |
An unsupervised framework for tracing textual sources of moral change (2021.findings-emnlp)
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| Challenge: | Existing studies on moral sentiment classification and temporal inference of moral sentiment have not quantified the origins of these changes. |
| Approach: | They propose an unsupervised framework for tracing textual sources of moral change toward entities through time. |
| Outcome: | The proposed framework captures fine-grained human moral judgments and identifies coherent source topics of moral change triggered by historical events. |
Accelerating Multiple Intent Detection and Slot Filling via Targeted Knowledge Distillation (2023.findings-emnlp)
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| Challenge: | Existing non-autoregressive Spoken Language Understanding models suffer from multi-modality problem . current methods have little prior knowledge about the reference during inference . |
| Approach: | They propose a Targeted Knowledge Distillation Framework (TKDF) for multi-intent SLU that utilizes the knowledge distillation method to improve the performance. |
| Outcome: | The proposed model outperforms existing models on two public multi-intent datasets while speeding up by over 4.5 times. |
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. |
Joint Semantic and Strategy Matching for Persuasive Dialogue (2023.findings-emnlp)
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Chuhao Jin, Yutao Zhu, Lingzhen Kong, Shijie Li, Xiao Zhang, Ruihua Song, Xu Chen, Huan Chen, Yuchong Sun, Yu Chen, Jun Xu
| Challenge: | Persuasive dialogue models rely on utterance semantic matching and a key aspect has been ignored . compared with utterrance semantics, conversation strategies are high-level concepts, which can be informative and provide complementary information to achieve effective persuation. |
| Approach: | They propose to model conversation semantics and strategies to match them using a BERT-like module and an auto-regressive predictor. |
| Outcome: | The proposed model improves state-of-the-art by 5% on a small and 37% on 'large' datasets. |
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. |
UMRSpell: Unifying the Detection and Correction Parts of Pre-trained Models towards Chinese Missing, Redundant, and Spelling Correction (2023.acl-long)
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| Challenge: | Chinese Spelling Correction (CSC) is a task of detecting and correcting misspelled charac- ters in Chinese texts. |
| Approach: | They propose a model to learn detection and correction parts together from a multi-task learning perspective. |
| Outcome: | The proposed model can learn detection and correction parts together from a multi-task learning perspective. |
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models (2024.acl-long)
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| Challenge: | Retrieval-augmented generation (RAG) is a main technique for alleviating hallucinations in large language models. |
| Approach: | They propose to integrate RAG into large language models to analyze word-level hallucinations using a corpus of 18,000 naturally generated responses from diverse LLMs. |
| Outcome: | The proposed model can fine tune a relatively small LLM and achieve a competitive hallucination detection performance when compared to the existing prompt-based approaches. |
Alignment for Efficient Tool Calling of Large Language Models (2025.emnlp-main)
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| Challenge: | Recent advances in tool learning have enabled large language models to integrate external tools, enhancing their task performance by expanding their knowledge boundaries. |
| Approach: | They propose a framework that combines probabilistic knowledge boundary estimation with dynamic decision-making to allow LLMs to better assess when to invoke tools based on their confidence. |
| Outcome: | The proposed framework shows significant improvements in tool efficiency by reducing unnecessary tool usage. |
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. |
LoRA-drop: Efficient LoRA Parameter Pruning based on Output Evaluation (2025.coling-main)
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| Challenge: | Low-Rank Adaptation (LoRA) is currently the most commonly used PEFT method for fine-tuning models with billions of parameters. |
| Approach: | They propose to use low-rank Adaptation to evaluate LoRA parameter features and then retain LoRA for important layers and the other layers share the same LoRA. |
| Outcome: | The proposed method achieves comparable performance to full fine-tuning and LoRA while retaining 50% of the LoRA parameters on average. |
TS-SQL: Test-driven Self-refinement for Text-to-SQL (2025.findings-emnlp)
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| Challenge: | null |
| Approach: | null |
| Outcome: | null |
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. |
AdapTime: Enabling Adaptive Temporal Reasoning in Large Language Models (2026.findings-acl)
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Yimin Deng, Yejing Wang, Zhenxi Lin, Zichuan Fu, Guoshuai Zhao, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Xian Wu, Li Zhu, Xueming Qian
| Challenge: | Existing methods for temporal reasoning are limited and apply a fixed pipeline to all questions. |
| Approach: | They propose an adaptive temporal reasoning method that dynamically executes reasoning steps based on context and task requirements. |
| Outcome: | Experiments on two temporal QA benchmarks show the proposed method works. |
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. |
Mining Cross-Cultural Differences and Similarities in Social Media (P18-1)
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| Challenge: | a new paper examines the problem of computing cross-cultural differences and similarities in natural language understanding . cross-culture differences are important for cross-lingual research, especially in social media . |
| Approach: | They propose a framework for computing cross-cultural differences and similarities from social media . they propose to use a social media platform to find similar terms for slang across languages . |
| Outcome: | The proposed framework outperforms baseline methods on two novel tasks. |
RA2FD: Distilling Faithfulness into Efficient Dialogue Systems (2024.emnlp-main)
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| Challenge: | Retrieval Augmented Generation (RAG) is effective but inference inefficient, while Retrieral Free Generations (RFG) are more efficient but sacrifice faithfulness. |
| Approach: | They propose a retrieval-free model training scheme that uses a teacher-student framework to distill the faithfulness capacity of a student's knowledge-infused responses. |
| Outcome: | The proposed model surpasses the previous SOTA RFG model on knowledge-grounded dialogue datasets by an average of 33% while improving inference efficiency. |
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. |
StreamMeCo: Long-Term Agent Memory Compression for Efficient Streaming Video Understanding (2026.findings-acl)
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Junxi Wang, Te Sun, Jiayi Zhu, Junxian Li, Haowen Xu, Zichen Wen, Xuming Hu, Zhiyu li, Linfeng Zhang
| Challenge: | StreamMeCo is an efficient Stream Agent Memory Compression framework for video understanding. |
| Approach: | They propose an efficient Stream Agent Memory Compression framework that evicts redundant memory nodes and introduces a time-decay memory retrieval mechanism to mitigate performance degradation. |
| Outcome: | The proposed framework achieves 1.87 speedup in memory retrieval while delivering an average accuracy improvement of 1.0% on three challenging benchmark datasets. |
Neural reality of argument structure constructions (2022.acl-long)
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| Challenge: | lexicalist linguistic theories assume argument structure is predictable from meaning of verbs . construction grammarians propose argument structure constructions distinct from verbs. |
| Approach: | They adapt psycholinguistic studies to probe for the existence of argument structure constructions in Transformer-based language models. |
| Outcome: | The proposed method could be used to probe argument structure constructions in LMs . the study shows that LM learners prefer grouping by construction over verb grouping . |
A Diffusion Model for Event Skeleton Generation (2023.findings-acl)
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| Challenge: | Existing methods for event schema generation are noise-sensitive and error-accumulating, e.g., inability to correct errors while generating schema. |
| Approach: | They propose a novel diffusion event graph model that embeds and roundes event graphs into learnable latent representations and a denoising process to maintain the model's robustness. |
| Outcome: | The proposed model achieves better results than existing state-of-the-art models on three IED bombing datasets. |
Code over Words: Overcoming Semantic Inertia via Code-Grounded Reasoning (2026.findings-acl)
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| Challenge: | Large Language Models struggle with semantic inertia, a problem that is often attributed to natural language encoding, which entangles descriptive semantics and logical rules, leading to persistent hallucinations of familiar physics despite explicit contradictory rules. |
| Approach: | They propose a framework that decouples logical dynamics from visual priors via amortized theory induction and counterfactual contrastive alignment. |
| Outcome: | The proposed framework outperforms expensive inference-time search methods in both efficiency and accuracy. |
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. |
MCDTB: A Macro-level Chinese Discourse TreeBank (C18-1)
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| Challenge: | Discourse analysis is becoming increasingly important in the field of natural language processing. |
| Approach: | They propose to annotate macro discourse information and additional discourse information to make annotation more objective and accurate. |
| Outcome: | The results show that the annotations are more objective and accurate than the previous ones. |
ClusterUCB: Efficient Gradient-Based Data Selection for Targeted Fine-Tuning of LLMs (2025.findings-emnlp)
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| Challenge: | Gradient-based data influence approximation is not feasible in practice. |
| Approach: | They propose a gradient-based data selection framework with clustering and a modified Upper Confidence Bound algorithm to solve this problem. |
| Outcome: | The proposed framework can achieve comparable results to the original gradient-based data selection methods while reducing computational consumption. |
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. |
Medical Graph RAG: Evidence-based Medical Large Language Model via Graph Retrieval-Augmented Generation (2025.acl-long)
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Junde Wu, Jiayuan Zhu, Yunli Qi, Jingkun Chen, Min Xu, Filippo Menolascina, Yueming Jin, Vicente Grau
| Challenge: | GraphRAG framework is designed to enhance LLMs in generating evidence-based medical responses. |
| Approach: | They propose a graph-based Retrieval-augmented generation framework to enhance LLMs in generating evidence-based medical responses. |
| Outcome: | The proposed framework outperforms state-of-the-art models on 9 medical Q&A benchmarks, 2 health fact-checking datasets, and a long-form generation test set. |
Video2Roleplay: A Multimodal Dataset and Framework for Video-Guided Role-playing Agents (2025.emnlp-main)
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| Challenge: | Existing approaches to RPAs focus on static role profiles, overlooking dynamic perceptual abilities inherent to humans. |
| Approach: | They propose a framework that combines adaptive temporal sampling with dynamic and static role profiles. |
| Outcome: | The proposed framework combines adaptive temporal sampling with dynamic and static role profiles. |
CompileAgent: Automated Real-World Repo-Level Compilation with Tool-Integrated LLM-based Agent System (2025.acl-long)
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Li Hu, Guoqiang Chen, Xiuwei Shang, Shaoyin Cheng, Benlong Wu, LiGangyang LiGangyang, Xu Zhu, Weiming Zhang, Nenghai Yu
| Challenge: | CompileAgent is the first LLM-based agent framework dedicated to repo-level compilation. |
| Approach: | They propose a LLM-based agent framework dedicated to repo-level compilation. |
| Outcome: | The proposed method significantly improves compilation success rate, ranging from 10% to 71%. |
Entailment Tree Explanations via Iterative Retrieval-Generation Reasoner (2022.findings-naacl)
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Danilo Neves Ribeiro, Shen Wang, Xiaofei Ma, Rui Dong, Xiaokai Wei, Henghui Zhu, Xinchi Chen, Peng Xu, Zhiheng Huang, Andrew Arnold, Dan Roth
| Challenge: | Large language models have achieved high performance on various natural language benchmarks, but the explainability of their output remains elusive. |
| Approach: | They propose an architecture called iterative retrieval-generation reasoner that generates an entailment tree that explains a given hypothesis by using premises from C. |
| Outcome: | The proposed model outperforms existing benchmarks on premise retrieval and entailment tree generation with around 300% gain in overall correctness. |
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. |
APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoning (2023.acl-long)
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Soumya Sanyal, Yichong Xu, Shuohang Wang, Ziyi Yang, Reid Pryzant, Wenhao Yu, Chenguang Zhu, Xiang Ren
| Challenge: | Existing methods to improve logical reasoning skills require complex data processing. |
| Approach: | They propose an adaptive pretraining approach to improve logical reasoning over text . they use a subset of Wikipedia sentences for pretraining and a sentence-level classification loss . |
| Outcome: | The proposed model outperforms baselines on LogiQA and ReClor. |
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. |
Dynamic Curriculum Learning for Low-Resource Neural Machine Translation (2020.coling-main)
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| Challenge: | Recent work on neural machine translation (NMT) has demonstrated impressive performance improvements and became the de-facto standard. |
| Approach: | They propose a dynamic curriculum learning method to reorder training samples in training using a Transformer-based system. |
| Outcome: | The proposed method outperforms baselines on three low-resource machine translation benchmarks and different sized data of WMT’16 En-De. |
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%. |
Learning to Describe for Predicting Zero-shot Drug-Drug Interactions (2023.emnlp-main)
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| Challenge: | Existing computational methods for DDI prediction fail to capture interactions for new drugs due to the lack of knowledge. |
| Approach: | They propose a problem setup as zero-shot DDI prediction that deals with the case of new drugs by using textual information from online databases. |
| Outcome: | The proposed method improves on several settings including zero-shot and few-shot DDI prediction and the selected texts are semantically relevant. |
Second Language (Arabic) Acquisition of LLMs via Progressive Vocabulary Expansion (2025.acl-long)
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Jianqing Zhu, Huang Huang, Zhihang Lin, Juhao Liang, Zhengyang Tang, Khalid Almubarak, Mosen Alharthi, Bang An, Juncai He, Xiangbo Wu, Fei Yu, Junying Chen, Ma Zhuoheng, Yuhao Du, He Zhang, Saied Alshahrani, Emad A. Alghamdi, Lian Zhang, Ruoyu Sun, Haizhou Li, Benyou Wang, Jinchao Xu
| Challenge: | In the evolving landscape of large language models, the predominant focus has been on English and Chinese. |
| Approach: | They propose to utilize Arabic-specific vocabulary in the tokenizer to accelerate decoding. |
| Outcome: | The proposed model achieves decent performance comparable to the best Arabic LLMs across various Arabic benchmarks. |
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. |
Dynamic Prefix as Instructor for Incremental Named Entity Recognition: A Unified Seq2Seq Generation Framework (2025.findings-acl)
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| Challenge: | Named Entity Recognition (NER) is a fundamental problem in information extraction. |
| Approach: | They propose a parameter-efficient method for Incremental Named Entity Recognition (INER) task aimed at updating a model to extract entities from an expanding set of entity type candidates by employing a dynamic prefix as a task instructor to guide the generative model. |
| Outcome: | Empirical results show that the proposed method preserves task-invariant knowledge while adapting to new entities with minimal parameter updates. |
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 . |
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. |
RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis (2025.findings-acl)
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Pengzuo Wu, Yuhang Yang, Guangcheng Zhu, Chao Ye, Hong Gu, Xu Lu, Ruixuan Xiao, Bowen Bao, Yijing He, Liangyu Zha, Wentao Ye, Junbo Zhao, Haobo Wang
| Challenge: | Existing benchmarks for large language models focus on simple, flat table structures. |
| Approach: | They propose a benchmark to evaluate the performance of both Large Language Models and Multimodal LLMs across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs and Multimodal LLM models across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
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 . |
SCCS: Semantics-Consistent Cross-domain Summarization via Optimal Transport Alignment (2023.findings-acl)
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Jielin Qiu, Jiacheng Zhu, Mengdi Xu, Franck Dernoncourt, Trung Bui, Zhaowen Wang, Bo Li, Ding Zhao, Hailin Jin
| Challenge: | Existing methods for multimodal summarization ignore the structure and semantics of the whole video and article. |
| Approach: | They propose a semantic-consistent cross-domain summarization model that extracts features from video and article and uses fusion methods to select representative one. |
| Outcome: | The proposed model produces high-quality multimodal summaries on three MSMO datasets. |
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. |
KnowVrDU: A Unified Knowledge-aware Prompt-Tuning Framework for Visually-rich Document Understanding (2024.lrec-main)
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Yunqi Zhang, Yubo Chen, Jingzhe Zhu, Jinyu Xu, Shuai Yang, Zhaoliang Wu, Liang Huang, Yongfeng Huang, Shuai Chen
| Challenge: | Existing methods for integrating layout and image features into pre-training language models are not suitable for few-shot settings. |
| Approach: | They propose to reformulate VrDU tasks into a single question-answering format with task-specific prompts and train the pre-trained model with the parameter-efficient prompt tuning method. |
| Outcome: | The proposed framework can be used in few-shot settings and reduces data requirements. |
A Survey on Training-free Alignment of Large Language Models (2025.findings-emnlp)
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Birong Pan, Yongqi Li, Weiyu Zhang, Wenpeng Lu, Mayi Xu, Shen Zhou, Yuanyuan Zhu, Ming Zhong, Tieyun Qian
| Challenge: | a survey of large language models (LLMs) aims to ensure outputs adhere to human values, ethical standards, and legal norms. |
| Approach: | They present the first systematic review of TF alignment methods . they categorize them by stages of pre-decoding, in-decoder and post-decoration . |
| Outcome: | The proposed methods are based on training-free (TF) alignment techniques . they are able to be used in open-source and closed-source environments without retraining . |
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. |
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. |
TranSFormer: Slow-Fast Transformer for Machine Translation (2023.findings-acl)
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| Challenge: | Prior work has focused on treating subwords as basic units in developing such systems. |
| Approach: | They propose a slow-fast two-stream learning model that uses a “slow” branch to deal with subword sequences and a "fast" branch to cope with longer character sequences. |
| Outcome: | The proposed model shows consistent BLEU improvements (larger than 1 BLUE point) on several machine translation benchmarks. |
ZoomEye: Enhancing Multimodal LLMs with Human-Like Zooming Capabilities through Tree-Based Image Exploration (2025.emnlp-main)
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| Challenge: | Multimodal Large Language Models (MLLMs) have shown impressive capabilities in vision-language understanding but their visual input remains fixed throughout the reasoning process. |
| Approach: | They propose a model-agnostic tree search algorithm tailored for vision-level reasoning that allows MLLMs to explore textual tokens while visual input remains fixed throughout reasoning process. |
| Outcome: | The proposed algorithm outperforms strong large models such as GPT-4o on high-resolution benchmarks and improves performance on a series of elaborate high-level benchmarks. |
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. |
iAgent: LLM Agent as a Shield between User and Recommender Systems (2025.findings-acl)
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Wujiang Xu, Yunxiao Shi, Zujie Liang, Xuying Ning, Kai Mei, Kun Wang, Xi Zhu, Min Xu, Yongfeng Zhang
| Challenge: | Traditional recommender systems focus on the user-platform paradigm, where users are directly exposed under the control of the platform's recommendation algorithms. |
| Approach: | They propose a user-agent-platform paradigm where agent serves as the protective shield between user and recommender system that enables indirect exposure. |
| Outcome: | The proposed model improves 16.6% over baselines on four datasets and mitigates echo chamber effects and reduces model bias in disadvantaged users. |
Automatic Rule Induction for Efficient Semi-Supervised Learning (2022.findings-emnlp)
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| Challenge: | Existing approaches to generalize from labeled and unlabeled data are difficult to explain and behave unreliably. |
| Approach: | They propose a framework for automatic discovery and integration of symbolic rules into pretrained transformer models by using an attention mechanism. |
| Outcome: | The proposed framework can improve state-of-the-art methods with no manual effort and minimal computational overhead. |
Lego-MT: Learning Detachable Models for Massively Multilingual Machine Translation (2023.findings-acl)
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| Challenge: | Existing monolithic models for multilingual neural machine translation encounter parameter interference and inefficient inference for large models. |
| Approach: | They propose a detachable multi-way model that assigns each language to an individual branch . they use data from OPUS to build a translation benchmark covering 433 languages . |
| Outcome: | The proposed model outperforms existing models in OPUS and is faster than existing models. |
AMA: Adaptive Memory via Multi-Agent Collaboration (2026.findings-acl)
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Weiquan Huang, Zixuan Wang, Hehai Lin, Sudong Wang, Bo Xu, Qian Li, Beier Zhu, Linyi Yang, Chengwei Qin
| Challenge: | Existing approaches to longterm memory rely on rigid retrieval granularity, accumulation-heavy maintenance strategies, and coarse-grained update mechanisms. |
| Approach: | They propose a framework that leverages coordinated agents to manage memory across multiple granularities. |
| Outcome: | The proposed framework outperforms state-of-the-art benchmarks while reducing token consumption by approximately 80%. |
CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection (2022.findings-naacl)
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| Challenge: | Existing methods for multimodal sentiment detection do not consider token-level feature fusion. |
| Approach: | They propose a method for multimodal sentiment detection using a combination of text and image to encode and fuse token-level features. |
| Outcome: | The proposed method can fuse multimodal features with token-level features on three publicly available multimodal datasets. |
On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark (2022.findings-acl)
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Hao Sun, Guangxuan Xu, Jiawen Deng, Jiale Cheng, Chujie Zheng, Hao Zhou, Nanyun Peng, Xiaoyan Zhu, Minlie Huang
| Challenge: | Dialogue safety problems severely limit the real-world deployment of generative conversational models. |
| Approach: | They propose a taxonomy for dialogue safety specifically designed to capture unsafe behaviors in human-bot dialogue settings. |
| Outcome: | The proposed taxonomy captures unsafe behaviors in human-bot dialogue settings with rich context-sensitive unsafe examples. |
Reason from Future: Reverse Thought Chain Enhances LLM Reasoning (2025.findings-acl)
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Yinlong Xu, Yanzhao Zheng, Shuoshuo Sun, Shuaihan Huang, Baohua Dong, Zhu Hangcheng, Ruohui Huang, Gang Yu, Hongxia Xu, Jian Wu
| Challenge: | Existing reasoning paradigms that focus on local optimum reasoning lack global perspective. |
| Approach: | They propose a bidirectional reasoning paradigm that generates reasoning paths by bidirectional planning and bottom-up reasoning accumulation. |
| Outcome: | The proposed reasoning paradigm outperforms conventional paradigms with higher accuracy and less searching space to solve complex tasks. |
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation (2022.lrec-1)
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Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen
| Challenge: | Recent research on domain adaptation neglects diversity in translation within a domain . current research on NMT models considers very broad target domains . |
| Approach: | They propose a fine-grained domain adaptation task for autonomous vehicles, AI education, real-time networks, and smart phone. |
| Outcome: | The proposed task is compared with a dataset of Chinese-English translation tasks for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone. |
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. |
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. |
What are the Generator Preferences for End-to-end Task-Oriented Dialog System? (2024.emnlp-main)
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| Challenge: | Existing methods to improve the accuracy of entity retrieval are not effective. |
| Approach: | They propose a framework that improves the performance of task-oriented dialogue systems by obtaining fine-grained matching information between contexts and entities and extracting the entity attribute shift matrix as preference signals. |
| Outcome: | The proposed framework outperforms existing methods and improves the quality of the dialogue. |
MMErroR: A Benchmark for Erroneous Reasoning in Vision-Language Models (2026.acl-long)
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Yang Shi, Yifeng Xie, Minzhe Guo, Liangsi Lu, Mingxuan Huang, Jingchao Wang, Zhihong Zhu, Boyan Xu, Zhiqi Huang
| Challenge: | Recent advances in vision-language models have improved performance in multi-modal learning. |
| Approach: | They propose a multi-modal benchmark that embeds a single coherent reasoning error in 1997 samples. |
| Outcome: | The proposed benchmark is based on a set of 1997 samples embedding a single coherent reasoning error. |
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. |
PRiSM: Benchmarking Phone Realization in Speech Models (2026.acl-long)
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Shikhar Bharadwaj, Chin-Jou Li, Yoonjae Kim, Kwanghee Choi, Eunjung Yeo, Ryan Soh-Eun Shim, Hanyu Zhou, Brendon Boldt, Karen Rosero, Kalvin Chang, Darsh Agrawal, Keer Xu, Chao-Han Huck Yang, Jian Zhu, Shinji Watanabe, David R. Mortensen
| Challenge: | Existing evaluations of phone recognition systems only measure surface-level transcription accuracy. |
| Approach: | They propose to standardize transcription-based evaluation and assess downstream utility in clinical, educational, and multilingual settings with transcription and representation probes. |
| Outcome: | The proposed system outperforms LALMs in clinical, educational, and multilingual settings. |
Boosting Policy and Process Reward Models with Monte Carlo Tree Search in Open-Domain QA (2025.findings-acl)
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Chi-Min Chan, Chunpu Xu, Junqi Zhu, Jiaming Ji, Donghai Hong, Pengcheng Wen, Chunyang Jiang, Zhen Ye, Yaodong Yang, Wei Xue, Sirui Han, Yike Guo
| Challenge: | Experimental results show that our approach can effectively improve the performance of both the policy model and the reward model. |
| Approach: | They propose to use Monte Carlo Tree Search for both policy model improvement and reward model improvement to bridge it to more subtle open-domain question answering. |
| Outcome: | The proposed approach surpasses existing methods for annotation and training data with fewer data points and achieves better performance in test-time scaling strategies. |
Long and Diverse Text Generation with Planning-based Hierarchical Variational Model (D19-1)
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| Challenge: | Existing methods for data-to-text generation are insufficient to produce long and diverse texts. |
| Approach: | They propose a planning-based hierarchical variational model that plans a sequence of groups and then realizes each sentence conditioned on the planning result and the previously generated context. |
| Outcome: | The proposed model outperforms state-of-the-art models in long and diverse text generation. |
Concise and Precise Context Compression for Tool-Using Language Models (2024.findings-acl)
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Yang Xu, Yunlong Feng, Honglin Mu, Yutai Hou, Yitong Li, Xinghao Wang, Wanjun Zhong, Zhongyang Li, Dandan Tu, Qingfu Zhu, Min Zhang, Wanxiang Che
| Challenge: | Existing methods suffer from key information loss and difficulty in adjusting the length of compressed sequences based on documentation lengths. |
| Approach: | They propose two strategies for compressing tool documentation into concise and precise summary sequences for tool-using language models. |
| Outcome: | The proposed approach achieves comparable performance to the upper-bound baseline under 16x compression ratio. |
OpenToM: A Comprehensive Benchmark for Evaluating Theory-of-Mind Reasoning Capabilities of Large Language Models (2024.acl-long)
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| Challenge: | Existing N-ToM benchmarks lack ambiguous and artificial narratives, lack of personality traits and preferences, and limited diversity in the questions posed. |
| Approach: | They propose a benchmark to assess Neural Theory-of-Mind (N-ToM) with longer and clearer narrative stories, characters with explicit personality traits, actions triggered by character intentions, and questions designed to challenge LLMs’ abilities of modeling characters’ mental states. |
| Outcome: | The proposed test aims to assess the performance of LLMs in the physical and psychological worlds. |
CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments (2026.acl-long)
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Haotian Xu, Yue Hu, Zhengqiu Zhu, Chen Gao, Ziyou Wang, Junreng Rao, Wenhao Lu, Weishi Li, Quanjun Yin, Yong Li
| Challenge: | Existing benchmarks focus on indoor or street settings, overlooking challenges of open-ended urban spaces. |
| Approach: | They propose a benchmark to probe cross-view spatial reasoning capabilities of current VLMs in urban settings. |
| Outcome: | The citycube benchmark examines the performance of current vision-language models in urban environments. |
YuLan-Mini: Pushing the Limits of Open Data-efficient Language Model (2025.acl-long)
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Hu Yiwen, Huatong Song, Jie Chen, Jia Deng, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Zican Dong, Yang Lu, Xu Miao, Xin Zhao, Ji-Rong Wen
| Challenge: | prevailing pre-training approaches for large language models involve several complexities. |
| Approach: | They propose a low-cost training recipe and a robust optimization approach to mitigate training instability . they also propose synthesis, curriculum, and data selection pipelines to integrate data . |
| Outcome: | The proposed model achieves top-tier performance among models with similar parameter scale . it is comparable to industry-leading models that require significantly more data . |
Stacked Acoustic-and-Textual Encoding: Integrating the Pre-trained Models into Speech Translation Encoders (2021.acl-long)
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| Challenge: | End-to-end Speech Translation (E2E ST) encoders lack global context representation, whereas MT encoder lacks it. |
| Approach: | They propose a Stacked Acoustic-and-Textual Encoding method for speech translation . they propose an adaptor module to alleviate representation inconsistency . |
| Outcome: | The proposed method achieves state-of-the-art BLEU scores of 18.3 and 25.2 on two ST tasks. |
Can Large Language Models Understand You Better? An MBTI Personality Detection Dataset Aligned with Population Traits (2025.coling-main)
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Bohan Li, Jiannan Guan, Longxu Dou, Yunlong Feng, Dingzirui Wang, Yang Xu, Enbo Wang, Qiguang Chen, Bichen Wang, Xiao Xu, Yimeng Zhang, Libo Qin, Yanyan Zhao, Qingfu Zhu, Wanxiang Che
| Challenge: | Existing data on MBTI personality detection are based on self-reported labels and fail to capture the full range of population personality traits. |
| Approach: | They construct a manually annotated MBTI personality detection dataset with soft labels under the guidance of psychologists and use them to identify the task. |
| Outcome: | The MBTIBench is the first manually annotated MBti personality detection dataset with soft labels under the guidance of psychologists. |
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. |
DiffS2UT: A Semantic Preserving Diffusion Model for Textless Direct Speech-to-Speech Translation (2023.emnlp-main)
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| Challenge: | Existing models for speech generation are not efficient due to low information density of speech data. |
| Approach: | They propose a method to integrate discrete diffusion models into speech generation tasks . they propose to apply diffusion forward process while employing diffusion backward process . |
| Outcome: | The proposed model achieves comparable results to the auto-regressive baselines with significantly fewer decoding steps (50 steps). |
Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer (2024.acl-long)
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| Challenge: | Experimental results indicate that GPST significantly outperforms the existing speech language models in terms of word error rate, speech quality, and speaker similarity. |
| Approach: | They propose a hierarchical transformer that quantizes audio waveforms into two distinct types of discrete speech representations and integrates them within a transformer architecture. |
| Outcome: | The proposed model outperforms existing speech language models in word error rate, speech quality, and speaker similarity. |
Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning (2025.acl-long)
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Yongxin Xu, Ruizhe Zhang, Xinke Jiang, Yujie Feng, Yuzhen Xiao, Xinyu Ma, Runchuan Zhu, Xu Chu, Junfeng Zhao, Yasha Wang
| Challenge: | Existing methods for integrating internal and external knowledge lack effective control mechanisms for generating hallucinations and dealing with outdated knowledge. |
| Approach: | They propose a framework that decouples, identifies, and purposefully optimizes parameter subspaces related to adherence and robustness. |
| Outcome: | The proposed framework decouples, identifies, and purposefully optimizes parameter subspaces related to adherence and robustness. |
Huatuo-26M, a Large-scale Chinese Medical QA Dataset (2025.findings-naacl)
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Xidong Wang, Jianquan Li, Shunian Chen, Yuxuan Zhu, Xiangbo Wu, Zhiyi Zhang, Xiaolong Xu, Junying Chen, Jie Fu, Xiang Wan, Anningzhe Gao, Benyou Wang
| Challenge: | Large Language Models are a powerful tool for medical research, but the data is a bottleneck. |
| Approach: | They propose to use the largest ever medical Question Answering dataset with 26 Million QA pairs as a fine-tuning data for training large language models. |
| Outcome: | The proposed dataset demonstrates that it can be used to train large language models and improves zero-shot performance on other datasets. |
RQT: Hierarchical Residual Quantization for Multi-Model Compression (2025.findings-acl)
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| Challenge: | Existing methods for decomposing fine-tuned LLMs are sensitive to the magnitude of delta values. |
| Approach: | They propose a hierarchical quantization framework that shares low-bit integer weights across similar models. |
| Outcome: | The proposed framework achieves an average accuracy degradation of approximately 3% on fine-tuned models across mathematics, coding, chatbot, and Chinese LLMs. |
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. |
WildGraphBench: Benchmarking GraphRAG with Wild-Source Corpora (2026.findings-acl)
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| Challenge: | Existing benchmarks for Graph-based Retrieval-Augmented Generation (GraphRAG) rely on short, curated passages as external knowledge, failing to adequately evaluate systems in realistic settings involving long contexts and large-scale heterogeneous documents. |
| Approach: | They propose a benchmark to assess GraphRAG performance in the wild using Wikipedia's unique structure where cohesive narratives are grounded in long and heterogeneous external reference documents. |
| Outcome: | Experiments with articles across 12 top-level topics show that GraphRAG performs better in the wild than existing methods. |
HMoE: Heterogeneous Mixture of Experts for Language Modeling (2025.emnlp-main)
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An Wang, Xingwu Sun, Ruobing Xie, Shuaipeng Li, Jiaqi Zhu, Zhen Yang, Pinxue Zhao, Weidong Han, Zhanhui Kang, Di Wang, Naoaki Okazaki, Cheng-zhong Xu
| Challenge: | Mixture of Experts (MoE) models use homogeneous experts with diverse capacities, resulting in a lack of expert specialization and parameter utilization. |
| Approach: | They propose a framework where experts differ in size and possess diverse capacities . they propose HMoE to encourage frequent activation of smaller experts . |
| Outcome: | The proposed framework outperforms homogeneous homogenous MoE models on evaluation benchmarks and achieves lower loss rate with fewer activated parameters. |
Self-Constructed Context Decompilation with Fined-grained Alignment Enhancement (2024.findings-emnlp)
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| Challenge: | Decompilation is the process of converting compiled code back into a high-level programming language for analysis when source code is unavailable. |
| Approach: | They propose two methods to improve decompilation performance without fine-tuning and fine-grained alignment enhancement to achieve further improvements. |
| Outcome: | The proposed methods achieved a Re-Executability performance improvement of approximately 3.90% on the Decompile-Eval benchmark, establishing a new state-of-the-art performance of 52.41%. |
Hierarchical Information Matters: Text Classification via Tree Based Graph Neural Network (2022.coling-1)
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| Challenge: | Text classification is a primary task in natural language processing (NLP). |
| Approach: | They propose a graph neural network (HINT) that makes full use of hierarchical information contained in the text for the task of text classification. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on popular benchmarks while having a simple structure and few parameters. |
From Real to Synthetic: Synthesizing Millions of Diversified and Complicated User Instructions with Attributed Grounding (2025.acl-long)
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| Challenge: | a pursuit of diverse, complex, and large-scale instruction data is crucial for automatically aligning large language models . authors: methods that generate synthetic instructions at scale suffer from limited grounding sources . attributed grounding is a technique that can be used to align language models with human . |
| Approach: | They synthesize 1 million instructions using attributed grounding and a bottom-up synthesis process that leverages web documents to generate a situation, then a meaningful instruction. |
| Outcome: | The proposed framework achieves leading performance on benchmarks and scales with more web corpora. |
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. |
Reinforcement Learning for Large Language Models via Group Preference Reward Shaping (2025.emnlp-main)
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Huaisheng Zhu, Siyuan Xu, Hangfan Zhang, Teng Xiao, Zhimeng Guo, Shijie Zhou, Shuyue Hu, Vasant G. Honavar
| Challenge: | Existing methods for fine-tuning Large Language Models (LLMs) are expensive and sensitive to reward model quality. |
| Approach: | They propose a method that leverages preference-based comparisons rather than precise numerical rewards. |
| Outcome: | Experiments show that GPRS outperforms critic-model-free RL algorithms on RLHF and reasoning tasks. |
Cross-Document Event Coreference Resolution on Discourse Structure (2023.emnlp-main)
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| Challenge: | Experimental results show that our proposed model outperforms several baselines and achieves the competitive performance with the start-of-the-art baselines. |
| Approach: | They propose to use discourse rhetorical structure constructor to construct tree structures to represent documents and a multi-layer perceptron to capture similarities of event mention pairs. |
| Outcome: | The proposed model outperforms baselines and achieves competitive performance with the start-of-the-art baselines. |
A Contrastive Framework for Learning Sentence Representations from Pairwise and Triple-wise Perspective in Angular Space (2022.acl-long)
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| Challenge: | Existing methods for learning sentence representations focus on constitution of positive and negative representation pairs and do not focus on training objective. |
| Approach: | They propose a new method to learn sentence representations using BERT-like pre-trained models . they use a pairwise discriminating power and a model to model the entailment relation of triplet sentences . |
| Outcome: | The proposed method outperforms the previous state-of-the-art on diverse sentence related tasks. |
FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents (2026.acl-long)
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| Challenge: | Long trajectories in deep research often exceed model context limits, compressing token budgets for both evidence collection and report writing. |
| Approach: | They propose a file-system-based framework that scales deep research beyond context window . a Context Builder agent acts as a librarian and a Report Writer agent composes the final report . |
| Outcome: | Experiments on two open-ended benchmarks show that FS-Researcher achieves state-of-the-art report quality across different backbone models. |
A Hierarchical Network for Abstractive Meeting Summarization with Cross-Domain Pretraining (2020.findings-emnlp)
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| Challenge: | Existing methods of summarizing meetings require complex multi-step pipelines that are intractable. |
| Approach: | They propose an abstractive summary network that adapts to meeting transcripts by hierarchical structure and role vectors. |
| Outcome: | The proposed model outperforms existing methods in both metrics and human evaluation. |
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. |
PSC: Extending Context Window of Large Language Models via Phase Shift Calibration (2024.emnlp-main)
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| Challenge: | Large-scale language models (LLMs) have shown impressive results across a variety of tasks. |
| Approach: | They propose a module for calibrating the frequencies predefined by existing methods . they conducted extensive experiments across multiple models and tasks . |
| Outcome: | The proposed method reduces perplexity as the context window size is varied from 16k to 32k and up to 64k. |
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 . |
RoT: Enhancing Table Reasoning with Iterative Row-Wise Traversals (2025.emnlp-main)
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| Challenge: | Recent advances in reasoning large language models (RLLMs) have significantly enhanced reasoning capabilities, leading to brilliant performance on table reasoning. |
| Approach: | They propose a method which performs iterative row-wise table traversal, allowing for reasoning extension and reflection-based refinement at each traversal. |
| Outcome: | Experiments show that the proposed method outperforms RLLMs on WikiTableQuestions and TableBench by 4.3% and achieves state-of-the-art results with comparable models. |
MTR-DuplexBench: Towards a Comprehensive Evaluation of Multi-Round Conversations for Full-Duplex Speech Language Models (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on evaluating single-round interactions, neglecting other critical aspects. |
| Approach: | They propose a benchmark to evaluate full-duplex speech language models in multi-round settings . they segment continuous full-dual dialogues into discrete turns for evaluation . |
| Outcome: | The proposed benchmark compared full-duplex speech language models with full-dual speech models . the results show that the models perform better in multi-round settings than standard models compared to benchmarks . |
Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning (2026.findings-acl)
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| Challenge: | Existing unified optimization strategies overlook the statistical conflict between these distinct gradient signals. |
| Approach: | They propose a framework to reduce bias-variance trade-offs in Large Language Models . they propose DYPO, which leverages intrinsic group dynamics to significantly reduce RL gradient variance . |
| Outcome: | The proposed framework outperforms traditional pipelines on reasoning benchmarks and out-of-distribution tasks. |
EmRel: Joint Representation of Entities and Embedded Relations for Multi-triple Extraction (2022.naacl-main)
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| Challenge: | Existing studies only explore entity representations, but propose a novel triple perspective for relation extraction. |
| Approach: | They propose to explicitly introduce relation representation and jointly represent it with entities to identify valid triples. |
| Outcome: | The proposed method is based on ablations and document-level relation extraction and joint entity and relation extraction. |
DocEE: A Large-Scale and Fine-grained Benchmark for Document-level Event Extraction (2022.naacl-main)
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| Challenge: | Existing datasets focus on sentence-level event extraction, but document-level EE is limited due to the lack of large-scale and practical training and evaluation datasets. |
| Approach: | They propose a document-level event extraction dataset with 27,000+ events and 180,000+ arguments. |
| Outcome: | The proposed dataset includes 27,000+ events, 180,000+ arguments and large-scale manual annotations, fine-grained argument types and application-oriented settings. |
Knowledge-Augmented Methods for Natural Language Processing (2022.acl-tutorials)
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| Challenge: | Knowledge in natural language processing (NLP) is a rising trend especially after the advent of large scale pre-trained models. |
| Approach: | This tutorial introduces the key steps in integrating knowledge into natural language processing (NLP) it introduces knowledge grounding from text, knowledge representation and fusing. |
| Outcome: | This tutorial introduces the key steps in integrating knowledge into natural language processing including knowledge grounding from text, knowledge representation and fusing. |
Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese (P19-1)
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| Challenge: | Currently, most studies on implicit discourse relation recognition use sentence-level representations . Chinese is a paratactic language that tends to pro-drop clause connectives . |
| Approach: | They propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. |
| Outcome: | The proposed model outperforms state-of-the-art models in micro and macro F1 scores on a Chinese discourse corpus. |
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. |
Mixed-Lingual Pre-training for Cross-lingual Summarization (2020.aacl-main)
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| Challenge: | Cross-lingual summarization (CLS) aims at producing a summary in the target language for an article in the source language. |
| Approach: | They propose a mixed-lingual pre-training scheme that leverages both cross-lingual tasks such as translation and monolingual tasks like masked language models. |
| Outcome: | The proposed model improves on the translation and masked language models with no task-specific components and saves memory. |
Q-Mamba: Towards more efficient Mamba models via post-training quantization (2025.findings-acl)
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| Challenge: | Existing studies show that Mamba architectures have room for further optimization in linear projections and state caches. |
| Approach: | They propose a decoupled scale quantization scheme to mitigate outliers in states and channels by applying separate quantization scales. |
| Outcome: | The proposed method reduces memory consumption by 50% across various quantization settings, model sizes, and generation and zero-shot tasks. |
Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)
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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen
| Challenge: | Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. |
| Approach: | They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
| Outcome: | The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering (2021.acl-long)
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| Challenge: | Existing generative models for open-domain question answering focus on generating direct answers from unstructured textual information, but a large amount of knowledge is stored in structured databases, and need to be accessed using query languages such as SQL. |
| Approach: | They propose a hybrid framework that takes both textual and tabular evidences as input and generates either direct answers or SQL queries depending on which form could better answer the question. |
| Outcome: | The proposed framework outperforms baseline models on OpenSQuAD datasets and can generate SQL queries on the associated databases to obtain the final answers. |
DCIS: Efficient Length Extrapolation of LLMs via Divide-and-Conquer Scaling Factor Search (2025.emnlp-main)
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| Challenge: | Existing frameworks for large language models with context length limitations are suboptimal for initialization and fine-tuning. |
| Approach: | They propose a RoPE-based fine-tuning framework that strategically determines the best scaling factors for LLMs by a Divide-and-Conquer Incremental Search algorithm. |
| Outcome: | The proposed framework mitigates performance decay at extended target lengths and can perform effectively without fine-tuning. |
INK: Injecting kNN Knowledge in Nearest Neighbor Machine Translation (2023.acl-long)
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| Challenge: | Neural machine translation models induce a non-smooth representation space, which harms its generalization results. |
| Approach: | They propose a framework to smooth the representation space by adjusting neighbor representations with a small number of new parameters. |
| Outcome: | The proposed framework outperforms the state-of-the-art kNN-MT system with average gains of 1.99 COMET and 1.0 BLEU on four benchmark datasets. |
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. |
Leveraging Knowledge in Multilingual Commonsense Reasoning (2022.findings-acl)
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| Challenge: | Commonsense reasoning is a language-agnostic process, but most comprehensive knowledge sources are limited to a small number of languages, especially English. |
| Approach: | They propose to use English as a pivot language to integrate commonsense reasoning into models using a translate-retrieve-translate strategy. |
| Outcome: | The proposed model outperforms the state-of-the-art on the XCSR benchmarks. |
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. |
Chart2Code53: A Large-Scale Diverse and Complex Dataset for Enhancing Chart-to-Code Generation (2025.emnlp-main)
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Tianhao Niu, Yiming Cui, Baoxin Wang, Xiao Xu, Xin Yao, Qingfu Zhu, Dayong Wu, Shijin Wang, Wanxiang Che
| Challenge: | Existing Chart2code-related training datasets suffer from limited scale, limited type coverage, and inadequate complexity. |
| Approach: | They propose to synthesize chart2code-related training datasets using web plotting code and chart images to address these challenges. |
| Outcome: | The proposed dataset exhibits the greatest diversity and higher complexity compared to other open-source Chart2code related datasets. |
Relevance Is a Guiding Light: Relevance-aware Adaptive Learning for End-to-end Task-oriented Dialogue System (2024.emnlp-main)
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| Challenge: | Existing approaches to training task-oriented dialogue systems struggle with the Distractive Attributes Problem (DAP) Existing methods struggle to deal with false but similar knowledge (hard negative entities) |
| Approach: | They propose a two-stage training framework that eliminates hard negatives step-by-step and aligns retrieval with generation. |
| Outcome: | The proposed method eliminates hard negatives step-by-step and aligns retrieval with generation. |
WPO: Enhancing RLHF with Weighted Preference Optimization (2024.emnlp-main)
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Wenxuan Zhou, Ravi Agrawal, Shujian Zhang, Sathish Reddy Indurthi, Sanqiang Zhao, Kaiqiang Song, Silei Xu, Chenguang Zhu
| Challenge: | Off-policy preference optimization suffers from a distributional gap between the policy used for data collection and the target policy, leading to suboptimal optimization. |
| Approach: | They propose a method to simulate on-policy learning with off-police preference data. |
| Outcome: | The proposed method outperforms Direct Preference Optimization (DPO) by up to 5.6% on Alpaca Eval 2 and MT-bench. |
VeraCT Scan: Retrieval-Augmented Fake News Detection with Justifiable Reasoning (2024.acl-demos)
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Cheng Niu, Yang Guan, Yuanhao Wu, Juno Zhu, Juntong Song, Randy Zhong, Kaihua Zhu, Siliang Xu, Shizhe Diao, Tong Zhang
| Challenge: | generative artificial intelligence has exacerbated the challenge of distinguishing genuine news from fabricated stories. |
| Approach: | They propose a retrieval-augmented system that extracts the core facts from a given piece of news and conducts an internet-wide search to identify corroborating or conflicting reports. |
| Outcome: | The proposed system has demonstrated state-of-the-art accuracy in the realm of fake news detection. |
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. |
SCITAT: A Question Answering Benchmark for Scientific Tables and Text Covering Diverse Reasoning Types (2025.findings-acl)
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Xuanliang Zhang, Dingzirui Wang, Baoxin Wang, Longxu Dou, Xinyuan Lu, Keyan Xu, Dayong Wu, Qingfu Zhu
| Challenge: | Existing scientific question answering datasets lack diverse reasoning types and neglect relevance between tables and text. |
| Approach: | They propose a scientific question answering benchmark for scientific tables and text with diverse reasoning types (SCITAT) to address these challenges, they propose QA benchmark which incorporates tables and texts to ensure that the questions encompass both tables and textes. |
| Outcome: | The proposed benchmark improves by 4.1% over baselines on SCITAT. |
Hierarchical Prompting Assists Large Language Model on Web Navigation (2023.findings-emnlp)
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| Challenge: | Large language models struggle on processing complicated observations in interactive decision making tasks. |
| Approach: | They propose a hierarchical prompting approach that constructs an action-aware observation and a Summarizer prompt. |
| Outcome: | The proposed method outperforms the current state-of-the-art prompting mechanism by 6.2% on task success rate. |
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 . |
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. |
ExtRA: Extracting Prominent Review Aspects from Customer Feedback (D18-1)
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| Challenge: | Existing methods for analyzing and summarizing customer reviews are based on a number of prominent review aspects. |
| Approach: | They propose a framework for extracting the most prominent aspects of a given product type from textual reviews. |
| Outcome: | The proposed framework extracts K most prominent aspect terms which do not overlap semantically without supervision. |
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. |
To Code or not to Code? Adaptive Tool Integration for Math Language Models via Expectation-Maximization (2025.findings-acl)
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| Challenge: | Existing tools that integrate chain-of-thought reasoning and code execution lack metacognitive awareness to integrate tools. |
| Approach: | They propose a framework that synergizes structured exploration with off-policy RL optimization to create a cycle between metacognitive tool-use decisions and evolving capabilities. |
| Outcome: | The proposed framework improves over 11% on MATH500 and 9.4% on AIME without o1-like CoT. |
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. |
CH-SIMS: A Chinese Multimodal Sentiment Analysis Dataset with Fine-grained Annotation of Modality (2020.acl-main)
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| Challenge: | Existing studies in multimodal sentiment analysis only use unified multimodal annotations, which do not reflect the independent sentiment of single modalities. |
| Approach: | They propose a Chinese single- and multi-modal sentiment analysis dataset with multimodal and independent unimodal annotations that can be used to study the interaction between modalities. |
| Outcome: | The proposed methods achieve state-of-the-art performance and learn more distinctive unimodal representations. |
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. |
KNN-Instruct: Automatic Instruction Construction with K Nearest Neighbor Deduction (2024.emnlp-main)
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| Challenge: | Existing methods for generating synthetic instructions for large language models suffer from stale distribution and scalability. |
| Approach: | They propose a method which incorporates KNN deduction to produce meaningful new instructions by summarizing and learning from existing ones. |
| Outcome: | The proposed method outperforms all 7B models on the LMSYS leaderboard. |
Dict-BERT: Enhancing Language Model Pre-training with Dictionary (2022.findings-acl)
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Wenhao Yu, Chenguang Zhu, Yuwei Fang, Donghan Yu, Shuohang Wang, Yichong Xu, Michael Zeng, Meng Jiang
| Challenge: | Pre-trained language models (PLMs) capture word semantics in different contexts, hence the embeddings of rare words on the tail are poorly optimized. |
| Approach: | They propose to leverage definitions of rare words in dictionaries to enhance language model pre-training by leveraging dictionary definitions. |
| Outcome: | The proposed model improves understanding of rare words and boosts performance on various NLP downstream tasks. |
Data Augmentation with Adversarial Training for Cross-Lingual NLI (2021.acl-long)
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| Challenge: | Existing approaches to train cross-lingual models with labeled data are subpar, resulting in subpar results. |
| Approach: | They propose a data augmentation strategy that enriches data to reflect more diversity in a semantically faithful way and leverages adversarial training regimens to achieve greater robustness. |
| Outcome: | The proposed approach improves cross-lingual inference by leveraging the data to reflect more diversity in a semantically faithful way. |
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer (2020.coling-main)
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| Challenge: | Existing methods for unsupervised text style transfer lack parallel data and difficulties in content preservation. |
| Approach: | They propose a neural approach to unsupervised text style transfer using non-parallel data. |
| Outcome: | The proposed approach can be trained end-to-end on two widely-used public datasets. |
A Simple and Effective Approach to Coverage-Aware Neural Machine Translation (P18-2)
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| Challenge: | Neural Machine Translation (NMT) models are used to solve translation problems using long-term models. |
| Approach: | They propose a method to seek a better balance between model confidence and length preference for Neural Machine Translation. |
| Outcome: | The proposed model improves on Chinese-English and English-German translation 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. |
SConU: Selective Conformal Uncertainty in Large Language Models (2025.acl-long)
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| Challenge: | Existing frameworks fail to identify outliers that violate the exchangeability assumption, leading to unbounded miscoverage rates and unactionable prediction sets. |
| Approach: | They propose a method that implements significance tests to determine whether a given sample deviates from the uncertainty distribution of the calibration set. |
| Outcome: | The proposed approach facilitates rigorous management of miscoverage rates across single-domain and interdisciplinary contexts, and enhances the efficiency of predictions. |
Graph-guided Cross-composition Feature Disentanglement for Compositional Zero-shot Learning (2025.findings-acl)
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Yuxia Geng, Runkai Zhu, Jiaoyan Chen, Jintai Chen, Xiang Chen, Zhuo Chen, Shuofei Qiao, Yuxiang Wang, Xiaoliang Xu, Sheng-Jun Huang
| Challenge: | Disentanglement of visual features of primitives (i.e., attributes and objects) has shown exceptional results in Compositional Zero-shot Learning (CZSL). |
| Approach: | They propose a solution that takes multiple compositions as inputs and constrains disentangled primitive features to be general across compositions. |
| Outcome: | The proposed architecture significantly improves performance on three popular CZSL benchmarks and has been verified by solid ablation studies. |
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability (2025.findings-acl)
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| Challenge: | Existing studies have shown that training language models with rationales augmentation is beneficial, but this view does not hold consistently. |
| Approach: | They conduct comprehensive investigations to thoroughly inspect the impact of rationales on model performance and a novel perspective of model reliability. |
| Outcome: | The proposed method outperforms untrained models in several areas and provides informative regulations on the broad utilization of rationales. |
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. |
Improving AMR Parsing with Sequence-to-Sequence Pre-training (2020.emnlp-main)
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| Challenge: | Abstract meaning representation (AMR) parsing is limited by the size of curated datasets. |
| Approach: | They propose a seq2seq pre-training approach to build pre-trained models on three relevant tasks. |
| Outcome: | The proposed model improves performance on three relevant tasks while maintaining the response of pre-trained models. |
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. |
LegalReasoner: Step-wised Verification-Correction for Legal Judgment Reasoning (2025.acl-long)
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Weijie Shi, Han Zhu, Jiaming Ji, Mengze Li, Jipeng Zhang, Ruiyuan Zhang, Jia Zhu, Jiajie Xu, Sirui Han, Yike Guo
| Challenge: | Existing legal judgment prediction methods struggle with logical errors when conducting complex legal reasoning. |
| Approach: | They propose a method which enhances LJP reliability through step-wise verification and correction of the reasoning process. |
| Outcome: | The proposed model significantly improves concordance with court decisions from 72.37 to 80.27 on LLAMA-3.1-70B. |
Empowering Diffusion Models on the Embedding Space for Text Generation (2024.naacl-long)
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| Challenge: | Recent work adapts diffusion models to textual data by diffusing on the embedding space. |
| Approach: | They propose an embedding diffusion model based on Transformer to solve the problem of embeddable space and denoising model. |
| Outcome: | The proposed model is more efficient than previous methods on seminal text generation tasks and is superior to existing models. |
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. |
On the Calibration of Large Language Models and Alignment (2023.findings-emnlp)
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| Challenge: | Large language models are becoming more popular and are proving to be reliable . however, their reliability is often understudied due to their uncertainty and complex structure . |
| Approach: | They conduct a systematic examination of the calibration of aligned language models throughout the entire construction process including pretraining and alignment training. |
| Outcome: | The results shed light on whether popular large language models are well-calibrated and how the training process influences model calibration. |
MultiDx: A Multi-Source Knowledge Integration Framework towards Diagnostic Reasoning (2026.findings-acl)
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Yimin Deng, Zhenxi Lin, Yejing Wang, Guoshuai Zhao, Pengyue Jia, Zichuan Fu, Derong Xu, Yefeng Zheng, Xiangyu Zhao, Li Zhu, Xian Wu, Xueming Qian
| Challenge: | Existing approaches focus on diagnostic reasoning based on internal model knowledge or static knowledge bases. |
| Approach: | They propose a two-stage diagnostic reasoning framework that integrates multi-perspective evidence to generate a diagnostic prediction. |
| Outcome: | The proposed method generates suspected diagnoses and reasoning traces from web search, SOAP-formatted case, and clinical case database. |
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. |
Copyright Detective: A Forensic System to Evidence LLMs Flickering Copyright Leakage Risks (2026.acl-demo)
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Guangwei Zhang, Jianing Zhu, Cheng Qian, Neil Zhenqiang Gong, Rada Mihalcea, Zhaozhuo Xu, Jingrui He, Jiaqi W. Ma, Chaowei Xiao, Bo Li, Ahmed Abbasi, Dongwon Lee, Heng Ji, Denghui Zhang
| Challenge: | **Copyright Detective** is the first interactive forensic system for detecting, analyzing, and visualizing potential copyright risks in LLM outputs. |
| Approach: | They propose a system that detects copyright infringements and visualizes them . they use content recall testing, paraphrase-level similarity analysis and persuasive jailbreak probing . |
| Outcome: | The proposed system detects, analyzes, and visualizes potential copyright risks in LLM outputs. |
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. |
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering (2025.emnlp-main)
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| Challenge: | Chart question answering (CQA) is a multimodal task for evaluating the reasoning capabilities of vision-language models. |
| Approach: | They propose a chart question answering benchmark that incorporates multilingual contexts and supports open-domain textual outputs. |
| Outcome: | The proposed framework outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought. |
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. |
Narrate Dialogues for Better Summarization (2022.findings-emnlp)
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| Challenge: | Recent work on dialogue summarization models focuses on generating concise summaries for multi-party dialogues. |
| Approach: | They propose several ways to convert dialogue into a third-person narrative style . they propose to use narration as a valuable annotation for LLMs . |
| Outcome: | Empirical results show that the proposed approach achieves higher scores on ROUGE and a factual correctness metric. |
Visualize Before You Write: Imagination-Guided Open-Ended Text Generation (2023.findings-eacl)
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| Challenge: | Existing tools for text-to-image synthesis can visualize machine imaginations for a given context. |
| Approach: | They propose a framework that uses machine-generated images to guide language models in open-ended text generation. |
| Outcome: | The proposed framework is effective on open-ended text generation tasks while showing minor degeneration. |
MolSafeEval: A Benchmark for Uncovering Safety Risks in AI-Generated Molecules (2026.findings-acl)
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| Challenge: | generative models may produce molecules with toxic, reactive, or otherwise hazardous characteristics. |
| Approach: | They propose a benchmark to evaluate and analyze the safety risks of molecular generation. |
| Outcome: | The proposed benchmark aims to evaluate and analyze the safety risks of molecular generation. |
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. |
DioR: Adaptive Cognitive Detection and Contextual Retrieval Optimization for Dynamic Retrieval-Augmented Generation (2025.acl-long)
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| Challenge: | Existing methods for generating large language models face limitations in key aspects such as retrieval triggers and contextual scrutiny of retrieval content. |
| Approach: | They propose a dynamic RAG method that uses cognitive detection and contextual retrieval optimization to determine when retrieval is needed and what to retrieve for LLMs. |
| Outcome: | The proposed method achieves superior performance on all tasks, demonstrating the effectiveness of the proposed method. |
Improving Factual Consistency in Abstractive Summarization with Sentence Structure Pruning (2024.lrec-main)
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Dingxin Hu, Xuanyu Zhang, Xingyue Zhang, Yiyang Li, Dongsheng Chen, Marina Litvak, Natalia Vanetik, Qing Yang, Dongliang Xu, Yanquan Zhou, Lei Li, Yuze Li, Yingqi Zhu
| Challenge: | Abstractive summarization models suffer from factual inconsistency problem . post-editing methods focus on replacing suspicious entities, failing to modify incorrect content hidden in sentence structures. |
| Approach: | They propose to use sentence pruning operation to correct possible errors . they propose to apply sentence pruning operations to the syntactic dependency tree . |
| Outcome: | The proposed method improves factual consistency on the FRANK dataset compared with baselines . it is model-independent and can serve as the final step in ensuring factual consistentness. |
Enhancing Factual Consistency of Abstractive Summarization (2021.naacl-main)
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| Challenge: | Abstractive summarization models often distort or fabricate facts in articles . factual inconsistency is a common problem with abstractive summaries . |
| Approach: | They propose a fact-aware summarization model FASum to extract factual relations into the summary generation process via graph attention. |
| Outcome: | The proposed model can produce abstractive summaries with higher factual consistency compared with existing systems and corrects factual errors via modifying only a few keywords. |
Taming Language Models for Text-attributed Graph Learning with Decoupled Aggregation (2025.acl-long)
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| Challenge: | Existing approaches to learning text-attributed graphs neglect interaction between textual and structural information. |
| Approach: | They propose a framework that integrates textual and structural information into TAG learning . they propose combining semantic aggregation and structural aggregations to improve learning a . |
| Outcome: | The proposed framework outperforms state-of-the-art learning methods while requiring less resources. |
WenetSpeech-Wu: Datasets, Benchmarks, and Models for a Unified Chinese Wu Dialect Speech Processing Ecosystem (2026.findings-acl)
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Chengyou Wang, Mingchen Shao, Jingbin Hu, Zeyu Zhu, Hongfei Xue, Bingshen Mu, Xin Xu, Xingyi Duan, Binbin Zhang, Zhu Pengcheng, Chuang Ding, Xiaojun Zhang, Hui Bu, Lei Xie
| Challenge: | despite its linguistic significance, the Wu dialect of Chinese has long been hindered by the lack of large-scale speech data, standardized evaluation benchmarks, and publicly available models. |
| Approach: | They propose to use WenetSpeech-Wu as a large-scale, multi-dimensionally annotated open-source speech corpus for the Wu dialect of Chinese. |
| Outcome: | The proposed dataset includes 8,000 hours of speech data and strong open-source models . the proposed dataset is competitive and empirically validated . |
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. |
SymbolicThought: Integrating Language Models and Symbolic Reasoning for Consistent and Interpretable Human Relationship Understanding (2026.acl-demo)
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| Challenge: | Existing methods for constructing character relationships from plain text are time-consuming and low in coverage. |
| Approach: | They propose a human-in-the-loop framework that combines LLM-based extraction with symbolic reasoning. |
| Outcome: | The proposed framework improves annotation accuracy and consistency while significantly reducing time cost. |
Few-shot Temporal Pruning Accelerates Diffusion Models for Text Generation (2024.lrec-main)
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| Challenge: | Existing acceleration methods for text generation ignore the importance of the distribution of sampling steps, resulting in slow sampling rates. |
| Approach: | They propose a technique to accelerate diffusion models for text generation without additional training by using a Bayesian optimization approach. |
| Outcome: | The proposed technique achieves 400x acceleration even with minimal sampling steps after down to less than 1 minute of optimization yielding a competitive performance even with minimum sampling steps. |
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. |
TableEval: A Real-World Benchmark for Complex, Multilingual, and Multi-Structured Table Question Answering (2025.emnlp-main)
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| Challenge: | Existing TableQA benchmarks focus on simple flat tables and suffer from data leakage . current benchmarks are monolingual and fail to capture cross-lingual variability . |
| Approach: | They propose a table-based TableQA benchmark to evaluate LLMs on real-world tasks. |
| Outcome: | The proposed benchmarks show that they achieve high agreement with human judgment . the proposed framework improves on the alignment between model responses and reference answers . |
NiuTrans.LMT: Toward Inclusive and Scalable Multilingual Machine Translation with LLMs (2026.acl-long)
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Yingfeng Luo, Ziqiang Xu, Yuxuan Ouyang, MuRun Yang, DingYang Lin, Kaiyan Chang, Tong Zheng, Bei Li, Peinan Feng, Quan Du, Tong Xiao, JingBo Zhu
| Challenge: | Large language models have significantly advanced Multilingual Machine Translation (MMT) yet scaling to many languages while maintaining robust performance across directions remains challenging. |
| Approach: | They propose a strategy to reduce the number of translations in one direction . they propose auxiliary parallel sentences to promote cross-lingual transfer . |
| Outcome: | The proposed model performs on par with or better than substantially larger baselines. |
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. |
ConU: Conformal Uncertainty in Large Language Models with Correctness Coverage Guarantees (2024.findings-emnlp)
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Zhiyuan Wang, Jinhao Duan, Lu Cheng, Yue Zhang, Qingni Wang, Xiaoshuang Shi, Kaidi Xu, Heng Tao Shen, Xiaofeng Zhu
| Challenge: | Uncertainty quantification (UQ) in natural language generation tasks remains an open challenge . however, black-box uncertainty measures require investigating with the proliferation of LLMs served via APIs. |
| Approach: | They propose a conformal uncertainty measure and a method to transform heuristic uncertainty notions into rigorous prediction sets. |
| Outcome: | Empirical results show that the proposed method outperforms state-of-the-art methods and can provide reliable guarantees for open-ended NLG tasks. |
MedEthicEval: Evaluating Large Language Models Based on Chinese Medical Ethics (2025.naacl-industry)
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| Challenge: | Large language models (LLMs) have been used in clinical decision support, medical education and patient communication. |
| Approach: | They propose a benchmark to evaluate large language models in the domain of medical ethics and assess their grasp of medical ethical principles and their application across diverse scenarios. |
| Outcome: | The proposed framework assesses the models’ grasp of medical ethics principles and their ability to apply them across diverse scenarios. |
MRT: Multi-modal Short- and Long-range Temporal Convolutional Network for Time-sync Comment Video Behavior Prediction (2024.lrec-main)
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| Challenge: | Using time-sync comments, it is difficult to understand user behavior due to complexity of interactions between users, videos, and comments. |
| Approach: | They propose a novel time-sync comment behavior prediction model that takes historical behavior into account and optimizes it on the basis of user preferences. |
| Outcome: | The proposed model improves the performance of time-sync comments on visual frames and textual comments on two cats playing simultaneously. |
RAID: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors (2024.acl-long)
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Liam Dugan, Alyssa Hwang, Filip Trhlík, Andrew Zhu, Josh Magnus Ludan, Hainiu Xu, Daphne Ippolito, Chris Callison-Burch
| Challenge: | Existing methods for detecting machine-generated text are often insufficiently robust and lack benchmark datasets. |
| Approach: | They evaluate the out-of-domain and adversarial robustness of 8 open- and 4 closed-source detectors using RAID benchmark datasets. |
| Outcome: | The proposed detectors are fooled by adversarial attacks, repetition penalties, and unseen generative models. |
Alignment before Awareness: Towards Visual Question Localized-Answering in Robotic Surgery via Optimal Transport and Answer Semantics (2024.lrec-main)
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| Challenge: | Recent models for visual question localized-answering (VQLA) lack the ability to relate these answers to their localization at an instance level. |
| Approach: | They propose a model which introduces optimal transport to achieve bidirectional and fine-grained alignment between images and questions, enabling more precise localization. |
| Outcome: | The proposed model outperforms state-of-the-art models on two widely-used datasets on surgical scenes and surgical instruments. |
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. |
Taming the Real-world Complexities in CPT E/M Coding with Large Language Models (2025.emnlp-industry)
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Islam Nassar, Yang Lin, Yuan Jin, Rongxin Zhu, Chang Wei Tan, Zenan Zhai, Nitika Mathur, Thanh Tien Vu, Xu Zhong, Long Duong, Yuan-Fang Li
| Challenge: | Evaluation and Management (E/M) coding is performed by physicians and trained human coders who review clinical encounter notes and electronic health record data to assign appropriate codes. |
| Approach: | They propose a framework that automates evaluation and management coding tasks using the Current Procedural Terminology (CPT) taxonomy. |
| Outcome: | The proposed framework achieves an increase in coding accuracy of more than 36% over a commercial CPT E/M coding system and almost 5% over our strongest single-prompt baseline. |
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%. |
Making RALM Robust to Irrelevant Contexts via Layer Knowledge Guided Attention (2025.findings-acl)
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Weijie Shi, Hao Chen, Jiaming Li, Yao Zhao, Yazhong Zhang, Qijin Chen, Jipeng Zhang, Ruiyuan Zhang, Jia Zhu, Jiajie Xu, Xiaofang Zhou
| Challenge: | Large language models (LLMs) face factual hallucination and knowledge obsolescence when tackling knowledge-intensive tasks. |
| Approach: | They propose a layer-knowledge guided attention method which harnesses the layer-wise knowledge of large language models to optimize per-layer attention on useful passages. |
| Outcome: | The proposed method outperforms existing methods on RALM benchmarks. |
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. |
Multi-view Classification Model for Knowledge Graph Completion (2020.aacl-main)
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| Challenge: | Existing knowledge graph completion models only evaluated candidate triples from content information. |
| Approach: | They propose a multi-view classification model where multiple views are performed based on both content and context information for candidate triple evaluation. |
| Outcome: | The proposed model improves on two representative datasets and improves performance. |
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. |
BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing multilingual benchmarks focus primarily on language understanding tasks. |
| Approach: | They develop a multi-way multilingual benchmark that measures critical capabilities of large language models across languages. |
| Outcome: | Extensive experiments on BenchMAX reveal uneven utilization of core capabilities across languages, emphasizing the performance gaps that scaling model size alone does not resolve. |
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. |
Automatic Extraction of Commonsense LocatedNear Knowledge (P18-2)
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| Challenge: | LocatedNear relation is a kind of commonsense knowledge describing two physical objects that are typically found near each other in real life. |
| Approach: | They propose to automatically extract LocatedNear relation from corpus by a sentence-level relation classifier and aggregating scores of entity pairs from a large corpus. |
| Outcome: | The proposed method can be used to extract the commonsense LOCATEDNEAR relation from a large corpus. |
MeNTi: Bridging Medical Calculator and LLM Agent with Nested Tool Calling (2025.naacl-long)
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| Challenge: | Large Language Models (LLMs) have been widely used in medicine but are limited in their ability to fully address the complexities of the real world. |
| Approach: | They propose a universal agent architecture for Large Language Models that integrates a specialized medical toolkit and employs meta-tool and nested calling mechanisms to enhance LLM tool utilization. |
| Outcome: | The proposed framework improves the accuracy and performance of medical calculators in complex medical scenarios. |
i-Code Studio: A Configurable and Composable Framework for Integrative AI (2024.emnlp-demo)
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Yuwei Fang, Mahmoud Khademi, Chenguang Zhu, Ziyi Yang, Reid Pryzant, Yichong Xu, Yao Qian, Takuya Yoshioka, Lu Yuan, Michael Zeng, Xuedong Huang
| Challenge: | Existing frameworks for Integrative AI lack flexibility and composability to handle multimodal tasks. |
| Approach: | They propose a configurable framework for Integrative AI that orchestrates multiple pre-trained models to conduct complex multimodal tasks. |
| Outcome: | The proposed framework achieves impressive results on zero-shot multimodal tasks . it can communicate and personalize for users, and it can be used in a multimodal agent . |
Role Prompting Guided Domain Adaptation with General Capability Preserve for Large Language Models (2024.findings-naacl)
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| Challenge: | Large Language Models (LLMs) suffer catastrophic forgetting when tailored to specific domains . authors present a novel approach to manage multi-domain LLM adaptation . |
| Approach: | They propose a strategy to manage multi-domain LLM adaptation using self-distillation and role integration. |
| Outcome: | The proposed model alleviates catastrophic forgetting and inter-domain confusion while maintaining robust general capabilities. |
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. |
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering (2022.acl-long)
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Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng
| Challenge: | Open-Domain Question Answering (ODQA) models typically include a retrieving module and a reading module. |
| Approach: | They propose a new open-domain question-answering framework that uses a knowledge-enhanced version of FiD to improve the approach. |
| Outcome: | The proposed model improves on ODQA benchmark datasets with less than 40% computation cost. |
Coupling Distant Annotation and Adversarial Training for Cross-Domain Chinese Word Segmentation (2020.acl-main)
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| Challenge: | Fully supervised neural approaches have achieved significant progress in the task of Chinese word segmentation (CWS) however, they suffer from the cross-domain issue when they come to processing of out-of-domain data. |
| Approach: | They propose to use Chinese word as a target domain for distant annotation and adversarial training to reduce noise and maximize utilization of the source domain information. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods on real-world datasets and significantly outperformed previous state- of-the art methods. |
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. |
ToolHop: A Query-Driven Benchmark for Evaluating Large Language Models in Multi-Hop Tool Use (2025.acl-long)
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Junjie Ye, Zhengyin Du, Xuesong Yao, Weijian Lin, Yufei Xu, Zehui Chen, Zaiyuan Wang, Sining Zhu, Zhiheng Xi, Siyu Yuan, Tao Gui, Qi Zhang, Xuanjing Huang, Jiecao Chen
| Challenge: | Effective evaluation of multi-hop tool use is critical for analyzing the understanding, reasoning, and function-calling capabilities of large language models. |
| Approach: | They propose a dataset that provides rigorous evaluation of multi-hop tool use. |
| Outcome: | The proposed model achieves 49.04% accuracy across five model families. |
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. |
Rethinking and Improving Multi-task Learning for End-to-end Speech Translation (2023.emnlp-main)
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| Challenge: | auxiliary tasks are highly consistent with end-to-end speech translation (ST) but their effectiveness has not been thoroughly studied. |
| Approach: | They propose an improved multi-task learning approach for the ST task that bridges the modal gap by mitigating the difference in length and representation. |
| Outcome: | The proposed approach achieves state-of-the-art on the MuST-C dataset with 20.8% of training time required by the current SOTA method. |
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. |
XLPT-AMR: Cross-Lingual Pre-Training via Multi-Task Learning for Zero-Shot AMR Parsing and Text Generation (2021.acl-long)
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| Challenge: | Abstract Meaning Representation (AMR) research is limited and challenging for languages other than English. |
| Approach: | They propose a cross-lingual pre-training approach for AMR parsing and text generation . they use an English-to-English parallel dataset and a multi-task learning approach . |
| Outcome: | The proposed approach outperforms baseline pre-training methods on English parsing and text generation tasks. |
CMNEE:A Large-Scale Document-Level Event Extraction Dataset Based on Open-Source Chinese Military News (2024.lrec-main)
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| Challenge: | Current research focuses on the general news or financial domains, with relatively few studies for military domain. |
| Approach: | They propose to annotate Chinese military news events from documents using a schema for the military domain. |
| Outcome: | The proposed dataset is large-scale, document-level open-source for the military domain . it contains 17,000 documents and 29,223 events, which are all manually annotated . |
HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification (2023.acl-long)
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| Challenge: | Existing dual-encoder methods in HTC achieve weak performance gains with huge memory overheads and their structure encoders heavily rely on domain knowledge. |
| Approach: | They propose a hierarchy-aware tree isomorphism network to enhance the text representations with only syntactic information of the label hierarchy. |
| Outcome: | The proposed model could boost the performance of hierarchical text classification without prior statistics or label semantics without prior data. |
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. |
Can Brain Signals Reveal Inner Alignment with Human Languages? (2023.findings-emnlp)
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| Challenge: | Brain Signals, such as Electroencephalography, and human languages have been explored independently for many downstream tasks, however, the connection between them has not been well explored. |
| Approach: | They introduce a multimodal transformer alignment model to observe coordinated representations between EEG and language. |
| Outcome: | The proposed method achieved an F1-score improvement of 1.7% on ZuCo and 9.3% on Zuco datasets for sentiment analysis, and 7.4% on ZuCO for relation detection. |
From Outcomes to Processes: Guiding PRM Learning from ORM for Inference-Time Alignment (2025.acl-long)
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| Challenge: | Existing approaches to align large language models with human preferences suffer from inconsistent scoring and suboptimal alignment. |
| Approach: | They propose a dual-consistency framework that aligns partial sequences with human preferences. |
| Outcome: | The proposed framework significantly reduces granularity discrepancies and improves GPT-4 evaluation scores. |
Multi-perspective Improvement of Knowledge Graph Completion with Large Language Models (2024.lrec-main)
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Derong Xu, Ziheng Zhang, Zhenxi Lin, Xian Wu, Zhihong Zhu, Tong Xu, Xiangyu Zhao, Yefeng Zheng, Enhong Chen
| Challenge: | Knowledge graph completion (KGC) is a widely used method to tackle incompleteness in knowledge graphs (KGs). |
| Approach: | They propose a general framework to compensate for the deficiency of contextualized knowledge by querying large language models from various perspectives. |
| Outcome: | The proposed framework improves knowledge graph completion (KGC) by querying large language models from various perspectives. |
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. |
Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) generate only one token at each decoding step, leading to high latency. |
| Approach: | They propose a speculative decoding paradigm that stores tokens in an adjacency matrix and employs a breadth-first-search algorithm to construct a draft tree. |
| Outcome: | The proposed method outperforms existing train-free methods by 30% and even a training method by 25%. |
MULTITAT: Benchmarking Multilingual Table-and-Text Question Answering (2025.findings-emnlp)
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| Challenge: | Existing TATQA datasets are limited to English, leading to drawbacks . existing datasets overlook challenges of multilingual TAT-QA and do not reflect real-world multilingual scenarios . |
| Approach: | They propose a multilingual TATQA dataset that can be translated into 10 languages . they use data from 3 mainstream TATQ datasets and analyze the results . |
| Outcome: | The proposed dataset outperforms other baselines by an average of 3.3 . |
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. |