Papers by Liang Huang
Too Long, Do Re-weighting for Efficient LLM Reasoning Compression (2026.acl-long)
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Zhong-Zhi Li, Xiao Liang, Zihao Tang, Lei Ji, Peijie Wang, Haotian Xu, Xing W, Haizhen Huang, Weiwei Deng, Yeyun Gong, Zhijiang Guo, Xiao Liu, Fei Yin, Cheng-Lin Liu
| Challenge: | Large Language Models (LLMs) have recently achieved remarkable progress on complex reasoning tasks by leveraging extended Chain-of-Thought (CoT) techniques. |
| Approach: | They propose a method that uses Extended Chain-of-Thought (EFT) to reduce the number of output tokens by nearly 40% while maintaining the accuracy of the reasoning. |
| Outcome: | The proposed method reduces the number of output tokens by nearly 40% while maintaining the accuracy of the reasoning. |
Convolutional Interaction Network for Natural Language Inference (D18-1)
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| Challenge: | Attention-based neural models have achieved great success in natural language inference (NLI). |
| Approach: | They propose a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI. |
| Outcome: | The proposed model can capture complex interactions on three large datasets. |
ParroT: Translating during Chat using Large Language Models tuned with Human Translation and Feedback (2023.findings-emnlp)
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Wenxiang Jiao, Jen-tse Huang, Wenxuan Wang, Zhiwei He, Tian Liang, Xing Wang, Shuming Shi, Zhaopeng Tu
| Challenge: | Large language models (LLMs) like ChatGPT are only accessible through restricted APIs, which creates barriers to new research and advancements in the field. |
| Approach: | They propose a framework to enhance and regulate the translation abilities during chat . they reformulate translation data into the instruction-following style and introduce a "Hint" field . |
| Outcome: | The proposed framework enhances and regulates the translation abilities during chat . it reformulates translation data into the instruction-following style and introduces a "Hint" field . |
PaddleSpeech: An Easy-to-Use All-in-One Speech Toolkit (2022.naacl-demo)
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Hui Zhang, Tian Yuan, Junkun Chen, Xintong Li, Renjie Zheng, Yuxin Huang, Xiaojie Chen, Enlei Gong, Zeyu Chen, Xiaoguang Hu, Dianhai Yu, Yanjun Ma, Liang Huang
| Challenge: | PaddleSpeech is an open-source speech toolkit that supports speech-to-text and text-to speech tasks. |
| Approach: | They describe the design philosophy and core architecture of PaddleSpeech to support several essential speech-to-text and text-to speech tasks. |
| Outcome: | The proposed framework achieves competitive or state-of-the-art performance on various speech datasets and implements the most popular methods. |
VET: Verifiable Execution Tracing for Reliable Text-to-SQL Generation (2026.findings-acl)
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| Challenge: | Existing methods for text-to-SQL generation are prone to hallucinations and grounding . authors present a novel reasoning paradigm that transforms text- to-Sql from unverifiable textual rationales into step-wise executable semantics. |
| Approach: | They propose a reasoning paradigm that transforms text-to-SQL from unverifiable textual rationales into step-wise executable semantics. |
| Outcome: | The proposed reasoning paradigm transforms text-to-SQL from unverifiable textual rationales into step-wise executable semantics. |
Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks (D19-1)
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| Challenge: | Existing models that can handle cross-lingual tasks with limited or no training data are insensitive to different languages. |
| Approach: | They propose to use Unicoder to train models in one language and apply it to other languages. |
| Outcome: | Experiments show that Unicoder learns the mappings among different languages from more perspectives. |
CLOMO: Counterfactual Logical Modification with Large Language Models (2024.acl-long)
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Yinya Huang, Ruixin Hong, Hongming Zhang, Wei Shao, Zhicheng Yang, Dong Yu, Changshui Zhang, Xiaodan Liang, Linqi Song
| Challenge: | Existing studies on evaluating model reasoning are limited in both form and content. |
| Approach: | They propose a task to cultivate counterfactual thought processes within large language models and an evaluation metric to evaluate their natural language output instead of modeling the task as a multiple-choice problem. |
| Outcome: | The proposed evaluation metric aligns well with human preference. |
MINER: Improving Out-of-Vocabulary Named Entity Recognition from an Information Theoretic Perspective (2022.acl-long)
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Xiao Wang, Shihan Dou, Limao Xiong, Yicheng Zou, Qi Zhang, Tao Gui, Liang Qiao, Zhanzhan Cheng, Xuanjing Huang
| Challenge: | Named Entity Recognition models are feature-engineering and machine learning based. |
| Approach: | They propose a new NER learning framework that uses entity mentions to improve model performance. |
| Outcome: | The proposed model achieves better performance on OOV entities on various settings and datasets. |
Task Oriented In-Domain Data Augmentation (2024.emnlp-main)
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| Challenge: | Existing methods for large language models suffer from two major issues: in-domain data are scarce compared with general domain-agnostic data. |
| Approach: | They propose a task-oriented in-domain data augmentation framework that uses in- domain data selection and task-orientated synthetic passage generation to adapt LLMs to two domains: advertisement and math. |
| Outcome: | The proposed framework improves LLM performance by 8% in the advertisement domain and 7.5% in the math domain. |
Neural Chain-of-Thought Search: Searching the Optimal Reasoning Path to Enhance Large Language Models (2026.findings-acl)
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| Challenge: | Recent research indicates that Large Reasoning Models suffer from a strategic bottleneck at reasoning path planning. |
| Approach: | They propose a framework that reformulates reasoning as a dynamic search for the optimal thinking strategy. |
| Outcome: | The proposed framework improves accuracy and computational cost while reducing generation length by over 22%. |
Refuse Whenever You Feel Unsafe: Improving Safety in LLMs via Decoupled Refusal Training (2025.acl-long)
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Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, Jen-tse Huang, Jiahao Xu, Tian Liang, Pinjia He, Zhaopeng Tu
| Challenge: | Large Language Models exhibit a level of intelligence that is both impressive and everevolving, but their ability to refuse generating unsafe content is a double-edged sword. |
| Approach: | They propose a method to tackle a refusal position bias within safety tuning data that compromises the models’ ability to appropriately refuse generating unsafe content. |
| Outcome: | The proposed method significantly improves model safety without compromising performance and surpasses baseline methods in defending against attacks. |
Rethinking Task-Specific Knowledge Distillation: Contextualized Corpus as Better Textbook (2022.emnlp-main)
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| Challenge: | Existing methods for knowledge distillation use a two-stage paradigm: general distillation with a task-agnostic general corpus and task-specific distillation using augmented task- specific corpus. |
| Approach: | They propose a contextualized corpus that contextualizes task corpus with large-scale general corpus through relevance-based text retrieval to improve student learning. |
| Outcome: | The proposed model improves on the GLUE benchmark and shows that it is better than generalized corpus and augmented task-specific corpus. |
HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation (2023.acl-long)
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| Challenge: | Similes are a crucial part of creative writing, but there is still a lack of evaluation metrics for simile generation. |
| Approach: | They propose to use similes as a tool to evaluate simile generation metrics . they propose to combine five criteria and automatic metrics for each criterion . |
| Outcome: | The proposed metrics are significantly more correlated with human ratings from each perspective compared with prior automatic metrics. |
C-World: A Computer Use Agent Environment Creator (2026.acl-long)
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Ziqiao Xi, Shuang Liang, Qi Liu, Jiaqing Zhang, Letian Peng, Fang Nan, Meshal Nayim, Tianhui Zhang, Rishika Mundada, Lianhui Qin, Biwei Huang, Kun Zhou
| Challenge: | C-World enables users to build agent environments on demand. |
| Approach: | They propose a system that enables users to build agent environments on demand. |
| Outcome: | The proposed system outperforms baselines on 119k samples and achieves Spearman = 0.883 ranking correlation with real execution. |
Respond in my Language: Mitigating Language Inconsistency in Response Generation based on Large Language Models (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) show strong instruction understanding ability across multiple languages, but are easily biased towards English in instruction tuning. |
| Approach: | They propose to use a model with Pseudo-Inconsistent Penalization to prevent the model from generating English responses when given non-English language prompts during training and prior Enhanced decoding to improve the language consistency of the model. |
| Outcome: | The proposed methods significantly improve the language consistency of the model without multilingual data. |
MT3: A Synergistic Multi-Task RL Framework for Specializing MLLMs in Text Image Machine Translation (2026.acl-long)
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Zhaopeng Feng, Yupu Liang, Shaosheng Cao, Jiayuan Su, Jiahan Ren, Zhijie Zhou, Wenxuan Huang, Jian Wu, Zuozhu Liu
| Challenge: | Text Image Machine Translation (TIMT) is a critical subfield of machine translation . it requires accurate optical character recognition, robust visual-text reasoning, and high-quality translation a challenge . |
| Approach: | They propose a multi-task optimization framework to specialize MLLMs into expert TIMT models. |
| Outcome: | The proposed model outperforms baselines on the latest in-domain MIT-10M benchmark. |
Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models (2026.findings-acl)
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Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, Ngai Wong
| Challenge: | Existing literature on mechanistic interpretation (MI) treats it as an observational science, leaving practical applications underexplored. |
| Approach: | They propose a survey structured around the pipeline to identify and improve MI models. |
| Outcome: | The proposed framework enables tangible improvements in Alignment, Capability, and Efficiency. |
Simpler and Faster Learning of Adaptive Policies for Simultaneous Translation (D19-1)
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| Challenge: | Recent work on simultaneous translation is difficult because of its latency and quality. |
| Approach: | They propose a supervised-learning framework to learn adaptive policies from parallel text sequences . they use a model that predicts when a target word is read or WRITE if context provides enough information . |
| Outcome: | Experiments on German=>English show that the proposed method can learn flexible policies with better BLEU scores and similar latencies compared to previous work. |
Hidden States as Early Signals: Step-level Trace Evaluation and Pruning for Efficient Test-Time Scaling (2026.findings-acl)
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| Challenge: | Existing approaches to speed up parallel scaling have relied on similarity-based or confidence-based pruning, but these signals do not reliably indicate trace quality. |
| Approach: | They propose a pruning framework that evaluates reasoning steps using hidden states and dynamically prunes unpromising traces during generation. |
| Outcome: | The proposed framework reduces end-to-end inference latency by 45%–70% on average compared to self-consistency while improving reasoning accuracy. |
Infinity-Parser: Layout-Aware Reinforcement Learning with High-quality Document Parsing Dataset (2026.findings-acl)
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Baode Wang, Biao Wu, Weizhen Li, Meng Fang, Zuming Huang, Jun Huang, Yanjie Liang, Haozhe Wang, Ling Chen, Wei Chu, Yuan Qi
| Challenge: | Existing supervised fine-tuning methods struggle to generalize across document types, leading to poor performance. |
| Approach: | They propose layoutRL, a reinforcement learning framework that optimizes layout understanding through composite rewards integrating normalized edit distance, paragraph count accuracy, and reading order preservation. |
| Outcome: | The proposed model outperforms specialized document parsing systems and general-purpose vision-language models on a broad range of document types, languages, and structural complexities. |
A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck (2022.coling-1)
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| Challenge: | Event argument extraction (EAE) aims to extract arguments with given roles from texts. |
| Approach: | They propose a multi-format transfer learning model with variational information bottleneck to learn from existing datasets. |
| Outcome: | The proposed model improves on three benchmark datasets and obtains state-of-the-art performance on EAE. |
mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding (2024.findings-emnlp)
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Anwen Hu, Haiyang Xu, Jiabo Ye, Ming Yan, Liang Zhang, Bo Zhang, Ji Zhang, Qin Jin, Fei Huang, Jingren Zhou
| Challenge: | Existing Multimodal Large Language Models lack general structure understanding abilities for text-rich document images. |
| Approach: | They propose to use unified structure learning to boost the performance of MLLMs by encoding structure information into text-rich images. |
| Outcome: | The proposed model achieves state-of-the-art on 10 visual document understanding benchmarks. |
mPLUG-DocOwl2: High-resolution Compressing for OCR-free Multi-page Document Understanding (2025.acl-long)
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| Challenge: | Multimodal Large Language Models (MLLMs) have improved document understanding performance but generate thousands of visual tokens for a single document image, leading to excessive GPU memory and slower inference times. |
| Approach: | They propose a high-resolution document compression module to generate 324 tokens for a single document image. |
| Outcome: | The proposed module reduces first token latency by more than 50% and improves document comprehension performance. |
PFDial: A Structured Dialogue Instruction Fine-tuning Method Based on UML Flowcharts (2025.findings-acl)
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Ming Zhang, Yuhui Wang, Yujiong Shen, Tingyi Yang, Changhao Jiang, Yilong Wu, Shihan Dou, Qinhao Chen, Zhiheng Xi, Zhihao Zhang, Yi Dong, Zhen Wang, Zhihui Fei, Mingyang Wan, Tao Liang, Guojun Ma, Qi Zhang, Tao Gui, Xuanjing Huang
| Challenge: | Large Language Models (LLMs) have shown remarkable progress in dialogue and reasoning, but they struggle to solve strictly constrained dialogue tasks. |
| Approach: | They construct a dataset that contains 12,705 high-quality Chinese dialogue instructions from 440 flowcharts containing 5,055 process nodes. |
| Outcome: | The proposed model outperforms GPT-4o models on backward transitions and outperformed GPT-42 models on the same dataset. |
Flooding-X: Improving BERT’s Resistance to Adversarial Attacks via Loss-Restricted Fine-Tuning (2022.acl-long)
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Qin Liu, Rui Zheng, Bao Rong, Jingyi Liu, ZhiHua Liu, Zhanzhan Cheng, Liang Qiao, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing approaches to generating adversarial perturbations scale up the cost of training computational complexity by the number of gradient steps it takes to obtain the adversarials. |
| Approach: | They propose a flood method which aims at better generalization and a criterion to bring hyper-parameter-dependent flooding into effect with a narrowed-down search space by measuring how the gradient steps taken within one epoch affect the loss of each batch. |
| Outcome: | The proposed method improves BERT’s resistance to textual adversarial attacks by a large margin and achieves state-of-the-art robust accuracy on various text classification and GLUE tasks. |
AutoScraper: A Progressive Understanding Web Agent for Web Scraper Generation (2024.emnlp-main)
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Wenhao Huang, Zhouhong Gu, Chenghao Peng, Jiaqing Liang, Zhixu Li, Yanghua Xiao, Liqian Wen, Zulong Chen
| Challenge: | Existing methods for web scraping suffer from limited adaptability and scalability when faced with a new website. |
| Approach: | They propose a framework that generates web scrapers with large language models and a new executability metric to measure the performance of web scraper generation tasks. |
| Outcome: | The proposed framework can handle diverse web environments more efficiently. |
Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR (2021.findings-acl)
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| Challenge: | Existing approaches to simultaneous speech-to-text translation suffer from error propagation and extra latency. |
| Approach: | They propose a new paradigm for simultaneous speech-to-text translation using two separate decoders . they use multitask learning to jointly learn these two tasks with a shared encoder . |
| Outcome: | The proposed method achieves substantially better translation quality at similar levels of latency. |
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs (2023.findings-acl)
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Zijie Huang, Daheng Wang, Binxuan Huang, Chenwei Zhang, Jingbo Shang, Yan Liang, Zhengyang Wang, Xian Li, Christos Faloutsos, Yizhou Sun, Wei Wang
| Challenge: | Existing methods to embed knowledge graphs have ignored the fact that they contain two fundamentally different views: high-level ontology-view concepts and fine-grained instance-view entities. |
| Approach: | They propose a novel geometric representation that jointly embeds the two views of a KG using dual geometric representations. |
| Outcome: | Experiments on the public DBpedia KG and a newly-created industrial KG show the proposed method works well. |
A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extraction (2026.findings-acl)
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Chengguang Gan, Sunbowen Lee, Qingyu Yin, Yunhao Liang, Xinyang He, Hanjun Wei, Younghun Lim, Shijian Wang, Hexiang Huang, QingHao Zhang, Shiwen Ni, Tatsunori Mori
| Challenge: | Existing work on the Mutual Reinforcement Effect in information extraction has not been empirically validated . 76 percent of the 21 sub-datasets exhibit the Mutual Reforcement effect across languages . |
| Approach: | They propose a multilingual MRE mix dataset that integrates 21 sub-datasets covering English, Japanese, and Chinese. |
| Outcome: | The proposed framework reduces manual annotation effort while preserving structural requirements of MRE tasks. |
Think Beyond Words: Exploring Context-Relevant Visual Commonsense for Diverse Dialogue Generation (2022.findings-emnlp)
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| Challenge: | Existing approaches to generate intelligent open-domain dialogue agents only consider auxiliary commonsense stored in pure text, ignoring grounding information from the external visual world. |
| Approach: | They propose a VIsual Commonsense enhanced dialogue generaTOR that exploits auxiliary commonsense from images related to context to generate coherent and informative responses. |
| Outcome: | The proposed method outperforms the latest competitive methods in terms of coherence and diversity on two public datasets. |
DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models (2024.acl-long)
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Damai Dai, Chengqi Deng, Chenggang Zhao, R.x. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.k. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, Wenfeng Liang
| Challenge: | Mixture-of-Experts (MoE) architectures face challenges in ensuring expert specialization . despite the promising performance, scaling language models to an extremely large scale is associated with exceedingly high computational costs. |
| Approach: | They propose an architecture that allows for ultimate expert specialization by segmenting experts into mN ones and activating mK from them. |
| Outcome: | The proposed architecture achieves comparable performance with GShard with 2B parameters and computation. |
InsLogicBench: An Argumentation Logic Grounded Benchmark for Complex Insurance Claims Adjudication (2026.acl-long)
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Jin Liu, Yunpeng Liu, Keyi Wang, Jie Shi, Xiao Xu, Wenkang Huang, Xingzhong Xu, Xin Liang, Yanghua Xiao
| Challenge: | Existing benchmarks for insurance claims adjudication are limited to information retrieval or simple multiple-choice setups. |
| Approach: | They propose a benchmark that provides complete reasoning traces linking factual inputs, relevant policy clauses, and final verdicts. |
| Outcome: | The proposed benchmark shows that models often produce correct decisions but fail to provide precise justifications, highlighting a critical discrepancy between decision accuracy and logical reasoning capabilities. |
DialogGen: Multi-modal Interactive Dialogue System with Multi-turn Text-Image Generation (2025.findings-naacl)
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Minbin Huang, Yanxin Long, Xinchi Deng, Ruihang Chu, Jiangfeng Xiong, Xiaodan Liang, Hong Cheng, Qinglin Lu, Wei Liu
| Challenge: | Text-to-image (T2I) generation models have advanced in recent years, but effective interaction with these models is challenging for average users due to the need for specialized prompt engineering knowledge and the inability to perform multi-turn image generation. |
| Approach: | They propose to use off-the-shelf MLLMs and T2I models to build a multi-modal interactive dialogue system (MIDS) that can generate correct output modalities and coherence of output images. |
| Outcome: | The proposed pipeline can generate correct output modalities and coherent multi-modal outputs compared with other state-of-the-art models. |
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive Scenarios (2025.naacl-long)
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Xinyi Mou, Jingcong Liang, Jiayu Lin, Xinnong Zhang, Xiawei Liu, Shiyue Yang, Rong Ye, Lei Chen, Haoyu Kuang, Xuanjing Huang, Zhongyu Wei
| Challenge: | Large language models are increasingly employed to empower autonomous agents to simulate human behavior. |
| Approach: | They propose to evaluate LLM-driven agents through multi-turn interactions using a bottom-up approach to create diverse social scenarios constructed from extensive scripts. |
| Outcome: | The proposed model evaluates LLM-driven agents through multi-turn interactions emphasizing goal completion and implicit reasoning. |
MCS: An In-battle Commentary System for MOBA Games (2022.coling-1)
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Xiaofeng Qi, Chao Li, Zhongping Liang, Jigang Liu, Cheng Zhang, Yuanxin Wei, Lin Yuan, Guang Yang, Lanxiao Huang, Min Li
| Challenge: | In-battle commentary is an important component of live streaming of e-sports competitions and is applicable to a wide range of scenarios like combat information analysis and live streaming. |
| Approach: | They propose a generative system for in-battle real-time commentary in mobile MOBA games and propose 'transform' method to convert match statistics and utterances into consistent encoding space. |
| Outcome: | The proposed system is based on real-time match statistics and events and can be used for live streaming, e-sports commentary and combat information analysis. |
Simultaneous Translation with Flexible Policy via Restricted Imitation Learning (P19-1)
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| Challenge: | Existing approaches to simultaneous translation have been limited and use fixed-latency policies or a complicated two-staged model. |
| Approach: | They propose a single model that adds a “delay” token to the target vocabulary and a restricted dynamic oracle to greatly simplify training. |
| Outcome: | The proposed model achieves better BLEU scores and lower latencies compared to fixed and RL-learned policies on Chinese -> English simultaneous translation. |
DUAL RM: Beyond Rule-based Preference Reward Modeling via Meta-Reward (2026.acl-long)
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Xiaobo Liang, Wanfu Wang, Qipeng Huang, Yuyang Ding, Zecheng Tang, Yixin Ji, Qianben Chen, Zhe Zhao, Kehai Chen, Juntao Li, Min Zhang
| Challenge: | Existing preference-based reward modeling methods face a recursive dependency where each verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation. |
| Approach: | They propose a dual RM that couples discriminative and generative reward models under a non-parametric meta-reward. |
| Outcome: | The proposed model achieves strong performance across major preference benchmarks and even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench. |
Context-aware Embedding for Targeted Aspect-based Sentiment Analysis (P19-1)
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| Challenge: | Existing methods do not specifically pre-train reasonable embeddings for targets and aspects in TABSA. |
| Approach: | They propose to refine the embeddings of targets and aspects using a sparse coefficient vector . this allows the embeds to be refined from highly correlative words instead of context-independent vectors . |
| Outcome: | Experiments show that the proposed method improves on two benchmark datasets. |
From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems (2025.findings-emnlp)
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Zekun Zhou, Xiaocheng Feng, Lei Huang, Xiachong Feng, Ziyun Song, Ruihan Chen, Liang Zhao, Weitao Ma, Yuxuan Gu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | rapid development of artificial intelligence (AI) technologies has inspired researchers to explore how AI can accelerate and enhance research. |
| Approach: | They organize the relevant studies into three main categories: hypothesis formulation, hypothesis validation, and manuscript publication. |
| Outcome: | The authors summarize the current state of research in three main areas: hypothesis formulation, hypothesis validation, and manuscript publication. |
Simultaneous Translation (2020.emnlp-tutorials)
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| Challenge: | Simultaneous translation is a problem that has long been considered one of the hardest problems in AI . this tutorial will provide a deep understanding of the history and the recent advances in simultaneous translation. |
| Approach: | This tutorial will examine the design and evaluation of policies for simultaneous translation . it will provide an overview of the history and recent advances in simultaneous translation. |
| Outcome: | This tutorial will examine the design and evaluation of policies for simultaneous translation . |
POMP: Probability-driven Meta-graph Prompter for LLMs in Low-resource Unsupervised Neural Machine Translation (2024.acl-long)
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| Challenge: | Low-resource languages (LRLs) face challenges in supervised neural machine translation due to limited parallel data. |
| Approach: | They propose a method that uses a dynamic graph to organize auxiliary languages in prompts to improve LRL translations. |
| Outcome: | The proposed method improves translation accuracy in low-resource languages (LRLs) using auxiliary language pairs and synthetic pseudo-parallel data. |
Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefix (2023.findings-emnlp)
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| Challenge: | a large pre-trained language model can cause computational burdens in inference time due to multiple forward passes. |
| Approach: | They propose a method to learn fixed text representations with source tasks . they learn a task-specific prefix for each source task independently and combine them . |
| Outcome: | The proposed method improves generalizability of representations with source tasks. |
STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework (P19-1)
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Mingbo Ma, Liang Huang, Hao Xiong, Renjie Zheng, Kaibo Liu, Baigong Zheng, Chuanqiang Zhang, Zhongjun He, Hairong Liu, Xing Li, Hua Wu, Haifeng Wang
| Challenge: | Simultaneous translation is notoriously dif- ficult due to word-order differences. |
| Approach: | They propose a prefix-to-prefix framework that implicitly learns to anticipate in a single translation model. |
| Outcome: | The proposed framework achieves low latency and reasonable qual- ity on 4 directions. |
A Middle Path for On-Premises LLM Deployment: Preserving Privacy Without Sacrificing Model Confidentiality (2025.emnlp-main)
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| Challenge: | Privacy-sensitive users require deploying large language models within their own infrastructure (on-premises) vulnerabilities in local environments can lead to unauthorized access and potential model theft. |
| Approach: | They propose a framework that secures a few bottom layers in a secure environment . they propose metric to optimize trade-off between protection and customization flexibility . |
| Outcome: | The proposed framework outperforms baselines on five models with 1.3B to 70B parameters. |
Act-Adaptive Margin: Dynamically Calibrating Reward Models for Subjective Ambiguity (2026.acl-long)
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Feiteng Fang, Dingwei Chen, Xiang Huang, Ting-En Lin, Yuchuan Wu, Xiong Liu, Jing Ye, Ziqiang Liu, Haonan Zhang, Liang Zhu, Hamid Alinejad-Rokny, Min Yang, Yongbin Li
| Challenge: | Existing approaches to reward modeling in reinforcement learning tasks are limited when dealing with ambiguous preferences. |
| Approach: | They propose to use AAM to dynamically calibrate preference margins using the Bradley-Terry model's internal parameter knowledge to improve reward modeling in subjective tasks. |
| Outcome: | The proposed approach improves reward modeling by dynamically calibrating preference margins using the model’s internal parameter knowledge. |
Memp: Exploring Agent Procedural Memory (2026.findings-acl)
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Runnan Fang, Yuan Liang, Xiaobin Wang, Jialong Wu, Shuofei Qiao, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang
| Challenge: | Large Language Models (LLMs) based agents suffer from brittle procedural memory that is manually engineered or entangled in static parameters. |
| Approach: | They propose a procedural-memory repository that distills past agent trajectories into fine-grained, step-by-step instructions and higher-level, script-like abstractions. |
| Outcome: | The proposed repository can be used to improve agents' performance on travelplanner and Alfworld. |
Alleviating Hallucinations from Knowledge Misalignment in Large Language Models via Selective Abstention Learning (2025.acl-long)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Yuchun Fan, Xiachong Feng, Yuxuan Gu, Yangfan Ye, Liang Zhao, Weihong Zhong, Baoxin Wang, Dayong Wu, Guoping Hu, Lingpeng Kong, Tong Xiao, Ting Liu, Bing Qin
| Challenge: | Large language models (LLMs) suffer from severe hallucination issues due to the knowledge misalignment between the pre-training stage and the supervised fine-tuning stage. |
| Approach: | They propose a training objective with an abstention mechanism that selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token. |
| Outcome: | The proposed model selectively rejects tokens that misalign with the desired knowledge distribution via a special [REJ] token. |
Asynchronous Deep Interaction Network for Natural Language Inference (D19-1)
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| Challenge: | Existing methods have framed the reasoning problem as a semantic matching task. |
| Approach: | They propose an asynchronous deep interaction network (ADIN) to deconstruct the reasoning process and implement asynchron and multi-step reasoning. |
| Outcome: | The proposed model outperforms strong baselines on three popular benchmarks: SNLI, MultiNLI, and SciTail. |
Simultaneous Translation Policies: From Fixed to Adaptive (2020.acl-main)
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| Challenge: | Adaptive policies can balance translation quality and latency based on context information . previous methods on obtaining adaptive policies rely on complicated training process . |
| Approach: | They propose to obtain adaptive policies by a simple heuristic composition of fixed policies . they propose to use a heurism to obtain policies that can outperform fixed ones . |
| Outcome: | Experiments on Chinese -> English and German -> english show that adaptive policies outperform fixed policies by up to 4 BLEU points for the same latency. |
Opportunistic Decoding with Timely Correction for Simultaneous Translation (2020.acl-main)
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| Challenge: | Existing approaches to balancing translation quality and latency are either too aggressive or too conservative. |
| Approach: | They propose an opportunistic decoding technique that always (over-)generates a certain mount of extra words at each step to keep the audience on track with the latest information. |
| Outcome: | The proposed technique reduces latency and increases BLEU with no over-generating . it also corrects mistakes in the overgenerated words when observing more context . |
Context-aware Stand-alone Neural Spelling Correction (2020.findings-emnlp)
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| Challenge: | Existing natural language processing systems are vulnerable to noisy inputs resulting from misspellings. |
| Approach: | They propose a stand-alone spelling correction problem that corrects the spelling of tokens without additional token insertion or deletion. |
| Outcome: | The proposed solution outperforms the state-of-the-art spelling correction model by 12.8% absolute F0.5 score. |
Fluent and Low-latency Simultaneous Speech-to-Speech Translation with Self-adaptive Training (2020.findings-emnlp)
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| Challenge: | Current approaches to simultaneous speech-to-speech translation accumulate more and more latencies in later sentences when the speaker talks faster. |
| Approach: | They propose a method which generates more fluent target speech latency than the baseline . they propose to use self-adaptive translation to adjust the length of translations to accommodate different source speech rates. |
| Outcome: | Xiong et al., 2019) show that the proposed method generates more fluent target speech latency than baseline . authors say it provides more natural communication process than speech-to-text translation . xiong and colleagues say the proposed technique is more efficient than current approaches . |
Multi-Reference Training with Pseudo-References for Neural Translation and Text Generation (D18-1)
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| Challenge: | Neural text generation has been quite successful recently, but during training time, only one reference is considered for each example, even though there are often multiple references available. |
| Approach: | They propose an algorithm to generate exponentially many pseudo-references by compressing existing references into lattices and traversing them to generate new pseudo-References. |
| Outcome: | The proposed model significantly improves on baselines in machine translation and image captioning, and is comparable to existing models. |
MKT: A Multi-Stage Knowledge Transfer Framework to Mitigate Catastrophic Forgetting in Multi-Domain Chinese Spelling Correction (2025.emnlp-industry)
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Peng Xing, Yinghui Li, Shirong Ma, Xinnian Liang, Haojing Huang, Yangning Li, Shu-Yu Guo, Hai-Tao Zheng, Wenhao Jiang, Ying Shen
| Challenge: | Chinese Spelling Correction (CSC) is a model that detects and corrects spelling errors in given sentences. |
| Approach: | They propose a model-agnostic model with an evolving teacher model and dynamic distillation weights for knowledge transfer in each domain rather than focusing solely on new domain knowledge. |
| Outcome: | The proposed model-agnostic framework is based on an evolving teacher model and dynamic distillation weights for knowledge transfer in each domain, rather than focusing solely on new domain knowledge. |
Generating Hashtags for Short-form Videos with Guided Signals (2023.acl-long)
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Tiezheng Yu, Hanchao Yu, Davis Liang, Yuning Mao, Shaoliang Nie, Po-Yao Huang, Madian Khabsa, Pascale Fung, Yi-Chia Wang
| Challenge: | Short-form video hashtag recommendation (SVHR) is a classification or ranking problem that selects hashtags from a set of limited candidates. |
| Approach: | They propose a short-form video hashtag recommendation task that better represents how hashtags are created naturally by retrieving relevant hashtags from a large-scale hashtag pool as extra guidance signals. |
| Outcome: | The proposed model outperforms strong classification baselines on two short-form video datasets and the guidance signals boost the performance by 8.11 and 2.17 absolute ROUGE-1 scores on average. |
Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems (2026.acl-long)
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| Challenge: | Rapid urbanization and surging vehicle ownership intensify congestion . rapid urbanization drives crash rates, slow emergency response, and burden transit-poor communities . |
| Approach: | They introduce a 3B-parameter foundation model with human-like reasoning for Traffic signal control (TSC) they use reinforcement learning and network communication to convert LLM into a traffic-control model that operates like a human traffic agent. |
| Outcome: | The proposed model outperforms baselines and training-intensive RL controllers on a simulated traffic environment and reduces queue lengths by more than 5%. |
TRIGO: Benchmarking Formal Mathematical Proof Reduction for Generative Language Models (2023.emnlp-main)
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Jing Xiong, Jianhao Shen, Ye Yuan, Haiming Wang, Yichun Yin, Zhengying Liu, Lin Li, Zhijiang Guo, Qingxing Cao, Yinya Huang, Chuanyang Zheng, Xiaodan Liang, Ming Zhang, Qun Liu
| Challenge: | Automated theorem proving (ATP) benchmarks focus on symbolic inference but rarely involve understanding complex number combination reasoning. |
| Approach: | They propose a benchmark that requires a model to reduce a trigonometric expression with step-by-step proof and evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms. |
| Outcome: | The proposed benchmark evaluates a generative LM’s reasoning ability on formulas and ability to manipulate, group, and factor number terms. |
DAGN: Discourse-Aware Graph Network for Logical Reasoning (2021.naacl-main)
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| Challenge: | Recent QA with logical reasoning questions requires passage-level relations among the sentences. |
| Approach: | They propose a discourse-aware graph network that aggregates passage-level clues for QA by using discourse-based information. |
| Outcome: | The proposed model achieves competitive results on two logical reasoning QA datasets. |
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. |
Student Guides Teacher: Weak-to-Strong Inference via Spectral Orthogonal Exploration (2026.acl-long)
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| Challenge: | Existing Large Language Models suffer from "Reasoning Collapse" on mathematical reasoning tasks where stochastic sampling produces lexical variations of the same erroneous logic rather than genuine semantic exploration. |
| Approach: | They propose a geometric inference framework that uses a spectral orthogonal probe to introduce semantically heterogeneous reasoning signals into the teacher's orthogonale complement of its dominant subspace. |
| Outcome: | The proposed framework improves accuracy and sampling efficiency over baseline methods on logic and code generation benchmarks. |
Strength Lies in Differences! Improving Strategy Planning for Non-collaborative Dialogues via Diversified User Simulation (2024.emnlp-main)
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| Challenge: | Non-collaborative dialogue agents are expected to engage in strategic conversations with diverse users, and this poses two main challenges for existing dialogue agents: 1) the inability to integrate user-specific characteristics into the strategic planning; 2) the difficulty of training strategic planners that can be generalized to diverse users. |
| Approach: | They propose to integrate a user-aware strategic planning module and a population-based training paradigm into a non-collaborative dialogue agent for securing a mutual agreement that leans favorably towards the system's objectives. |
| Outcome: | The proposed model can be used to achieve a mutual agreement that leans favorably towards the system's objectives. |
UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model (2023.findings-emnlp)
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Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, Qin Jin, Liang He, Xin Lin, Fei Huang
| Challenge: | Existing studies for visually-situated language understanding have shown shallow zero-shot visual text recognition ability when fed a low-resolution image with salient text information. |
| Approach: | They propose a model for universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM) their model is jointly finetuned on a wide range of visually situated language understanding tasks via a unified instruction format. |
| Outcome: | The proposed model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks across 5 domains: documents, tables, charts, natural images, and webpage screenshots. |
CQG: A Simple and Effective Controlled Generation Framework for Multi-hop Question Generation (2022.acl-long)
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| Challenge: | Current models can not ensure the complexity of generated questions, so they may generate shallow questions that can be answered without multi-hop reasoning. |
| Approach: | They propose a controlled framework to generate multi-hop questions that contain key entities in multi- hop reasoning chains and a novel Transformer-based decoder to guarantee that key entities appear in the questions. |
| Outcome: | The proposed model outperforms the state-of-the-art model 25% on HotpotQA. |
Investigating and Mitigating the Multimodal Hallucination Snowballing in Large Vision-Language Models (2024.acl-long)
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Weihong Zhong, Xiaocheng Feng, Liang Zhao, Qiming Li, Lei Huang, Yuxuan Gu, Weitao Ma, Yuan Xu, Bing Qin
| Challenge: | Large Vision-Language Models (LVLMs) suffer from multimodal hallucinations . however, the generated hallucines could influence the models’ subsequent generation . |
| Approach: | They propose a framework to evaluate LVLMs' behaviors when encountering generated hallucinations and a method to revise the output distribution of LVLs with the one derived from the residual visual input. |
| Outcome: | The proposed framework reduces the performance of open-source LVLMs by 31%, indicating that they are prone to accept the generated hallucinations and make false claims that they would not have supported without distractions. |
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. |
Towards Quantifiable Dialogue Coherence Evaluation (2021.acl-long)
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| Challenge: | Existing automatic dialogue coherence evaluation metrics are expensive and high-latency, which cannot meet the requirements of a dialogue system. |
| Approach: | They propose a framework to train a quantifiable dialogue coherence metric that can reflect actual human rating standards. |
| Outcome: | Experimental results show that the model trained by QuantiDCE presents stronger correlations with human judgements than the other state-of-the-art metrics. |
CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models (2024.findings-acl)
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Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Noah Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Wenhao Huang, Chenghua Lin, Jie Fu
| Challenge: | a recent study shows that large language models have limited generalization in low-resource languages like Chinese. |
| Approach: | They propose to evaluate the zero-shot generalizability of large language models to the Chinese language . they release only half of the dataset publicly, with the remainder kept private . |
| Outcome: | The Chinese Instruction-Following Benchmark evaluates the generalizability of LLMs to the Chinese language. |
Is There a One-Model-Fits-All Approach to Information Extraction? Revisiting Task Definition Biases (2024.findings-emnlp)
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| Challenge: | Definition bias is a negative phenomenon that can mislead models. |
| Approach: | They propose a framework that measures definition bias, bias-aware fine-tuning and task-specific bias mitigation to mitigate definition bias in information extraction. |
| Outcome: | The proposed framework mitigates definition bias in information extraction tasks by measuring definition bias, bias-aware fine-tuning, and task-specific bias mitigation. |
Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging (D18-1)
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| Challenge: | Existing POS tagging methods for Twitter use labeled newswire text . however, Twitter users tend to mimic formal media expressions and develop linguistically informal styles. |
| Approach: | They propose to use newswire text to learn POS tagging for Twitter while twitter users are developing linguistically informal styles. |
| Outcome: | The proposed method achieves better performance than state-of-the-art methods on three different datasets. |
Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning (2021.acl-long)
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| Challenge: | Existing methods for solving geometric problems are either small in scale or not publicly available. |
| Approach: | They propose a large-scale benchmark for geometric problem solving using formal language and symbolic reasoning. |
| Outcome: | The proposed approach parses geometry problems into formal language and performs symbolic reasoning step by step. |
Revisiting the Negative Data of Distantly Supervised Relation Extraction (2021.acl-long)
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| Challenge: | Existing methods for relation extraction with distant supervision generate plenty of training samples but noisy labels and imbalanced training data cause problems. |
| Approach: | They propose a method that automatically labels a sentence with relational triples from a knowledge base. |
| Outcome: | The proposed method outperforms existing methods even with false positive samples. |
Change Entity-guided Heterogeneous Representation Disentangling for Change Captioning (2025.findings-acl)
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| Challenge: | Existing approaches to describe differences between two images are highly challenging due to distractors such as illumination and viewpoint changes. |
| Approach: | They propose a change-entity-guided disentanglement network that explicitly learns difference representations while mitigating the impact of distractors. |
| Outcome: | The proposed method outperforms existing methods on CLEVR-Change, CLE VR-DC and Spot-the-Diff datasets and achieves state-of-the art performance. |
CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models (2024.acl-long)
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Tong Zhang, Peixin Qin, Yang Deng, Chen Huang, Wenqiang Lei, Junhong Liu, Dingnan Jin, Hongru Liang, Tat-Seng Chua
| Challenge: | Large language models are used to meet user information needs, but their effectiveness in dealing with user queries that contain various types of ambiguity remains unknown. |
| Approach: | They propose a benchmark for evaluating large language models using a well-organized taxonomy. |
| Outcome: | The proposed model is based on a well-organized taxonomy and compares it with other models. |
PRIME: A Process-Outcome Alignment Benchmark for Verifiable Reasoning in Mathematics and Engineering (2026.acl-long)
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Xiangfeng Wang, Hangyu Guo, Yanlin Lai, Mitt Huang, Liang Zhao, Chengyuan Yao, Yinmin Zhang, Qi Han, null Xiaoxiaoren, Chun Yuan, Tong Xu, Zheng Ge, Xiangyu Zhang, Daxin Jiang
| Challenge: | Current outcome-centric verification paradigms neglect potential errors in the derivation process. |
| Approach: | They propose a process-aware RLVR training paradigm utilizing verifiers selected via **PRIME**. |
| Outcome: | The proposed approach outperforms the baseline verification paradigm on AIME24, AIME25, and Beyond-AIME models. |
M2PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning (2024.emnlp-main)
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Taowen Wang, Yiyang Liu, James Liang, Junhan Zhao, Yiming Cui, Yuning Mao, Shaoliang Nie, Jiahao Liu, Fuli Feng, Zenglin Xu, Cheng Han, Lifu Huang, Qifan Wang, Dongfang Liu
| Challenge: | Multimodal Large Language Models (MLLMs) exhibit remarkable performance across a wide range of domains. |
| Approach: | They propose a multimodal prompt tuning approach for efficient instruction tuning of MLLMs. |
| Outcome: | The proposed approach shows superior performance on multimodal evaluation datasets compared to state-of-the-art methods. |
AlignedCoT: Prompting Large Language Models via Native-Speaking Demonstrations (2024.findings-emnlp)
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| Challenge: | Existing LLMs are delicate and elusive in prompt words and styles. |
| Approach: | They propose an LLM-acquainted prompting technique that includes proficient "native-speaking" they propose to use in-context learning to prompt LLMs to perform high-performance reasoning . |
| Outcome: | The proposed technique achieves step-wise prompts in zero-shot scenarios while maintaining the prompt quality. |
Agent Trading Arena: A Study on Numerical Understanding in LLM-Based Agents (2025.findings-emnlp)
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| Challenge: | Existing approaches to large language models are limited to historical backtesting and static data. |
| Approach: | a new large-language model is developed to simulate real-time trading in a virtual stock market . the agent trading arena simulates real-world bid-ask interactions and provides real-life trading scenarios . |
| Outcome: | The Agent Trading Arena simulates real-world market conditions and directly impacts price dynamics. |
Exploiting Noisy Data in Distant Supervision Relation Classification (N19-1)
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| Challenge: | Existing approaches to relation classification are noisy and time-consuming . RCEND uses noisy data to split noisy data into correctly and incorrectly labeled data . |
| Approach: | They propose a framework to enhance relation classification by exploiting noisy data . they use an instance discriminator with reinforcement learning to split noisy data into correctly and incorrectly labeled data based on the noisy data. |
| Outcome: | The proposed method outperforms the state-of-the-art models on relation classification . the proposed method is based on a semi-supervised learning method . |
AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents (2025.findings-acl)
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Yuxiang Chai, Siyuan Huang, Yazhe Niu, Han Xiao, Liang Liu, Guozhi Wang, Dingyu Zhang, Shuai Ren, Hongsheng Li
| Challenge: | a new dataset is being developed to improve the capabilities of mobile GUI-control agents. |
| Approach: | They propose a dataset designed for generalist mobile GUI-control agents . they use screenshots from popular mobile applications to create a detailed GUI-annotated dataset . |
| Outcome: | The Android Multi-annotation EXpo (AMEX) is a large-scale dataset for generalist mobile GUI-control agents . it includes screenshots from popular mobile applications, which are annotated at multiple levels . |
Iterative Nearest Neighbour Machine Translation for Unsupervised Domain Adaptation (2023.findings-acl)
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| Challenge: | Existing methods for supervised domain adaptation of machine translation focus on fine-tuning, which is non-extensible. |
| Approach: | They propose to perform unsupervised domain adaptation in a non-parametric manner by using in-domain monolingual data and performing nearest neighbour inference on both forward and backward directions. |
| Outcome: | The proposed method significantly improves the in-domain translation performance and achieves state-of-the-art results among non-parametric methods. |
TinyChart: Efficient Chart Understanding with Program-of-Thoughts Learning and Visual Token Merging (2024.emnlp-main)
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| Challenge: | Recent studies have shown that multimodal large language models can be useful for chart understanding, but their size limits their use in resource-constrained environments. |
| Approach: | They propose an efficient multimodal large language model with only 3B parameters for chart understanding. |
| Outcome: | The proposed model outperforms several chart-understanding MLLMs with up to 13B parameters on ChartQA, Chart-to-Text, Chart to Table, OpenCQA, and ChartX. |
Improving Recall of Large Language Models: A Model Collaboration Approach for Relational Triple Extraction (2024.lrec-main)
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| Challenge: | Existing large language models can extract triples from simple sentences with few-shot learning or fine-tuning, but they often miss out when extracting from complex sentences. |
| Approach: | They propose an evaluation-filtering framework that integrates large language models with small models for relational triple extraction tasks. |
| Outcome: | The proposed framework integrates large language models with small models for relational triple extraction tasks. |
Improving Simultaneous Translation by Incorporating Pseudo-References with Fewer Reorderings (2021.emnlp-main)
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| Challenge: | Existing systems for simultaneous translation are still trained on full-sentence bitexts due to the abundance of unnecessary long-distance reorderings. |
| Approach: | They propose to rewrite target side of existing full-sentence corpora into simultaneous-style translation by adding generated pseudo-references to the target side. |
| Outcome: | Experiments on ZhEn and JaEn simultaneous translation show that the proposed method improves on existing full-sentence corpora. |
EduBench: A Comprehensive Benchmarking Dataset for Evaluating Large Language Models in Diverse Educational Scenarios (2026.acl-long)
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Bin Xu, Yu Bai, Huashan Sun, Yiguan Lin, Siming Liu, Xinyue Liang, Yaolin Li, Zhuangzhi Dong, Jingren Zhang, Yufan Deng, Xinyu Zou, Yang Gao, Heyan Huang
| Challenge: | Existing benchmarks that focus on knowledge-intensive tasks do not reflect diverse educational scenarios. |
| Approach: | They propose a benchmark that incorporates 9 major scenarios and 4,000 educational contexts. |
| Outcome: | The proposed model performs comparable to state-of-the-art large models on the test set. |
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding (P19-1)
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| Challenge: | Neural machine translation models are sensitive to noises in input sentences . one special kind of noise is the homophone noise, where words are replaced by other words with similar pronunciations. |
| Approach: | They propose to embed phonetic and textual information into neural machine translation datasets to improve robustness to homophone noises. |
| Outcome: | The proposed method improves the robustness of neural machine translation to homophone noises on clean test sets. |
Length-Adaptive Distillation: Customizing Small Language Model for Dynamic Token Pruning (2023.findings-emnlp)
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| Challenge: | Existing methods to accelerate inference speed are model compression and dynamic computation (e.g., dynamic token pruning). |
| Approach: | They propose a two-stage knowledge distillation framework that produces a customized small language model for dynamic token pruning. |
| Outcome: | The proposed framework can make the small language model more customized for dynamic token pruning and achieve better speed-performance trade-off. |
CNNs for NLP in the Browser: Client-Side Deployment and Visualization Opportunities (N18-5)
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| Challenge: | a JavaScript implementation of a convolutional neural network performs feedforward inference completely in the browser. |
| Approach: | They propose a JavaScript implementation that performs feedforward inference completely in the browser. |
| Outcome: | The proposed model performs feedforward inference completely in the browser without server requests . the proposed model is useful for applications with stringent latency requirements or low connectivity . |
Self-Evolution Knowledge Distillation for LLM-based Machine Translation (2025.coling-main)
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| Challenge: | Existing knowledge distillation strategies for large language models minimize output distributions between student and teacher models indiscriminately for each token. |
| Approach: | They propose a distillation strategy that integrates teacher and one-hot distribution of ground truth into the student distribution as prior knowledge, which promotes the distillation process. |
| Outcome: | The proposed method brings an average improvement of approximately 1.4 SacreBLEU points across four translation directions in the WMT22 test sets. |
Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining (2023.emnlp-main)
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Jingcong Liang, Rong Ye, Meng Han, Qi Zhang, Ruofei Lai, Xinyu Zhang, Zhao Cao, Xuanjing Huang, Zhongyu Wei
| Challenge: | Recent studies have discussed its capability to assist language models for various applications. |
| Approach: | They propose a structure to organize arguments using the **Hi**erarchical **Ar**gumentation **G**raph (Hi-ArG) and propose two approaches to exploit Hi-AarG, including a text-graph multi-modal model GreaseArR and a framework augmented with graph information. |
| Outcome: | The proposed structure supersedes existing language models on two argumentation tasks while incorporating graph information during further training improves vanilla language models. |
ChatMusician: Understanding and Generating Music Intrinsically with LLM (2024.findings-acl)
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Ruibin Yuan, Hanfeng Lin, Yi Wang, Zeyue Tian, Shangda Wu, Tianhao Shen, Ge Zhang, Yuhang Wu, Cong Liu, Ziya Zhou, Liumeng Xue, Ziyang Ma, Qin Liu, Tianyu Zheng, Yizhi Li, Yinghao Ma, Yiming Liang, Xiaowei Chi, Ruibo Liu, Zili Wang, Chenghua Lin, Qifeng Liu, Tao Jiang, Wenhao Huang, Wenhu Chen, Jie Fu, Emmanouil Benetos, Gus Xia, Roger Dannenberg, Wei Xue, Shiyin Kang, Yike Guo
| Challenge: | Despite LLMs' impressive capabilities in musical knowledge, music reasoning remains an unsolved task. |
| Approach: | They propose an open-source large language model (LLM) that integrates intrinsic musical abilities into LLaMA2 and GPT-3.5. |
| Outcome: | The proposed model can understand and generate music with a pure text tokenizer without external multi-modal neural structures or tokenizers. |
A3: Android Agent Arena for Mobile GUI Agents with Essential-State Procedural Evaluation (2026.findings-acl)
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Yuxiang Chai, Shunye Tang, Han Xiao, Weifeng Lin, Hanhao Li, Jiayu Zhang, Liang Liu, Pengxiang Zhao, Guangyi Liu, Guozhi Wang, Shuai Ren, Rongduo Han, Haining Zhang, Siyuan Huang, Hongsheng Li
| Challenge: | Existing evaluation methods for mobile GUI agents rely on static frame assessments or offline static apps. |
| Approach: | They propose an evaluation system that leverages large language models as reward models to verify task completion and process achievement. |
| Outcome: | The proposed system addresses the limitations of traditional function based evaluation methods on online dynamic apps. |
AssoCiAm: A Benchmark for Evaluating Association Thinking while Circumventing Ambiguity (2025.emnlp-main)
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| Challenge: | Recent advances in multimodal large language models (MLLMs) have garnered significant attention, offering a promising pathway toward artificial general intelligence (AGI). |
| Approach: | They propose a benchmark to evaluate associative ability while circumventing the inherent ambiguity in association tasks by decomposing ambiguities into two types and propose 'assoCiAm' they conduct extensive experiments on MLLMs, revealing a strong positive correlation between cognition and association. |
| Outcome: | The proposed method shows that ambiguity in association evaluations makes MLLMs more random-like and the model's behavior more random. |
Data-Centric Perspectives on Agentic Retrieval-Augmented Generation: A Survey (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) excel at natural language understanding and generation, yet rely on static pre-training data. |
| Approach: | They propose to augment Large Language Models with external retrieval to ground model outputs . traditional RAG is constrained by a fixed retrieve-then-generate routine . authors aim to guide creation of high-quality datasets for next generation of adaptive LLM agents . |
| Outcome: | The proposed model can decompose tasks, issue exploratory queries, and refine evidence through iterative retrieval. |
Question Tells You Where the Answer Is: Intention-aware Long-Context KV Cache Compression (2026.acl-long)
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Liang Zhao, Xiaocheng Feng, Weihong Zhong, Lei Huang, Kun Zhu, Baoxin Wang, Dayong Wu, Guoping Hu, Ting Liu, Bing Qin
| Challenge: | Recent methods to reduce the KV cache size fail to identify crucial KVs for generation while excluding others accurately, resulting in severe information loss. |
| Approach: | They propose an intention-aware KV cache eviction method that identifies and retains crucial KVs according to the attention distribution of intention, which semantically reflects the user’s goal and determines which part of the context is relevant. |
| Outcome: | The proposed method can maintain the model performance while reducing the KV cache size from 128K to 2K, leading to a 6.3x increase in decoding speed and 7.8x enhancement in memory efficiency compared to the default setting. |
Learning First-Order Logic Rules for Argumentation Mining (2025.acl-long)
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Yang Sun, Guanrong Chen, Hamid Alinejad-Rokny, Jianzhu Bao, Yuqi Huang, Bin Liang, Kam-Fai Wong, Min Yang, Ruifeng Xu
| Challenge: | Argumentation Mining (AM) aims to extract argumentative structures from texts by identifying argumentation components (ACs) and their argumentative relations (ARs). |
| Approach: | They propose a First- Order Logic reasoning framework for AM to capture logical reasoning paths within argumentative texts. |
| Outcome: | The proposed framework outperforms strong baselines while significantly improving explainability. |
Controllable Contamination Detection for Reliable LLM Evaluation with Statistical Guarantees (2026.acl-long)
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Zheng Zhang, Qi Liu, Siyuan Liang, Ning Li, Zirui Hu, Weibo Gao, Rui Li, Zhenya Huang, Leszek Rutkowski, Baosheng Yu, Dacheng Tao
| Challenge: | Existing training data detectors fail to detect clean samples from contaminated test sets . existing methods fail to identify clean samples due to black-box nature of LLMs . |
| Approach: | They propose a framework that detects and filters contaminated evaluation data . they propose 'failure detection' to reduce the proportion of contaminated samples mistakenly retained . |
| Outcome: | The proposed framework reduces false discovery rate (FDR) under valid FDR control while maintaining evaluation consistency. |
ATG: Benchmarking Automated Theorem Generation for Generative Language Models (2024.findings-naacl)
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| Challenge: | Existing generative language models (LMs) can generate new or reusable theorems, but their ability to generate new theorels is under-explored. |
| Approach: | They propose to use Metamath library to generate new theorems that can be saved as reusable knowledge for future theoretical proving. |
| Outcome: | The proposed benchmark evaluates whether an agent can generate valuable (and possibly brand new) theorems that are applicable for downstream theoretic proving as reusable knowledge. |
Can MLLMs Understand the Deep Implication Behind Chinese Images? (2025.acl-long)
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Chenhao Zhang, Xi Feng, Yuelin Bai, Xeron Du, Jinchang Hou, Kaixin Deng, Guangzeng Han, Qinrui Li, Bingli Wang, Jiaheng Liu, Xingwei Qu, Yifei Zhang, Qixuan Zhao, Yiming Liang, Ziqiang Liu, Feiteng Fang, Min Yang, Wenhao Huang, Chenghua Lin, Ge Zhang, Shiwen Ni
| Challenge: | MLLMs perform poorly on traditional culture images, indicating limitations in understanding high-level semantics and lacking a deep knowledge base of Chinese traditional culture. |
| Approach: | They propose to use Chinese images to assess MLLMs' higher-order perception and understanding of Chinese visual content. |
| Outcome: | The proposed model incorporates images that represent Chinese traditional culture, such as famous Chinese traditional paintings, to ensure the authenticity of the Chinese context. |
VCORE: Variance-Controlled Optimization-based Reweighting for Chain-of-Thought Supervision (2026.acl-long)
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| Challenge: | Empirical evaluations demonstrate that VCORE achieves the strongest overall average performance, with especially clear gains on lower-capacity models. |
| Approach: | They propose a framework that reformulates supervision as a constrained optimization problem. |
| Outcome: | Empirical evaluations show that VCORE achieves the strongest overall average performance, with especially clear gains on lower-capacity models. |
A Semantic-based Method for Unsupervised Commonsense Question Answering (2021.acl-long)
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| Challenge: | Existing methods to score candidates without labeled task data are difficult to use . e.g., pre-trained language models can be easily affected by irrelevant factors . |
| Approach: | They propose a method that generates plausible answers with generative models and uses them to select the correct answer. |
| Outcome: | The proposed method achieves the best results in unsupervised situations. |
Think Natively: Unlocking Multilingual Reasoning with Consistency-Enhanced Reinforcement Learning (2026.acl-long)
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Xue Zhang, Yunlong Liang, Fandong Meng, Songming Zhang, Kaiyu Huang, Yufeng Chen, Xu Jinan, Jie Zhou
| Challenge: | Current Large Reasoning Models exhibit two critical limitations when processing non-English languages: (1) They struggle to maintain input-output language consistency; (2) They generally perform poorly with wrong reasoning paths and lower answer accuracy compared to English. |
| Approach: | They propose a language-consistency reward and a cross-lingual thinking alignment reward to improve the model's interpretability and accuracy. |
| Outcome: | The proposed model achieves nearly 100% language consistency and superior performance on two multilingual benchmarks (MMATH and PolyMath). |
Robust Lottery Tickets for Pre-trained Language Models (2022.acl-long)
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| Challenge: | Recent studies have shown that pre-trained language models contain smaller matching subnetworks that are not robust to adversarial examples. |
| Approach: | They propose a method to find robust tickets hidden in pre-trained language models by learning binary weight masks and an adversarial loss objective to guide the search. |
| Outcome: | The proposed method improves on previous work on adversarial robustness evaluation. |
MMSD2.0: Towards a Reliable Multi-modal Sarcasm Detection System (2023.findings-acl)
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| Challenge: | Existing benchmarks for multi-modal sarcasm detection have some shortcomings . a new framework can leverage multi-grained cues from multiple perspectives for multimodal detection . |
| Approach: | They propose a correction dataset that removes spurious cues and re-annotates the unreasonable samples. |
| Outcome: | The proposed framework outperforms the existing benchmarks in multi-modal sarcasm detection. |
PCAD: Towards ASR-Robust Spoken Language Understanding via Prototype Calibration and Asymmetric Decoupling (2024.acl-long)
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| Challenge: | Spoken language understanding (SLU) suffers from error propagation from automatic speech recognition (ASR) in actual scenarios. |
| Approach: | They propose a framework which calibrates bias and errors and achieves adaptive-balanced decoupling training by a prototype-based loss model. |
| Outcome: | The proposed framework outperforms existing approaches and achieves state-of-the-art performance on three datasets. |
RLSeek: Evidence-Grounded Reasoning for RAG Hallucination Detection (2026.acl-long)
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Zhaoheng Huang, Dacheng Wen, Yutao Zhu, Xiaoying Lian, Yushi Liang, Kai Hao, Nan Li, Liangjie Zhang, Qi Zhang, Ji-Rong Wen, Zhicheng Dou, Fangzhao Wu
| Challenge: | Recent work addresses this problem by training span-level hallucination detectors using reinforcement learning and chain-of-thought reasoning. |
| Approach: | They propose a framework that explicitly enforces active evidence seeking during CoT reasoning by requiring quotation of relevant source segments at each verification step. |
| Outcome: | The proposed framework improves hallucination span detection performance with limited reasoning overhead and improved robustness in out-of-domain settings. |
Distorted or Fabricated? A Survey on Hallucination in Video LLMs (2026.findings-acl)
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| Challenge: | Despite significant advances in video-language modeling, hallucinations remain a persistent challenge in video large language models. |
| Approach: | They present a systematic taxonomy that categorizes hallucinations into two core types: dynamic distortion and content fabrication. |
| Outcome: | The proposed taxonomy categorizes hallucinations into two core types: dynamic distortion and content fabrication. |
Speculative Beam Search for Simultaneous Translation (D19-1)
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| Challenge: | Beam search is widely used in (full-sentence) machine translation but its application to simultaneous translation remains highly non-trivial. |
| Approach: | They propose a beam search algorithm that hallucinates several steps into the future to reach a more accurate decision by implicitly benefiting from a target language model. |
| Outcome: | The proposed method improves on language models over diverse language pairs and shows significant improvements over greedy search. |
An ensemble CNN method for biomedical entity normalization (D19-57)
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| Challenge: | Named entity recognition (NER) and entity normalization (entity linking) are two fundamental natural language processing tasks to achieve entity normalizing. |
| Approach: | They propose a CNN method that normalizes microbiology-related entities to concepts in standard dictionaries. |
| Outcome: | The proposed method performs well in the BioNLP-OST19 shared task Bacteria Biotope. |
Hypernetwork-Assisted Parameter-Efficient Fine-Tuning with Meta-Knowledge Distillation for Domain Knowledge Disentanglement (2024.findings-naacl)
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| Challenge: | Recent work on domain adaptation for text summarization fails to account for the huge gap between dialogue and general articles. |
| Approach: | They propose a hypernetwork-assisted encoder-decoder architecture with parameter-efficient fine-tuning to disentangle domain-invariant knowledge from source domains while learning specific knowledge of the target domain. |
| Outcome: | The proposed model can disentangle domain-invariant knowledge from source domains while learning specific knowledge of the target domain. |
Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation (D18-1)
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| Challenge: | Beam search is widely used in neural machine translation, but beam sizes larger than 5 hurt translation quality. |
| Approach: | They propose to use beam search to improve translation quality by using hyperparameter-free methods that outperform the widely-used heuristic of length normalization by +2.0 BLEU. |
| Outcome: | The proposed methods outperform the widely-used heuristic on Chinese-to-English translation and achieve the best results among all methods. |
MAS-Bench: A Unified Benchmark for Shortcut-Augmented Hybrid Mobile GUI Agents (2026.acl-long)
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Pengxiang Zhao, Guangyi Liu, Yaozhen Liang, Weiqing He, Zhengxi Lu, WenHao Wang, Yuehao Huang, Yuxiang Chai, Zhaolu Kang, Yaxuan Guo, Hao Wang, Kexin Zhang, Liang Liu, Yong Liu
| Challenge: | Shortcuts such as APIs and deep-links have emerged as efficient complements to flexible GUI operations, but systematic evaluation of GUI–shortcut hybrid agents remains underexplored. |
| Approach: | They propose a benchmark that evaluates GUI-shortcut hybrid agents with a specific focus on the mobile domain. |
| Outcome: | MAS-Bench evaluates agent's ability to generate shortcuts by discovering and creating reusable, low-cost workflows. |
From Text to Historical Ecological Knowledge: The Construction and Application of the Shan Jing Knowledge Base (2024.lrec-main)
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| Challenge: | Traditional Ecological Knowledge (TEK) is a shared cultural heritage and crucial instrument to tackle environmental challenges. |
| Approach: | They propose to build a language resource based on Shanhai Jing (the classic of mountains and seas) written 2000 years ago and uses a stylized narrative and juxtaposition of knowledge from multiple domains to build the knowledge base. |
| Outcome: | The proposed knowledge base contains 1432 systematically classified entities and 3294 relationships. |
Targeting the Needle, Ignoring the Haystack: Anchoring Crucial Cues for Evolving Scam Call Detection via an LLM-Assisted Classifier (2026.findings-acl)
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| Challenge: | Existing methods for fraud detection on online service platforms often fail to generalize due to the scarcity of labeled data and the continuous evolution of conversational contexts. |
| Approach: | They propose a framework that anchors detection on Semantic Primitives . they prioritize stable evidence over conversational noise to ensure a verifiable fraud tactic . |
| Outcome: | The proposed framework achieves superior robustness and efficiency compared to baselines . it prioritizes stable evidence over diverse conversational noise . |
Context-aware Difference Distilling for Multi-change Captioning (2024.acl-long)
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| Challenge: | Existing methods for multi-change captioning are difficult because it requires a higher level of cognition to reason an arbitrary number of changes. |
| Approach: | They propose a context-aware difference distilling network to capture all genuine changes for yielding sentences. |
| Outcome: | The proposed network captures all genuine changes for yielding sentences on three public datasets. |
GraphAgent: Agentic Graph Language Assistant (2025.emnlp-main)
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| Challenge: | Real-world data combines structured and unstructured formats, capturing explicit relationships and implicit semantic interdependencies. |
| Approach: | They propose GraphAgent, an automated agent pipeline addressing both explicit and implicit graph-enhanced semantic dependencies for predictive and generative tasks. |
| Outcome: | Extensive experiments on diverse datasets validate GraphAgent’s effectiveness in graph-related predictive and text generative tasks. |
A Semantic Filter Based on Relations for Knowledge Graph Completion (2021.emnlp-main)
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| Challenge: | Knowledge graph embedding is a new form of knowledge graphing that allows for better link prediction. |
| Approach: | They propose to use relational embedding to fit symmetry/antisymmetry and combination relationships. |
| Outcome: | The proposed model can fit symmetry/antisymmetry and combination relationships. |
ESC-Eval: Evaluating Emotion Support Conversations in Large Language Models (2024.emnlp-main)
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Haiquan Zhao, Lingyu Li, Shisong Chen, Shuqi Kong, Jiaan Wang, Kexin Huang, Tianle Gu, Yixu Wang, Jian Wang, Liang Dandan, Zhixu Li, Yan Teng, Yanghua Xiao, Yingchun Wang
| Challenge: | Emotion Support Conversation (ESC) is a crucial application for reducing stress and providing emotional guidance. |
| Approach: | They re-organize 2,801 role-playing cards to define roles of role-players . they train a specific role- playing model called ESC-Role which behaves more like a confused person than GPT-4 . |
| Outcome: | The proposed model behaves more like a confused person than GPT-4, and the model performs better than GPLs. |
Adaptive Helpfulness–Harmlessness Alignment with Preference Vectors (2026.eacl-long)
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Ren-Wei Liang, Chin Ting Hsu, Chan-Hung Yu, Saransh Agrawal, Shih-Cheng Huang, Chieh-Yen Lin, Shang-Tse Chen, Kuan-Hao Huang, Shao-Hua Sun
| Challenge: | Existing approaches to balancing helpfulness and harmlessness suffer from performance conflicts, limited controllability, and poor extendability. |
| Approach: | They propose a framework that allows users to control their own preferences and dynamically merge them at test time. |
| Outcome: | The proposed framework improves helpfulness without conservatism and smooth control over preference trade-offs. |
Projecting Out the Malice: A Global Subspace Approach to LLM Detoxification (2026.acl-long)
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Zenghao Duan, Zhiyi Yin, Zhichao Shi, Liang Pang, Shaoling Jing, Zihe Huang, Jiayi Wu, Yu Yan, Jingcheng Deng, Huawei Shen, Xueqi Cheng
| Challenge: | Large language models (LLMs) exhibit exceptional performance but pose inherent risks of generating toxic content. |
| Approach: | They propose a method that removes toxic subspaces from FFN parameters . they propose to use a lightweight method to eliminate toxic subespaces . |
| Outcome: | The proposed method achieves SOTA detoxification while preserving general capabilities without large-scale retraining. |
RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs (2025.findings-emnlp)
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| Challenge: | a lack of comprehensive benchmarks for Routing large language models has hindered the development of routers. |
| Approach: | They propose a router-based benchmark to evaluate Routing large language models . the benchmark includes performance records for 12 popular LLM evaluations . |
| Outcome: | The proposed model-level scaling up phenomenon can surpass the best single model in the pool and many existing strong LLMs. |
COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)
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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin
| Challenge: | Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. |
| Approach: | They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets. |
| Outcome: | The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark. |
Semantic-Space Exploration and Exploitation in RLVR for LLM Reasoning (2026.findings-acl)
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Fanding Huang, Guanbo Huang, Xiao Fan, Yi He, Xiao Liang, Xiao Chen, Qinting Jiang, Faisal Nadeem Khan, Jingyan Jiang, Zhi Wang
| Challenge: | Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have substantially improved the reasoning abilities of Large Language Models (LLMs). |
| Approach: | They propose a method that balances exploration and exploitation in the hidden-state space of response trajectories. |
| Outcome: | The proposed model yields consistent improvements across models, algorithms and reasoning benchmarks. |
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation (P19-1)
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| Challenge: | Disentangling the content and style in the latent space is prevalent in text style transfer . recurrent neural networks (RNN) based encoder and decoder cannot deal with the long-term dependency . |
| Approach: | They propose a style transformer which disentangles style information in latent space . they propose encoding and decoding methods that disentangle style information . |
| Outcome: | The proposed method can achieve better style transfer and better content preservation. |
SHARP: Steering Hallucination in LVLMs via Representation Engineering (2025.emnlp-main)
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Junfei Wu, Yue Ding, Guofan Liu, Tianze Xia, Ziyue Huang, Dianbo Sui, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan
| Challenge: | Large Vision-Language Models (LVLMs) generate responses that are plausible but incorrect or unsupported—commonly referred to as hallucinations. |
| Approach: | They propose a representation-level intervention framework that modulates hallucination-related features during inference by probing their encoded features. |
| Outcome: | The proposed framework reduces hallucinations while maintaining the performance and generalization capabilities of Large Vision-Language Models (LVLMs). |
GRADE: Automatic Graph-Enhanced Coherence Metric for Evaluating Open-Domain Dialogue Systems (2020.emnlp-main)
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| Challenge: | Existing evaluation metrics only consider surface features or utterance-level semantics, without explicitly considering the fine-grained topic transition dynamics of dialogue flows. |
| Approach: | They propose a graph-enhanced evaluation metric GRADE to evaluate dialogue coherence . GRADE incorporates utterance-level contextualized representations and fine-grained topic-level graph representations to improve communication logic. |
| Outcome: | The proposed evaluation metric outperforms state-of-the-art metrics on measuring diverse dialogue models in terms of Pearson and Spearman correlations with human judgments. |
Improving Text Embeddings with Large Language Models (2024.acl-long)
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| Challenge: | Existing methods for obtaining text embeddings require complex training pipelines . authors leverage proprietary LLMs to generate diverse synthetic data for text embeds based on 93 languages . |
| Approach: | They propose a method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. |
| Outcome: | The proposed method achieves strong performance on competitive text embedding benchmarks without using any labeled data. |
Enhancing Chain-of-Thought Reasoning with Critical Representation Fine-tuning (2025.acl-long)
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Chenxi Huang, Shaotian Yan, Liang Xie, Binbin Lin, Sinan Fan, Yue Xin, Deng Cai, Chen Shen, Jieping Ye
| Challenge: | Representation Fine-tuning (ReFT) is a proposed method for improving parameter efficiency . however, it yields suboptimal performance, as fixed-position representations have uncertain impact on outputs . |
| Approach: | They propose a method that fine-tunes critical representations in a low-rank linear subspace while freezing the base model. |
| Outcome: | The proposed method improves accuracy of LLaMA-2-7B and ReFT by 18.2 and 3.8 on GSM8K. |
Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study (2025.findings-emnlp)
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Yujun Zhou, Jiayi Ye, Zipeng Ling, Yufei Han, Yue Huang, Haomin Zhuang, Zhenwen Liang, Kehan Guo, Taicheng Guo, Xiangqi Wang, Xiangliang Zhang
| Challenge: | Existing benchmarks that rely on final-answer accuracy fail to capture the quality of the reasoning process. |
| Approach: | They propose a fine-grained evaluation framework that assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing. |
| Outcome: | The proposed framework assesses logical reasoning across three dimensions: overall accuracy, stepwise soundness, and representation-level probing. |
SciMMIR: Benchmarking Scientific Multi-modal Information Retrieval (2024.findings-acl)
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Siwei Wu, Yizhi Li, Kang Zhu, Ge Zhang, Yiming Liang, Kaijing Ma, Chenghao Xiao, Haoran Zhang, Bohao Yang, Wenhu Chen, Wenhao Huang, Noura Al Moubayed, Jie Fu, Chenghua Lin
| Challenge: | Multi-modal information retrieval (MMIR) is a rapidly evolving field . current benchmarks for image-text pairings overlook the scientific domain . |
| Approach: | They develop a scientific domain-specific MMIR benchmark to evaluate image-text pairings using open-access research paper corpora. |
| Outcome: | The proposed benchmarks are based on 530K image-text pairs extracted from scientific documents with detailed captions. |
Attending via both Fine-tuning and Compressing (2021.findings-acl)
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| Challenge: | Existing studies show that attention mechanisms can improve models' interpretation, but they are not explicable. |
| Approach: | They propose a framework consisting of a learner and a compressor to purify attention scores . they propose to fine-tune and compress the attention mechanism to obtain a more faithful explanation . |
| Outcome: | The proposed framework improves performance and interpretability on eight benchmark datasets. |
MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models (2025.acl-long)
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| Challenge: | Existing LCU benchmarks for large language models often result in prohibitively high evaluation costs . existing benchmarks exhibit significant redundancy, which means inefficiency in evaluation . |
| Approach: | They propose a data compression method tailored for long-text data with sparse information characteristics. |
| Outcome: | The proposed method reduces evaluation costs to 4.5% of the long-text benchmark LongBench . the proposed method is based on a long-term LCU benchmark with sparse information characteristics . |
SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval (2023.acl-long)
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Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei
| Challenge: | SimLM uses a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training. |
| Approach: | They propose a simple yet effective pre-training method for dense passage retrieval that learns to compress the passage information into a dense vector through self-supervised pre-tuning. |
| Outcome: | The proposed method outperforms multi-vector approaches on large-scale passage retrieval datasets and shows significant improvements over baselines. |
Speeding Up Neural Machine Translation Decoding by Cube Pruning (D18-1)
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| Challenge: | Neural machine translation suffers from slow translation speed due to the large search space . a trade-off has to be made between translation quality and speed, argues a new study . |
| Approach: | They apply cube pruning technique to speed up dynamic programming into neural machine translation to speed it up. |
| Outcome: | The proposed method can translate faster on GPUs and CPUs with better translation quality than naive beam search. |
Towards Effective Automatic Debt Collection with Persona Awareness (2023.emnlp-industry)
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| Challenge: | Existing debt collection agents fail to tailor strategies to debtor personas, leading to ineffective collection. |
| Approach: | They present a commercial practice on debt collection agents that organizes debtor personas into a taxonomy and constructs a persona-aware conversation dataset. |
| Outcome: | The proposed agent increases recovery rate by 3.31% and collects additional 100K RMB after two months of testing. |
How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmark (2025.emnlp-main)
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| Challenge: | Prior work has not explored the mechanisms underlying this sensitivity. |
| Approach: | They propose a synthetic benchmark to evaluate Large Language Models’ reasoning robustness against systematically controlled irrelevant context (IC). |
| Outcome: | The proposed model improves in-distribution and out-of-disttribution scenarios while training with strong distractors. |
MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models (2026.findings-eacl)
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Siwei Wu, King Zhu, Yu Bai, Yiming Liang, Yizhi Li, Haoning Wu, Jiaheng Liu, Ruibo Liu, Xingwei Qu, Xuxin Cheng, Ge Zhang, Wenhao Huang, Chenghua Lin
| Challenge: | Current multimodal benchmarks focus on facts within individual images, but neglect associative relations among multiple images. |
| Approach: | They propose a multi-image relational association task and a MMRA benchmark to evaluate LVLMs. |
| Outcome: | The proposed benchmarks show that entity-level multi-image perception tasks pose greater challenges than image-level tasks. |
Debatrix: Multi-dimensional Debate Judge with Iterative Chronological Analysis Based on LLM (2024.findings-acl)
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| Challenge: | Recent studies have focused on short dialogues, but mainly on short debates. |
| Approach: | They propose to use Large Language Models to construct an automated debate judge to evaluate multi-turn debates. |
| Outcome: | The proposed system improves on the PanelBench benchmark, which compares its performance to actual debate outcomes. |
Maria: A Visual Experience Powered Conversational Agent (2021.acl-long)
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| Challenge: | Existing studies focus on grounding conversational agents on text-only corpora, but they lack the perception ability to our physical world. |
| Approach: | They propose to ground conversational agents on images retrieved from large-scale image indexes . they propose to use visual knowledge to generate informative responses based on the extracted knowledge . |
| Outcome: | The proposed agent outperforms state-of-the-art methods on automatic metrics and human evaluation. |
Adaptive Ordered Information Extraction with Deep Reinforcement Learning (2023.findings-acl)
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| Challenge: | Existing methods for information extraction follow a fixed extraction order for complex tasks with multiple elements to be extracted in one instance. |
| Approach: | They propose an adaptive ordered IE paradigm to find optimal element extraction order for different instances and a reinforcement learning framework to generate optimal order dynamically. |
| Outcome: | The proposed method beats existing methods and improves on several public datasets. |
Linear-time Constituency Parsing with RNNs and Dynamic Programming (P18-2)
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| Challenge: | Existing span-based constituency parsers are too slow for longer sentences and for applications beyond sentence boundaries. |
| Approach: | They propose a linear-time constituency parser with RNNs and dynamic programming using graph-structured stack and beam search. |
| Outcome: | The proposed parser is faster for long sentences and faster for discourse parsing. |
Getting More Juice Out of Your Data: Hard Pair Refinement Enhances Visual-Language Models Without Extra Data (2025.naacl-long)
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Haonan Wang, Minbin Huang, Runhui Huang, Lanqing Hong, Hang Xu, Tianyang Hu, Xiaodan Liang, Zhenguo Li, Hong Cheng, Kenji Kawaguchi
| Challenge: | Contrastive Language-Image Pre-training (CLIP) is a standard for cross-modal image-text representation learning. |
| Approach: | They propose a framework that enhances pre-trained CLIP models by exploiting challenging text-image pairs within existing datasets. |
| Outcome: | The proposed framework improves CLIP models by exploiting text-image pairs in training. |
Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models (2026.acl-long)
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Xin Cheng, Wangding Zeng, Damai Dai, Qinyu Chen, Bingxuan Wang, Zhenda Xie, Kezhao Huang, Xingkai Yu, Zhewen Hao, Han Zhang, Yu-Kun Li, Huishuai Zhang, Dongyan Zhao, Wenfeng Liang
| Challenge: | Mixture-of-Experts (MoE) scales capacity via conditional computation, but lacks knowledge lookup primitive. |
| Approach: | They propose a conditional memory instantiated via Deep Sparse Embedding (DSE) they propose 'u-shaped scaling law' that identifies optimal balance between MoE experts and DSE memory . |
| Outcome: | The proposed model outperforms an iso-parameter and isoFLOPs MoE baseline across knowledge and reasoning benchmarks and is infrastructure-efficient. |
Generative Entity Typing with Curriculum Learning (2022.emnlp-main)
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| Challenge: | Entity typing fails to assign an entity to the types beyond the predefined type set. |
| Approach: | They propose a generative entity typing paradigm that assigns types to entities . traditional classification-based approaches fail to assign entities to the types beyond the predefined set . they employ curriculum learning to train the model on heterogeneous data . |
| Outcome: | The proposed model outperforms the state-of-the-art model on heterogeneous training data. |
ORMind: A Cognitive-Inspired End-to-End Reasoning Framework for Operations Research (2025.acl-industry)
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| Challenge: | Large Language Models (LLMs) have shown promising results in various domains, but their practical application in industry-relevant operations research presents significant challenges and opportunities. |
| Approach: | They propose a cognitive-inspired framework that enhances optimization through counterfactual reasoning . they use a workflow that transforms requirements into mathematical models and executable solver code . |
| Outcome: | Experiments show that ORMind outperforms existing methods in the NL4Opt dataset and ComplexOR dataset. |
COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)
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Yuelin Bai, Xeron Du, Yiming Liang, Leo Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Moore Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang
| Challenge: | Existing datasets for Chinese instruction tuning are not well-aligned with Chinese users’ interaction patterns. |
| Approach: | They propose to use Chinese instruction tuning datasets to improve instruction fine-tuning for Chinese users. |
| Outcome: | The proposed dataset shows that Chinese models achieve competitive performance in diverse benchmarks. |
StyleDubber: Towards Multi-Scale Style Learning for Movie Dubbing (2024.findings-acl)
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Gaoxiang Cong, Yuankai Qi, Liang Li, Amin Beheshti, Zhedong Zhang, Anton Hengel, Ming-Hsuan Yang, Chenggang Yan, Qingming Huang
| Challenge: | Existing methods for movie dubbing break phonemes in scripts, resulting in incomplete phoneme pronunciation and poor identity stability. |
| Approach: | They propose a method that switches dubbing learning from frame level to phoneme level . it uses a multimodal style adaptor to learn pronunciation style from audio . |
| Outcome: | The proposed method improves on two benchmarks, V2C and Grid, and is available on github. |
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)
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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang
| Challenge: | Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored. |
| Approach: | They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities. |
| Outcome: | The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities. |
Advancing Large Language Model Attribution through Self-Improving (2024.emnlp-main)
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Lei Huang, Xiaocheng Feng, Weitao Ma, Liang Zhao, Yuchun Fan, Weihong Zhong, Dongliang Xu, Qing Yang, Hongtao Liu, Bing Qin
| Challenge: | Teaching large language models to generate text with citations to evidence sources requires high-quality attribution data, which is costly and labor-intensive. |
| Approach: | They propose a framework for iteratively improving the attribution capability of large language models (LLMs) by attributing output to verifiable sources. |
| Outcome: | Experiments on three open-domain question-answering datasets show that START improves in aggregating information across multiple sources. |
CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality (2023.acl-long)
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Liang Li, Ruiying Geng, Chengyang Fang, Bing Li, Can Ma, Rongyu Cao, Binhua Li, Fei Huang, Yongbin Li
| Challenge: | Current datasets bias in the English language while leaving other languages underexplored. |
| Approach: | They propose a Chinese answer-to-sequence dataset with high quality and large scale . they propose encoding space for two hybrid knowledge resources to convert this task to a graph-totext problem. |
| Outcome: | The proposed method is effective in generating textual descriptions for the Chinese answer-to-sequence dataset. |
CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark (2022.acl-long)
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Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen
| Challenge: | a new benchmark for biomedical language understanding is being developed in Chinese . most benchmarks are limited to English, which makes it difficult to replicate success in other languages. |
| Approach: | They propose to use Chinese biomedical language understanding evaluation benchmarks to evaluate Chinese models. |
| Outcome: | The proposed benchmarks show that the current models perform worse than the human ceiling. |
Learning to Stop in Structured Prediction for Neural Machine Translation (N19-1)
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| Challenge: | Beam search optimization solves many problems in neural machine translation, but lacks principled stopping criteria and does not learn how to stop during training. |
| Approach: | They propose a ranking method which enables an optimal beam search stop-ping criteria and a structured prediction loss function which penalizes suboptimal finished candidates produced by beam search during training. |
| Outcome: | Experiments on synthetic and real languages show that the proposed methods improve translation quality and length. |
SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement (2025.acl-short)
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Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen
| Challenge: | Using Large Language Models (LLMs)-based agents can enhance their understanding of environments and tasks. |
| Approach: | They propose a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search exploration to refine their action knowledge in the current environment. |
| Outcome: | The proposed framework synthesizes possible scenarios with multi-step action invocation within the action space and performs Monte Carlo Tree Search exploration to refine action knowledge in the current environment. |
RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training (2023.findings-emnlp)
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Jaehyung Kim, Yuning Mao, Rui Hou, Hanchao Yu, Davis Liang, Pascale Fung, Qifan Wang, Fuli Feng, Lifu Huang, Madian Khabsa
| Challenge: | Several perspectives of robustness for pre-trained language models have been studied independently, but lacking a unified consideration in multiple perspectives. |
| Approach: | They propose a technique to enhance the multi-perspective robustness of LMs by introducing adversarial perturbation while the model parameters are selectively updated upon their relative importance. |
| Outcome: | The proposed technique improves the robustness of LMs by incorporating four perspectives on model robustness. |
TRACE: Evaluating Execution Efficiency of LLM-Based Code Translation (2026.acl-long)
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| Challenge: | Large Language Models (LLMs) have improved the functional correctness of code translation, but execution efficiency remains overlooked. |
| Approach: | They propose a benchmark to explicitly assess execution efficiency in LLM-translated code. |
| Outcome: | The proposed benchmark identifies that execution efficiency is an essential dimension of code translation . the results highlight that correctness and efficiency are often misaligned . |
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition (2020.acl-main)
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| Challenge: | Many studies have applied reinforcement learning to train a dialog policy . but modeling a real-world user simulator is challenging and requires domain expertise . |
| Approach: | They propose to build dialog policies with two agents as dialog agents to avoid building a user simulator beforehand. |
| Outcome: | The proposed method can build a system policy and a user policy simultaneously . it can achieve high task success rate through conversational interaction . |
Surge: On the Potential of Large Language Models as General-Purpose Surrogate Code Executors (2025.emnlp-main)
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| Challenge: | Neural surrogate models are powerful tools in data mining, but are underexplored . large language models (LLMs) have demonstrated remarkable capabilities in code-related tasks . |
| Approach: | They propose a benchmarking framework to examine the feasibility of large language models . they examine scaling laws, data efficiency, and predictive accuracy of 21 open-source and proprietary LLMs . |
| Outcome: | The proposed benchmark examines 21 open-source and proprietary LLMs . it also examines scaling laws, data efficiency, and predictive accuracy . |
CodeV: Issue Resolving with Visual Data (2025.findings-acl)
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Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang, Dong Chen, Bo Shen, Tianyu Liu, Yongshun Gong, Huang Pengjie, Xudong Lu, Guangtai Liang, Lizhen Cui, Qianxiang Wang
| Challenge: | Large Language Models (LLMs) have expanded to more complex repository-level tasks. |
| Approach: | They propose a first approach to leveraging visual data to enhance the issue-resolving capabilities of Large Language Models (LLMs) they demonstrate the effectiveness of CodeV and provide valuable insights into leveraging visualization to resolve GitHub issues. |
| Outcome: | The proposed approach improves the issue-resolving capabilities of Large Language Models (LLMs) by using visual data. |
Competition-Level Problems are Effective LLM Evaluators (2024.findings-acl)
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Yiming Huang, Zhenghao Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan, Weizhu Chen
| Challenge: | Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about their capabilities and the potential data contamination problem. |
| Approach: | They propose to evaluate the reasoning capabilities of large language models in solving recent competition-level programming problems in Codeforces. |
| Outcome: | The proposed model has experienced a cliff-like decline in problems after September 2021, which shows the potential data contamination and the challenges for any existing LLM to solve unseen complex reasoning problems. |
Incremental Text-to-Speech Synthesis with Prefix-to-Prefix Framework (2020.findings-emnlp)
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Mingbo Ma, Baigong Zheng, Kaibo Liu, Renjie Zheng, Hairong Liu, Kainan Peng, Kenneth Church, Liang Huang
| Challenge: | Text-to-speech synthesis (TTS) has seen rapid progress in recent years, but still suffers from latencies. |
| Approach: | They propose a neural incremental TTS approach that synthesizes speech in an online fashion, playing a segment of audio while generating the next. |
| Outcome: | Experiments on English and Chinese TTS show that the proposed approach achieves similar speech naturalness compared to full sentence TTS, but with a constant (1-2 words) latency. |
Learning Neural Templates for Recommender Dialogue System (2021.emnlp-main)
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Zujie Liang, Huang Hu, Can Xu, Jian Miao, Yingying He, Yining Chen, Xiubo Geng, Fan Liang, Daxin Jiang
| Challenge: | Recent advances in neural models have shown promising progress on this task, but key challenges remain . |
| Approach: | They propose a framework that can decouple dialogue generation from item recommendation . they use a response template generator and item selector to generate a responses template . |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on the benchmark ReDial. |
Improve Transformer Models with Better Relative Position Embeddings (2020.findings-emnlp)
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| Challenge: | Existing methods for generating position embeddings are not fully utilized in NLP tasks. |
| Approach: | They propose to generalize the absolute position embedding to a generalized relative position embedded method . they also propose to use the relative embeddable method to improve the accuracy of large models . |
| Outcome: | The proposed method improves accuracy on the SQuAD1.1 dataset compared to previous methods . it can be easily adopted as a drop-in replacement for improving accuracy of large models . |
MGCL: Multi-Granularity Clue Learning for Emotion-Cause Pair Extraction via Cross-Grained Knowledge Distillation (2024.findings-emnlp)
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| Challenge: | Traditional methods often rely on coarse-grained clause-level annotations, which overlook valuable fine-grain clues. |
| Approach: | They propose a method that captures fine-grained clues from a weakly-supervised perspective efficiently by using a teacher model to give sub-clause clues without needing fine-grain annotations. |
| Outcome: | The proposed method achieves state-of-the-art performance while offering improved interpretability. |
Escaping the Echo Trap: On Credit Assignment Failure in Multi-turn LLM Self-Reflection (2026.acl-long)
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Linxuan Du, Guangquan Xue, Xiaobo Liang, Qipeng Huang, Yuyang Ding, Xinyu Shi, Zhang Yijun, Ji Qi, Wenpeng Zhu, Juntao Li, Min Zhang
| Challenge: | Existing methods for multi-turn self-reflection are limited by the Echo Trap problem . the model is limited by its inherent capabilities and repeats earlier reflections to preserve reward signals . |
| Approach: | They propose a tree-structured extension of GRPO for multi-turn self-reflection which enables more accurate advantage estimation. |
| Outcome: | The proposed method mitigates behavior collapse and improves performance across benchmarks. |
MMEvalPro: Calibrating Multimodal Benchmarks Towards Trustworthy and Efficient Evaluation (2025.naacl-long)
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Jinsheng Huang, Liang Chen, Taian Guo, Fu Zeng, Yusheng Zhao, Bohan Wu, Ye Yuan, Haozhe Zhao, Zhihui Guo, Yichi Zhang, Jingyang Yuan, Wei Ju, Luchen Liu, Tianyu Liu, Baobao Chang, Ming Zhang
| Challenge: | Large Multimodal Models (LMMs) exhibit impressive cross-modal understanding and reasoning abilities, but many benchmarks suffer from systematic biases. |
| Approach: | They propose a benchmark to avoid Type-I errors by creating one perception question and one knowledge anchor question through a meticulous annotation process. |
| Outcome: | The proposed benchmark avoids Type-I errors while maintaining reliability of MCQ evaluations. |
Towards User-Driven Neural Machine Translation (2021.acl-long)
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| Challenge: | a good translation should implicitly mirror user traits rather than translate the original content semantically. |
| Approach: | They propose a framework that captures user traits from historical inputs . they propose 'user-driven' NMT to model user behavior under a zero-shot learning fashion . |
| Outcome: | The proposed framework can capture user traits from historical inputs under zero-shot learning fashion. |
RAP: Efficient Text-Video Retrieval with Sparse-and-Correlated Adapter (2024.findings-acl)
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Meng Cao, Haoran Tang, Jinfa Huang, Peng Jin, Can Zhang, Ruyang Liu, Long Chen, Xiaodan Liang, Li Yuan, Ge Li
| Challenge: | Text-Video Retrieval (TVR) aims to align relevant video content with natural language queries. |
| Approach: | They propose to conduct efficient text-video Retrieval with a salient-and-correlated AdaPter . they propose a low-rank modulation module to refine per-image features from frozen CLIP backbone . |
| Outcome: | Experiments on four TVR datasets show that the proposed method performs better than other methods. |
MetaLogic: Logical Reasoning Explanations with Fine-Grained Structure (2022.emnlp-main)
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| Challenge: | Current explanation datasets often employ synthetic data with simple reasoning structures. |
| Approach: | They propose a comprehensive logical reasoning explanation form that incorporates three main components to better fit the human cognitive process. |
| Outcome: | The proposed model performs better than existing models on real-life scenarios, but is more challenging for the current models. |
LLEOT: A Privacy-Enhancing Offsite Tuning Framework via Loss Landscape Elevation (2026.findings-acl)
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| Challenge: | Existing approaches to fine-tune large language models are infeasible due to privacy regulations. |
| Approach: | They propose an offsite tuning framework that secures data privacy and model parameter and capability privacy. |
| Outcome: | The proposed framework secures data privacy and model parameter and capability privacy while preserving gradient alignment. |
Large Margin Neural Language Model (D18-1)
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| Challenge: | Conventionally, neural language models are trained by minimizing perplexity (PPL) on grammatical sentences. |
| Approach: | They propose a large margin criterion for training neural language models by minimizing perplexity on grammatical sentences and propose enlarged margins for task-specific training. |
| Outcome: | The proposed method gains up to 1.1 WER reduction for speech recognition and 1.0 BLEU increase for machine translation. |