Papers by Rui Zhao
Neural Machine Translation with Contrastive Translation Memories (2022.emnlp-main)
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| Challenge: | Experimental results show that retrieval-augmented NMT model obtains substantial improvements over strong baselines in the benchmark dataset. |
| Approach: | They propose a retrieval-augmented NMT model that is holistically similar to the source sentence while individually contrastive to each other. |
| Outcome: | The proposed model improves on baselines in the translation task. |
Make Your Decision Convincing! A Unified Two-Stage Framework: Self-Attribution and Decision-Making (2023.findings-emnlp)
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| Challenge: | Existing frameworks for explaining black-box model behavior are unreliable . large-scale pre-trained models often rely on superficial clues for predictions . |
| Approach: | They propose a unified two-stage framework that uses subsequences from the input text as a rationale to generate model decision. |
| Outcome: | The proposed framework achieves competitive results on five reasoning datasets and in semi-supervised scenarios. |
F2TEval: Human-Aligned Multi-Dimensional Evaluation for Figure-to-Text Task (2025.emnlp-main)
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| Challenge: | Existing evaluation methods for Figure-to-Text tasks are limited due to the inherent ambiguity and semantic compression of figures, the generated texts suffer from factual inaccuracies, incomplete coverage, and weak logical reasoning. |
| Approach: | They propose a five-dimensional reference-free evaluation method aligned with expert criteria to support fine-grained evaluation. |
| Outcome: | The proposed method outperforms Gemini-2.0 and Claude-3.5 with only 0.9B parameters. |
Grid Tagging Scheme for Aspect-oriented Fine-grained Opinion Extraction (2020.findings-emnlp)
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| Challenge: | Aspect-oriented Fine-grained Opinion Extraction (AFOE) aims to extract aspect terms and opinion terms from review text in the form of opinion pairs or opinion triplets. |
| Approach: | They propose a grid-based AFOE tagging scheme to address the task in an end-to-end fashion only with one unified grid tracking task. |
| Outcome: | The proposed tagging scheme outperforms baselines and achieves state-of-the-art performance. |
Lattice-Based Transformer Encoder for Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation (NMT) takes deterministic sequences for source representations. However, word-level or subword-level segmentation has multiple choices to split a source sequence with different word segmentors or different subword vocabulary sizes. |
| Approach: | They propose lattice-based encoders to explore effective word or subword representations in an automatic way during training. |
| Outcome: | The proposed encoders can explore effective word or subword representation in an automatic way during training. |
There Are a Thousand Hamlets in a Thousand People’s Eyes: Enhancing Knowledge-grounded Dialogue with Personal Memory (2022.acl-long)
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| Challenge: | Existing methods for knowledge selection focus on relevance between knowledge and dialogue context, ignoring personal preference for knowledge. |
| Approach: | They propose to introduce personal memory into knowledge selection in chatbots to address personalization issue by integrating personal memory and inverse mapping into a closed loop. |
| Outcome: | The proposed method outperforms existing methods significantly on automatic evaluation and human evaluation. |
Decouple knowledge from paramters for plug-and-play language modeling (2023.findings-acl)
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| Challenge: | Pre-trained language models (PLMs) have made impressive results in a wide range of NLP tasks. |
| Approach: | They propose a pre-training model with editable and scalable key-value memory and leverage knowledge in an explainable manner by knowledge retrieval in the pasted macro ‘MEMORY’. |
| Outcome: | The proposed model decouples the knowledge storage from model parameters with an editable and scalable key-value memory and leverages knowledge in an explainable manner by knowledge retrieval in the pasted macro ‘MEMORY’. |
LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing (2024.emnlp-main)
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Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, Wenpeng Yin
| Challenge: | a comparative analysis of paper (meta-)reviews by large language models (LLMs) aims to identify and distinguish LLMs from human activities . |
| Approach: | They present a comparative analysis to identify and distinguish LLM activities from human activities. |
| Outcome: | The proposed analysis aims to improve recognition of instances when someone implicitly uses LLMs for reviewing activities. |
FOLIO: Natural Language Reasoning with First-Order Logic (2024.emnlp-main)
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Simeng Han, Hailey Schoelkopf, Yilun Zhao, Zhenting Qi, Martin Riddell, Wenfei Zhou, James Coady, David Peng, Yujie Qiao, Luke Benson, Lucy Sun, Alexander Wardle-Solano, Hannah Szabó, Ekaterina Zubova, Matthew Burtell, Jonathan Fan, Yixin Liu, Brian Wong, Malcolm Sailor, Ansong Ni, Linyong Nan, Jungo Kasai, Tao Yu, Rui Zhang, Alexander Fabbri, Wojciech Kryscinski, Semih Yavuz, Ye Liu, Xi Lin, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Rex Ying, Arman Cohan, Dragomir Radev
| Challenge: | Existing benchmarks for logical reasoning in large language models lack language naturalness or limited complexity. |
| Approach: | They propose to use first-order logic annotations to evaluate logical reasoning capabilities of large language models. |
| Outcome: | The proposed dataset evaluates the FOL reasoning ability of supervised fine-tuning on medium-sized language models. |
Graph-Structured Speculative Decoding (2024.findings-acl)
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Zhuocheng Gong, Jiahao Liu, Ziyue Wang, Pengfei Wu, Jingang Wang, Xunliang Cai, Dongyan Zhao, Rui Yan
| Challenge: | Speculative decoding is a promising technique to accelerate the inference of Large Language Models. |
| Approach: | They propose a method that uses a token graph to record multiple sequence hypotheses within a single draft stage. |
| Outcome: | The proposed method significantly accelerates the inference of Large Language Models (LLMs) it allows the LLM to choose from and select the longest sequence that meets its standards. |
Enhancing Extractive Question Answering in Multiparty Dialogues with Logical Inference Memory Network (2025.coling-main)
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| Challenge: | Existing models for multiparty dialogue question answering (QA) do not consider logical inference relations in multiparty dialogs, leading to suboptimal performance. |
| Approach: | They propose a memory network with logical inference for extractive QA in multiparty dialogues. |
| Outcome: | The proposed model achieves state-of-the-art on Molweni and FriendsQA benchmarks. |
Uncovering Scaling Laws for Large Language Models via Inverse Problems (2025.findings-emnlp)
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Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
| Challenge: | Large Language Models (LLMs) have achieved remarkable success across diverse domains. |
| Approach: | inverse problems can efficiently uncover scaling laws that guide the building of LLMs, authors argue . authors propose brute-force approaches to improve LLM training costs due to high costs . |
| Outcome: | This paper advocates that inverse problems can efficiently uncover scaling laws that guide the building of LLMs to achieve the desirable performance with significantly better cost-effectiveness. |
Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation (2021.acl-long)
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| Challenge: | Existing related work generation models are inflexible and extract sentences from multiple papers to form a related work discussion. |
| Approach: | They propose a Relation-aware Related work generator which generates an abstractive related work from the given multiple scientific papers in the same research area. |
| Outcome: | The proposed model improves over existing models and can be used to familiarize researchers with the state of the art in the field. |
CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language (2026.acl-long)
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| Challenge: | CNSL-bench is the first comprehensive Chinese National Sign Language benchmark . current MLLMs are inferior to human performance, despite advances in multimodal modeling . |
| Approach: | They propose a Chinese National Sign Language benchmark to evaluate multimodal large language models in sign language understanding. |
| Outcome: | The proposed benchmark evaluates 21 open-source and proprietary MLLMs . results show that current models are inferior to human performance . |
CWSeg: An Efficient and General Approach to Chinese Word Segmentation (2023.acl-industry)
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| Challenge: | Existing methods for Chinese word segmentation have achieved state-of-the-art performance, but they pose challenges in the deployment. |
| Approach: | They propose to augment PLM-based Chinese word segmentation schemes by developing cohort training and versatile decoding strategies. |
| Outcome: | The proposed model can be used to augment existing PLM-based models and improve their performance on Chinese LLaMA and Alpaca datasets. |
Context-aware Information-theoretic Causal De-biasing for Interactive Sequence Labeling (2022.findings-emnlp)
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| Challenge: | Existing deep learning models for sequence labeling are expensive and time-consuming. |
| Approach: | They propose an interactive sequence labeling that allows training directly with the user feedback . they identify context and feedback biases by formulating interactive sequence labels via a Structural Causal Model. |
| Outcome: | The proposed approach can effectively alleviate the biases and can be learnt with the user feedback. |
CMR Scaling Law: Predicting Critical Mixture Ratios for Continual Pre-training of Language Models (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) excel in diverse tasks but often underperform in specialized fields due to limited domain-specific or proprietary corpus. |
| Approach: | They propose a power-law relationship between loss, mixture ratio, and training tokens scale and formalize the trade-off between general and domain-specific capabilities. |
| Outcome: | The proposed model achieves the desired domain transfer while maintaining general ability and highest utilization of available resources. |
LiveCANNBench: Benchmark SWE AI Coding for Ascend CANN (2026.findings-acl)
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Sijie Wang, Kai Zhao, Wee Peng Tay, Shuo Zhang, Chengwen Liu, Quanjiang Guo, Ren Junhao, Xin Li, Heng Lian, Jingdi Lei, Rui She, Huacan Wang, Ronghao Chen
| Challenge: | Recent advances in agents have enabled multi-file, multi-language, and dependency-aware AI coding. |
| Approach: | They propose an SWE-level benchmark for AI coding in the Huawei Ascend CANN software stack. |
| Outcome: | The proposed benchmark is constructed from real-world CANN repositories and consists of over 400 task instances spanning multiple file, multi-language, and execution-aware coding challenges. |
Treasures Outside Contexts: Improving Event Detection via Global Statistics (2021.emnlp-main)
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| Challenge: | Existing neural-based ED models are confused by changeable contexts during testing . we propose a system that extracts statistical event features from word-event cooccurrence frequencies . |
| Approach: | They propose to integrate a set of statistical event features from word-event co-occurrence frequencies into the training set to cooperate with contextual features. |
| Outcome: | The proposed model outperforms ten strong baselines on ACE2005 and KBP2015 datasets. |
Iterative Document Representation Learning Towards Summarization with Polishing (D18-1)
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| Challenge: | Existing summarization methods read through document only once to generate a document representation, resulting in a sub-optimal representation. |
| Approach: | They propose an iterative model for supervised extractive text summarization which polishes the document representation on many passes through the document. |
| Outcome: | The proposed model outperforms state-of-the-art extractive systems on CNN/DailyMail and DUC2002 datasets. |
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation Learning (2024.lrec-main)
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| Challenge: | Existing methods for data augmentation have not been well explored. |
| Approach: | They propose to use punctuation insertion, modal verbs, and double negation to produce diverse forms of sentences. |
| Outcome: | The proposed methods perform better on diverse datasets with semantic similarity and standard negation. |
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues (P19-1)
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| Challenge: | Currently, retrieval-based dialogues are performed in shallow ways . a recent study investigated the problem of context-response matching in open-domain . |
| Approach: | They propose a model that lets utterance-response interaction go deep by stacking interaction blocks. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three benchmark data sets. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations (D19-1)
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| Challenge: | In multi-party conversations, addressee information is not always explicit . researchers have spent great efforts to understand conversations between two participants, which is known as multi-part conversation. |
| Approach: | They propose a who-to-whom model which models users and utterances in a conversation session jointly in an interactive way. |
| Outcome: | The proposed model outperforms baseline models on the Ubuntu Multi-Party Conversation Corpus and shows consistent improvements. |
Sentence-Level Agreement for Neural Machine Translation (P19-1)
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| Challenge: | Empirical results show that a sentence-level agreement module can significantly improve the performance of neural machine translation (NMT) |
| Approach: | They propose a sentence-level agreement module to minimize the difference between the representation of source and target sentences. |
| Outcome: | Empirical results show the proposed agreement module significantly improves translation performance. |
Robust Tool Use via Fission-GRPO: Learning to Recover from Execution Errors (2026.acl-long)
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Zhiwei Zhang, Fei Zhao, Rui Wang, Zezhong Wang, Bin Liang, Jiakang Wang, Yao Hu, Shaosheng Cao, Kam-Fai Wong
| Challenge: | Large language models (LLMs) can call tools effectively, but they remain brittle in multi-turn execution. |
| Approach: | They propose a framework that converts execution errors into on-policy corrective supervision within the RL training loop. |
| Outcome: | The proposed framework improves the error recovery rate of Qwen3-8B by 5.7% absolute and overall accuracy by 4.0% on BFCL v4 Multi-Turn. |
P-FOLIO: Evaluating and Improving Logical Reasoning with Abundant Human-Written Reasoning Chains (2024.findings-emnlp)
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Simeng Han, Aaron Yu, Rui Shen, Zhenting Qi, Martin Riddell, Wenfei Zhou, Yujie Qiao, Yilun Zhao, Semih Yavuz, Ye Liu, Shafiq Joty, Yingbo Zhou, Caiming Xiong, Dragomir Radev, Rex Ying, Arman Cohan
| Challenge: | Existing methods on understanding the capabilities of LLMs in logical reasoning rely on binary entailment classification or synthetically derived rationales. |
| Approach: | They propose to annotate a human-annotated dataset consisting of diverse and complex reasoning chains for a set of realistic logical reasoning stories also written by humans. |
| Outcome: | The proposed model outperforms existing methods on understanding the capabilities of LLMs in logical reasoning by 10% or more. |
Dynamic Feature Fusion for Sign Language Translation Using HyperNetworks (2025.findings-naacl)
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| Challenge: | Using RGB and keypoint streams, sign language translation is highly dependent on the brain's ability to process color, shape, and motion simultaneously. |
| Approach: | They propose a hypernetwork-based fusion method that extracts salient features from RGB and keypoint streams and introduces self-distillation and SST contrastive learning to maintain feature advantages while aligning the global semantic space. |
| Outcome: | The proposed method achieves state-of-the-art performance on two public sign language datasets, reducing model parameters by about two-thirds. |
Improving Input-label Mapping with Demonstration Replay for In-context Learning (2023.findings-emnlp)
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| Challenge: | In-context learning (ICL) is an emerging capability of large autoregressive language models where a few demonstrations are appended to the input to enhance the model’s understanding of downstream NLP tasks without directly adjusting the model parameters. |
| Approach: | They propose a method where a few demonstrations are appended to the input to enhance the model's understanding of downstream NLP tasks without directly adjusting the model parameters. |
| Outcome: | The proposed method significantly improves the input-label mapping in ICL demonstrations. |
Selection and Generation: Learning towards Multi-Product Advertisement Post Generation (2020.emnlp-main)
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| Challenge: | E-commerce websites have billions of products, so it is impossible to write all copywriting manually. |
| Approach: | They propose a model to generate an AD post using a select network and a MGenNet network to generate a post including selected products. |
| Outcome: | The proposed model achieves impressive performance on a large-scale real-world AD post dataset. |
Mitigating the Alignment Tax of RLHF (2024.emnlp-main)
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Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang
| Challenge: | Large Language Models (LLMs) acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax. |
| Approach: | They propose to use a model averaging technique to find the most powerful alignment-forging Pareto front among RLHF algorithms. |
| Outcome: | The proposed method achieves the strongest alignment-forging Pareto front among competing methods. |
Deeply Coupled Cross-Modal Prompt Learning (2023.findings-acl)
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| Challenge: | Existing prompt-tuning methods focus on language branch or learn vision-language interaction in a shallow mechanism. |
| Approach: | They propose a Deeply coupled Cross-modal Prompt learning method based on CLIP to facilitate the interplay between vision and language with a Cross-Modal Prompting Attention mechanism. |
| Outcome: | The proposed method enables the interplay between vision and language with a Cross-Modal Prompt Attention mechanism. |
Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules (2024.emnlp-main)
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| Challenge: | Empirical results show that MoMs consistently outperform vanilla transformers . |
| Approach: | They propose an architecture that allows for a mixture-of-modules computation that uses a finite set of modules defined by multi-head attention and feed-forward networks. |
| Outcome: | The proposed architecture outperforms vanilla Transformers and their variants in multiple ways. |
To Copy Rather Than Memorize: A Vertical Learning Paradigm for Knowledge Graph Completion (2023.acl-long)
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Rui Li, Xu Chen, Chaozhuo Li, Yanming Shen, Jianan Zhao, Yujing Wang, Weihao Han, Hao Sun, Weiwei Deng, Qi Zhang, Xing Xie
| Challenge: | Existing methods for embedding knowledge graphs implicitly memorize relation rules to infer missing links, but they are difficult to memorize due to the inherent deficiencies of such implicit memorization strategy. |
| Approach: | They propose a vertical learning paradigm that allows to explicitly copy target information from related factual triples for more accurate prediction. |
| Outcome: | The proposed model improves generalization ability and makes distant link prediction significantly easier. |
MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data (2022.acl-long)
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| Challenge: | Existing benchmarks for numerical reasoning over hybrid data only include a single flat table in each document . |
| Approach: | They propose a new benchmark with QA pairs over multi hierarchical tabular and textual data. |
| Outcome: | The proposed model is more complex and challenging than existing benchmarks and is available on github . it uses facts retrieving to extract relevant facts from both tables and text and symbolic reasoning over retrieved facts. |
BioGen: Generating Biography Summary under Table Guidance on Wikipedia (2021.findings-acl)
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| Challenge: | Existing methods for summarizing text have not captured the salient information from an article. |
| Approach: | They propose a table-guided abstractive biography summarization that utilizes factual tables to capture important information and generate a summary of a biography. |
| Outcome: | The proposed method is the first large-scale biography summarization dataset with tables. |
ReasTAP: Injecting Table Reasoning Skills During Pre-training via Synthetic Reasoning Examples (2022.emnlp-main)
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| Challenge: | Existing models with table-specific architectures and pre-training methods perform well on understanding table structures but lack table reasoning skills. |
| Approach: | They propose to pre-train tables with table reasoning skills without complex architectures . they define 7 table reasoning skill, and then pre-teach them to generate tables . |
| Outcome: | The proposed model improves on four tasks and is available on github. |
Reward Difference Optimization For Sample Reweighting In Offline RLHF (2024.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) are becoming more capable, but their maximum likelihood objective for the next token prediction falls short in capturing such crucial human values. |
| Approach: | They propose a reward difference prediction method that uses reward difference coefficients to reweigh sample pairs in offline RLHF and a difference model that considers rich interactions between a pair of responses. |
| Outcome: | The proposed method is effective in both automatic metrics and human evaluation. |
Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems (P19-1)
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| Challenge: | Existing methods for learning a robust matching model from noisy training data are retrieval-based or generation-based. |
| Approach: | They propose a general co-teaching framework that learns matching models from noisy training data. |
| Outcome: | The proposed learning framework can improve existing models on two public data sets. |
Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety (2026.acl-long)
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Can Jin, Rui Wu, Tong Che, Qixin Zhang, Hongwu Peng, Jiahui Zhao, Zhenting Wang, Wenqi Wei, Ligong Han, Zhao Zhang, Yuan Cao, Ruixiang Tang, Dimitris N. Metaxas
| Challenge: | OpenAI introduces deliberative alignment (DA) to enhance safety of its o-series models, but effectiveness of this approach in open-source LLMs is understudied. |
| Approach: | They propose a case-augmented deliberative alignment method for large language models . they propose to use reinforcement learning on self-generated safety reasoning chains . |
| Outcome: | The proposed method avoids narrowly enumerated rules and allows broader adaptability. |
Logic Unveils Truth, While Disguise Obscures It: Transition Logic Augmented Response Selection for Multi-Turn Dialogue (2023.findings-emnlp)
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| Challenge: | Existing methods of negative samples tend to yield false negatives due to one-to-many property in open-domain dialogue. |
| Approach: | They propose a sequential variational ladder auto-encoder to capture one-to-many transition pattern of multiple characteristics in open-domain dialogue. |
| Outcome: | The proposed approach improves the performance of a retrieval dialogue system on two benchmarks. |
Model Surgery: Modulating LLM’s Behavior Via Simple Parameter Editing (2025.naacl-long)
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| Challenge: | Current approaches for detoxification or preventing jailbreaking involve fine-tuning billions of parameters through gradient descent with substantial computational cost. |
| Approach: | They propose to use supervised fine-tuning and Reinforcement Learning from human feedback to modify LLMs' behavior by directly editing a small subset of parameters. |
| Outcome: | Experiments show that editing a small subset of parameters can modulate specific behaviors of LLMs, such as detoxification and resistance to jailbreak, with only inference-level computational resources. |
CERES: Pretraining of Graph-Conditioned Transformer for Semi-Structured Session Data (2022.naacl-main)
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| Challenge: | Despite advances in self-supervised learning, there is a lack of models that can effectively capture both intra- and intra-item semantics for semi-structured session data. |
| Approach: | They propose a graph-based transformer model for semi-structured session data that captures both intra- and intra-item semantics. |
| Outcome: | The proposed model outperforms baselines in three session search and entity linking tasks by up to 9%. |
Multilingual Generation in Abstractive Summarization: A Comparative Study (2024.lrec-main)
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| Challenge: | Existing models for multilingual generation lack thorough analysis due to extensive linguistic diversity. |
| Approach: | They propose to classify multilingual generation methodologies into three categories based on their underlying modeling principles . they introduce an automatic metric to mitigate spurious correlations associated with language mixing . |
| Outcome: | The proposed model improves in high-resource, low-resourced, and zero-shot scenarios. |
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation. |
| Approach: | They propose a generic workflow for LLM-driven synthetic data generation. |
| Outcome: | The proposed workflows highlight gaps in existing research and outline avenues for future studies. |
Summarizing Procedural Text: Data and Approach (2022.findings-emnlp)
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| Challenge: | Procedural text summarization task is a popular task in the NLP field because of its long length and complexity. |
| Approach: | They propose a procedural text summarization task with two granularity . they propose an Entity-State Graph-based Summarizer (ESGS) which aggregates contextual information for each procedure. |
| Outcome: | The proposed model can summarize the entire procedural text or give an overview for each step or both . Experiments on two datasets confirm the proposed model's effectiveness. |
GNN-encoder: Learning a Dual-encoder Architecture via Graph Neural Networks for Dense Passage Retrieval (2022.findings-emnlp)
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| Challenge: | Existing approaches to perform large-scale query-passage retrieval are term-based, but they lose interaction between query-pastage pairs. |
| Approach: | They propose to fuse query (passage) information into query representations via graph neural networks that are constructed by queries and their top retrieved passages. |
| Outcome: | The proposed model outperforms existing models on MSMARCO, Natural Questions and TriviaQA datasets and achieves the new state-of-the-art on these datasets. |
Representation Purification for End-to-End Speech Translation (2025.coling-main)
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| Challenge: | Existing approaches to enhance speech translation focus on enhancing knowledge transfer . factors in speech that are not relevant to translation content, such as timbre and rhythm, often limit the efficiency of knowledge transfer. |
| Approach: | They propose a framework that excludes content-agnostic perturbations from speech representations to mitigate their negative impact on ST. |
| Outcome: | The proposed framework significantly improves translation performance across all translation directions in three settings and achieves preeminent performance under a *transcript-free* setting. |
Tree-KG: An Expandable Knowledge Graph Construction Framework for Knowledge-intensive Domains (2025.acl-long)
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| Challenge: | Knowledge graphs are a useful tool for organizing complex data in knowledge-intensive domains. |
| Approach: | They propose an expandable framework that combines structured domain texts with advanced semantic techniques to create a tree-like graph from textbooks. |
| Outcome: | The proposed framework surpasses competing methods in the text-Annotated dataset with high scores on the Text-Annalytated data. |
KnowledgeFMath: A Knowledge-Intensive Math Reasoning Dataset in Finance Domains (2024.acl-long)
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| Challenge: | Existing benchmarks for large language models (LLMs) are only 56.6% accurate, leaving room for improvement. |
| Approach: | They propose a benchmark to evaluate LLMs' capabilities in solving knowledge-intensive math reasoning problems using a finance-domain knowledge bank and expert-annotated solution references. |
| Outcome: | The proposed system achieves only 56.6% accuracy, leaving room for improvement. |
MiCRo: Mixture Modeling and Context-aware Routing for Personalized Preference Learning (2025.emnlp-main)
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| Challenge: | Existing reward models assume a global reward function, limiting personalization and pluralistic alignment. |
| Approach: | They propose a framework that leverages binary preference datasets to enhance personalized preference learning. |
| Outcome: | The proposed framework captures diverse human preferences without fine-grained annotations and significantly improves personalized preference learning on downstream tasks. |
An Empirical Study on Neural Keyphrase Generation (2021.naacl-main)
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| Challenge: | Recent years have seen a flourishing of neural keyphrase generation (KPG) works, including the release of several large-scale datasets and a host of new models to tackle them. |
| Approach: | They propose to compare the generalizability of KPG models with other models by analyzing the most crucial factors that may affect their generalizarability. |
| Outcome: | The proposed model can be used to predict keyphrases from a set of input sequences, and it can be compared with existing models. |
Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study (2021.naacl-main)
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| Challenge: | Existing approaches to encode natural languages without orders are lacking. |
| Approach: | They conduct a comprehensive analysis of the ability of neural models to organize sentences from a bag of words under three typical scenarios. |
| Outcome: | The proposed models can reorder or reconstruct sentences from a bag of words under three typical scenarios. |
Personalized Federated Learning for Text Classification with Gradient-Free Prompt Tuning (2024.findings-naacl)
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Rui Wang, Tong Yu, Ruiyi Zhang, Sungchul Kim, Ryan Rossi, Handong Zhao, Junda Wu, Subrata Mitra, Lina Yao, Ricardo Henao
| Challenge: | Pretrained language models (PLMs) are used for personalized federated learning . communication costs are high with large PLMs, and local training is expensive . |
| Approach: | They propose a framework for federated learning with pretrained language models . they propose 'discrete local search' and compression mechanism for local training . |
| Outcome: | The proposed framework achieves superior performance compared with baselines. |
Marrying Up Regular Expressions with Neural Networks: A Case Study for Spoken Language Understanding (P18-1)
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| Challenge: | Experimental results show that the combination of regular expressions and NNs improves learning effectiveness when a small number of training examples are available. |
| Approach: | They propose to combine a neural network (NN) with regular expressions (RE) to improve supervised learning for NLP by exploiting the rich expressiveness of REs at different levels within a NN. |
| Outcome: | The proposed approach significantly improves learning effectiveness when a small number of training examples are available. |
RePair: Automated Program Repair with Process-based Feedback (2024.findings-acl)
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| Challenge: | Commercial-scale language models (LMs) have taken APR to unprecedented levels, but they are limited by parameters and humans interact with them through explicit prompts. |
| Approach: | They propose a method that utilizes process supervision to improve program repair by allowing users to input feedback from compilers and test cases. |
| Outcome: | The proposed method outperforms large outcome-based generation methods and is inspired by strategies used in programming competitions. |
Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems (D19-1)
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| Challenge: | Existing studies focus on constructing a matching model with sophisticated neural architectures, but do little to how to effectively learn such architectures from data. |
| Approach: | They propose to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems by using four sampling strategies. |
| Outcome: | The proposed learning method improves the performance of matching models on two benchmarks with three matching models. |
AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge (2025.acl-long)
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Xiaobao Wu, Liangming Pan, Yuxi Xie, Ruiwen Zhou, Shuai Zhao, Yubo Ma, Mingzhe Du, Rui Mao, Anh Tuan Luu, William Yang Wang
| Challenge: | Existing studies solve this challenge by updating benchmarks with newly collected data, but they fail to guarantee contamination-free evaluation as the newly collected knowledge may contain pre-existing knowledge. |
| Approach: | They propose an automated anti-leakage benchmarking framework that builds and updates benchmarks without human labor instead of using newly collected data. |
| Outcome: | The proposed framework significantly reduces the cost of benchmark maintenance to accommodate emerging LLMs. |
Finding the Dominant Winning Ticket in Pre-Trained Language Models (2022.findings-acl)
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| Challenge: | Existing studies on pre-trained language models show that they can fine-tune parameters but achieve good downstream performance. |
| Approach: | They find that a dominant winning ticket takes up 0.05% of the parameters and is transferable across different tasks. |
| Outcome: | The proposed model can achieve comparable performance with the full-parameter model, the authors show . the dominant winning ticket takes up 0.05% of the parameters, and the model is transferable across tasks, they show - the authors conclude . |
CLEAR: Can Language Models Really Understand Causal Graphs? (2024.findings-emnlp)
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| Challenge: | Existing language models lack a conceptual framework for understanding causal graphs, but there is still potential for improvement. |
| Approach: | They develop a framework to define causal graph understanding by assessing language models’ behaviors through four practical criteria derived from diverse disciplines. |
| Outcome: | The proposed framework defines three complexity levels and encompasses 20 causal graph-based tasks across 20 different levels. |
Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure (2022.emnlp-main)
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| Challenge: | Existing models that use millions of parameters on massive data are inefficient and lack interpretability. |
| Approach: | They propose a model with a latent structure that is easily transferable from the general domain to downstream tasks in a lightweight and transparent way. |
| Outcome: | The proposed model performs better than four strong baseline models in terms of automatic and human evaluations and is 5x faster than the strongest baseline model. |
Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)
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| Challenge: | Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment. |
| Approach: | They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues. |
| Outcome: | The proposed model outperforms state-of-the-art methods in evaluation and human judgment. |
Dialogue Summarization with Static-Dynamic Structure Fusion Graph (2023.acl-long)
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| Challenge: | Dialogue summarization is a challenging task since it has dynamic interaction nature and inconsistent information flow among various speakers. |
| Approach: | They propose a Static-Dynamic graph-based Dialogue Summarization model which fuses prior knowledge from human expertise and adaptively learns the graph structure in an end-to-end learning fashion. |
| Outcome: | The proposed model can help people capture the highlights of a semi-structured and multi-participant dialogue without reviewing the complex dialogue context. |
Comprehensive Benchmarking of Long-Form Speech Generation in Diverse Scenarios (2026.findings-acl)
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Changhao Pan, Rui Yang, Han Wang, Zhuan Zhou, Xuming He, Wenxiang Guo, Ziyue Jiang, Ruiqi Li, Yu Zhang, Chenyuhao Wen, Ke Lei, Xiang Yin, Jingyu Lu, Zhiyuan Zhu, Zhou Zhao
| Challenge: | Existing evaluation benchmarks for long-form speech are limited to limited domains, creating a significant gap with the diverse downstream applications. |
| Approach: | They propose a benchmark that decomposes "long-form speech quality" into specific, disentangled dimensions. |
| Outcome: | The proposed benchmark decomposes “long-form speech quality” into specific, disentangled dimensions. |
Curr-ReFT: Overcoming Training Bottlenecks in Small-scale Vision-Language Models via Curriculum Reinforcement Finetuning (2025.findings-emnlp)
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| Challenge: | State-of-the-art vision-language models require massive scaling that limits practical deployment. |
| Approach: | They propose to use supervised fine-tuning to train small-scale vision-language models but face out-of-domain collapse when trained with traditional supervised learning (SFT). |
| Outcome: | Experiments show that curr-reFT achieves state-of-the-art performance across visual tasks in both in- and out-of domain settings and benchmarks. |
Reference Language based Unsupervised Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Existing approaches to use a common language as an auxiliary for better translation have a long tradition in machine translation. |
| Approach: | They propose a reference language-based framework for unsupervised neural machine translation that uses only one auxiliary language as an auxiliary for better translation. |
| Outcome: | The proposed framework improves the quality of pivot translation over a baseline that uses only one auxiliary language. |
Stylized Dialogue Generation with Feature-Guided Knowledge Augmentation (2023.findings-emnlp)
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| Challenge: | Existing methods synthesize pseudo data through back translation but lack guidance on target style features. |
| Approach: | They propose a knowledge-augmented stylized dialogue generation model with a feature-guided style knowledge selection module that utilizes context and response features. |
| Outcome: | The proposed model produces a satisfactory performance on two public benchmarks on both semantic and stylized levels. |
Fair Abstractive Summarization of Diverse Perspectives (2024.naacl-long)
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Yusen Zhang, Nan Zhang, Yixin Liu, Alexander Fabbri, Junru Liu, Ryo Kamoi, Xiaoxin Lu, Caiming Xiong, Jieyu Zhao, Dragomir Radev, Kathleen McKeown, Rui Zhang
| Challenge: | Existing work on summarization metrics and large language models has not explored fair abstractive summarizing. |
| Approach: | They propose four reference-free automatic metrics to measure the differences between target and source perspectives. |
| Outcome: | The proposed methods alleviate fair abstractive summarization on user-generated data. |
Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective Rewards (2024.acl-long)
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| Challenge: | Reinforcement Learning from Human Feedback (RLHF) relies on scalar rewards to capture user preferences. |
| Approach: | They propose a framework that integrates multi-objective reward modeling to represent diverse preference profiles. |
| Outcome: | The proposed method improves performance across reward objectives and targets. |
SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model (2024.emnlp-demo)
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Dayong Wu, Jiaqi Li, Baoxin Wang, Honghong Zhao, Siyuan Xue, Yanjie Yang, Zhijun Chang, Rui Zhang, Li Qian, Bo Wang, Shijin Wang, Zhixiong Zhang, Guoping Hu
| Challenge: | Large language models (LLMs) have shown remarkable achievements across various language tasks. |
| Approach: | They propose a scientific literature LLM and a knowledge service system based on it . they collect scientific literature and then pre-train it using autoregressive training . |
| Outcome: | The proposed system provides literature investigation, paper reading, and academic writing functions. |
Pruning as a Domain-specific LLM Extractor (2024.findings-naacl)
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Nan Zhang, Yanchi Liu, Xujiang Zhao, Wei Cheng, Runxue Bao, Rui Zhang, Prasenjit Mitra, Haifeng Chen
| Challenge: | Large Language Models (LLMs) have exhibited remarkable proficiency across a wide array of NLP tasks. |
| Approach: | They propose a method for pruning large language models using general or task-specific weights to extract a compressed, task-agnostic LLM. |
| Outcome: | The proposed method extracts a compressed, domain-specific, and task- agnostic LLM by identifying LLM weights that are pivotal for general capabilities, like linguistic capability and multi-task solving, and domain- specific knowledge. |
Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training (D18-1)
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| Challenge: | Existing models for automatic poetry generation lack term novelty and thematic consistency. |
| Approach: | They propose a conditional variational autoencoder with adversarial training for classical Chinese poem generation. |
| Outcome: | The proposed model outperforms existing models on a large poetry corpus on 'classical Chinese' . it generates poems with novel terms and learns their thematic consistency with their titles. |
Balancing Speciality and Versatility: a Coarse to Fine Framework for Supervised Fine-tuning Large Language Model (2024.findings-acl)
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| Challenge: | Aligned Large Language Models exhibit remarkable versatility, capable of handling diverse real-world tasks. |
| Approach: | They propose a coarse to fine framework to fine-tune aligned Large Language Models to achieve a balance between speciality and versatility. |
| Outcome: | The proposed framework outperforms baseline methods across diverse tasks and model scales. |
How to Represent Context Better? An Empirical Study on Context Modeling for Multi-turn Response Selection (2022.findings-emnlp)
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| Challenge: | Existing work on building a conversational system for open domain human-machine conversation is attracting more attention . early models concatenate all utterances or independently encode each dialogue turn, which may lead to an inadequate understanding of dialogue status. |
| Approach: | They propose to use a turn-aware context modeling layer to adapt existing models . they propose to model multi-turn contexts from the perspective of sequential relationship, local relationship, and query-alike manner . |
| Outcome: | The proposed method can be adapted to several advanced response selection models. |
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)
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Yilun Kong, Jingqing Ruan, YiHong Chen, Bin Zhang, Tianpeng Bao, Shi Shiwei, Du Qing, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, Xueqian Wang
| Challenge: | Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools. |
| Approach: | They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems. |
| Outcome: | The proposed framework enhances the task planning and tool usage abilities of LLM-based agents in industrial systems. |
Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling (2020.coling-main)
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| Challenge: | Existing models for machine translation and dialogue response generation require a large number of handcrafted features. |
| Approach: | They propose to interpret a general neural model comparatively by using the seq2seq model in two mainstream NLP tasks. |
| Outcome: | The proposed model is used in two mainstream NLP tasks and is compared with a standard model. |
Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation (2022.acl-long)
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Mingzhe Li, XieXiong Lin, Xiuying Chen, Jinxiong Chang, Qishen Zhang, Feng Wang, Taifeng Wang, Zhongyi Liu, Wei Chu, Dongyan Zhao, Rui Yan
| Challenge: | Existing studies focus on contrastive learning on the instance level without discriminating the contribution of each word. |
| Approach: | They propose a hierarchical contrastive learning mechanism which can unify semantic meaning in the input text. |
| Outcome: | The proposed model outperforms baselines on storytelling, paraphrasing, dialogue generation, and storytelling tasks. |
Modeling discourse cohesion for discourse parsing via memory network (P18-2)
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| Challenge: | Existing approaches to discourse parsing focus on studying the semantic and syntactic aspects of EDU pairs, but they do not address long span dependencies. |
| Approach: | They propose a new transition-based discourse parser that takes discourse cohesion into account by using memory networks. |
| Outcome: | The proposed method outperforms traditional features and improves performance on the RST discourse treebank. |
Exploring Recombination for Efficient Decoding of Neural Machine Translation (D18-1)
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| Challenge: | Neural Machine Translation (NMT) decoder captures features of entire prediction history . some partial hypotheses with different prefixes will be regarded differently no matter how similar they are . |
| Approach: | They propose a method that uses a n-gram suffix to adapt it to beam search decoding. |
| Outcome: | The proposed method can obtain similar translation quality with a smaller beam size, making it more efficient. |
QAEval: Mixture of Evaluators for Question-Answering Task Evaluation (2025.acl-long)
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| Challenge: | Existing QA evaluation methods struggle with open-ended and unstructured responses. |
| Approach: | They propose a hybrid framework that combines rule-based reliability with LLM-based adaptability to overcome these challenges. |
| Outcome: | The proposed framework outperforms existing models like GPT-4o and Claude-3 in accuracy and cost. |
Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders (D19-1)
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| Challenge: | Existing work on variableal autoencoders and waterstein autoencoding models has shown significant progress in open-domain response generation. |
| Approach: | They propose to embed user-level and utterance-level information into two multimodal distributions and combine them into a mixed distribution. |
| Outcome: | The proposed model outperforms state-of-the-art models on a large-scale real-world dataset. |
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training (2020.coling-main)
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| Challenge: | Unsupervised neural machine translation (UNMT) has attracted great interest in the machine translation community. |
| Approach: | They propose to explicitly take noisy data into consideration to improve the robustness of UNMT based systems. |
| Outcome: | The proposed methods significantly improved the robustness of the conventional UNMT systems in noisy scenarios. |
Towards Effective and Efficient Continual Pre-training of Large Language Models (2025.acl-long)
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Jie Chen, Zhipeng Chen, Jiapeng Wang, Kun Zhou, Yutao Zhu, Jinhao Jiang, Yingqian Min, Xin Zhao, Zhicheng Dou, Jiaxin Mao, Yankai Lin, Ruihua Song, Jun Xu, Xu Chen, Rui Yan, Zhewei Wei, Di Hu, Wenbing Huang, Ji-Rong Wen
| Challenge: | Continual pre-training (CPT) has been an important approach for adapting language models to specific domains or tasks. |
| Approach: | They propose a Continual pre-training method that can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
| Outcome: | The proposed method can greatly improve Chinese language ability and scientific reasoning ability of LLMs. |
ReAgent: Reversible Multi-Agent Reasoning for Knowledge-Enhanced Multi-Hop QA (2025.emnlp-main)
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Zhao Xinjie, Fan Gao, Xingyu Song, Yingjian Chen, Rui Yang, Yanran Fu, Yuyang Wang, Yusuke Iwasawa, Yutaka Matsuo, Irene Li
| Challenge: | Multi-hop question answering (QA) is a central challenge in natural language processing . early mistakes can cause errors and undermine the final result, authors say . |
| Approach: | They propose a reversible multi-agent reasoning framework that backtracks to earlier valid states when conflicts arise. |
| Outcome: | Empirical evaluation shows that the framework improves on forward-only benchmarks by 6% . the approach enables agents to backtrack to valid states when conflicts arise . |
RankCSE: Unsupervised Sentence Representations Learning via Learning to Rank (2023.acl-long)
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Jiduan Liu, Jiahao Liu, Qifan Wang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Kai Chen, Rui Yan
| Challenge: | Unsupervised sentence representation learning is one of the fundamental problems in natural language processing . contrastive learning methods fail to capture fine-grained ranking information among the sentences . |
| Approach: | They propose a novel approach for unsupervised sentence representation learning that integrates ranking consistency and ranking distillation with contrastive learning into a unified framework. |
| Outcome: | The proposed approach performs better over state-of-the-art models on STS and TR tasks. |
Integrating Transformer and Paraphrase Rules for Sentence Simplification (D18-1)
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| Challenge: | Current models for sentence simplification adopted ideas from machine translation studies and implicitly learned simplification mapping rules from normal-simple sentence pairs. |
| Approach: | They propose a novel model based on a multi-layer and multi-head attention architecture and two innovative approaches to integrate a paraphrase knowledge base for simplification. |
| Outcome: | The proposed model outperforms state-of-the-art models for sentence simplification . it seeks to select more accurate simplification rules, the authors show . |
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression (2023.findings-emnlp)
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| Challenge: | Large-scale pre-trained language models (LLMs) have demonstrated exceptional performance in various natural language processing (NLP) tasks. |
| Approach: | They propose a new compression paradigm that extracts knowledge from pre-trained language models to construct a knowledge store from which the model can leverage it for effective inference. |
| Outcome: | The proposed model extracts knowledge from LLMs to construct a knowledge store, which the model can leverage for effective inference. |
Beyond Modality Collapse: Taming Guided Modality Entropy for Omni-modal Emotion Reasoning (2026.findings-acl)
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| Challenge: | EmoOmni is a data paradigm for omni-modal large language models that can be used for emotion reasoning. |
| Approach: | They propose a data paradigm that interleaves guided tokens into reasoning traces to enforce structured evidence extraction. |
| Outcome: | The proposed paradigm over-relys on a dominant modality while neglecting complementary cues. |
RoBGuard: Enhancing LLMs to Assess Risk of Bias in Clinical Trial Documents (2025.coling-main)
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Changkai Ji, Bowen Zhao, Zhuoyao Wang, Yingwen Wang, Yuejie Zhang, Ying Cheng, Rui Feng, Xiaobo Zhang
| Challenge: | Existing approaches to assess the risk of bias in RCTs focus on manually crafted prompts and a restricted set of simple questions, limiting their accuracy and generalizability. |
| Approach: | They propose a framework for enhancing Large Language Models to assess the risk of bias in RCTs by reformulation, document parsing and multi-expert collaboration. |
| Outcome: | The proposed framework outperforms existing methods on the RoB-Item and RoB domains. |
HER: Human-like Reasoning and Reinforcement Learning for LLM Role-playing (2026.findings-acl)
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Chengyu Du, Xintao Wang, Aili Chen, Weiyuan Li, Rui Xu, Junteng Liu, Zishan Huang, Rong Tian, Zijun Sun, Yuhao Li, Liheng Feng, Deming Ding, Pengyu Zhao, Yanghua Xiao
| Challenge: | Existing models for LLM role-playing lack high-quality datasets with explicit reasoning traces and reliable reward signals aligned with human preferences. |
| Approach: | They propose a unified framework for cognitive-level persona simulation that strictly distinguishes characters’ first-person thinking processes from LLMs’ third-person reasoning. |
| Outcome: | The proposed framework outperforms the Qwen3-32B baseline model and achieves a 30.26% and 14.97% performance on the minimax benchmarks. |
STINMatch: Semi-Supervised Semantic-Topological Iteration Network for Financial Risk Detection via News Label Diffusion (2023.emnlp-main)
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Xurui Li, Yue Qin, Rui Zhu, Tianqianjin Lin, Yongming Fan, Yangyang Kang, Kaisong Song, Fubang Zhao, Changlong Sun, Haixu Tang, Xiaozhong Liu
| Challenge: | Commercial news provides rich semantics and timely information for automated financial risk detection. |
| Approach: | They propose a semi-supervised Semantic-Topological Iteration Network, STINMatch, along with a news-enterprise knowledge graph to endorse the risk detection enhancement. |
| Outcome: | The proposed model outperforms existing models in terms of generalization and semantics and annotation. |
R-Judge: Benchmarking Safety Risk Awareness for LLM Agents (2024.findings-emnlp)
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Tongxin Yuan, Zhiwei He, Lingzhong Dong, Yiming Wang, Ruijie Zhao, Tian Xia, Lizhen Xu, Binglin Zhou, Fangqi Li, Zhuosheng Zhang, Rui Wang, Gongshen Liu
| Challenge: | Large language models (LLMs) have shown compelling abilities in reasoning, decision-making, and instruction following. |
| Approach: | They propose a benchmark to evaluate the proficiency of large language models (LLMs) in judging and identifying safety risks given agent interaction records. |
| Outcome: | The proposed model outperforms the best-performing model, GPT-4o, while no other models significantly exceed the random. |
VMSMO: Learning to Generate Multimodal Summary for Video-based News Articles (2020.emnlp-main)
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| Challenge: | Existing studies show that multimodal news can significantly improve users' sense of satisfaction for informativeness. |
| Approach: | They propose a task of Video-based Multimodal Summarization with Multimodal Output to solve this problem. |
| Outcome: | The proposed method can generate multimodal summaries with a single input . it can model the temporal dependency of video with semantic meaning of article . |
Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References (P19-1)
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| Challenge: | Existing approaches to open-domain dialogue generation ignore the nature of 1-to-1 mapping that there may exist multiple valid responses corresponding to the same query. |
| Approach: | They propose to model open-domain dialogue generation using 1-to-1 mapping . they first extract common features of different responses and then combine them with distinctive features to generate multiple diverse and appropriate responses. |
| Outcome: | The proposed model outperforms existing models on automatic and human evaluations. |
Enhancing the Open-Domain Dialogue Evaluation in Latent Space (2021.findings-acl)
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| Challenge: | Existing methods to evaluate opendomain dialogues are limited due to the one-to-many nature of dialogues. |
| Approach: | They propose a self-supervised setting to obtain a smooth latent space that captures discourse-level context information and implicitly models more references in latent spaces. |
| Outcome: | The proposed method outperforms baseline methods on two real-world dialogue datasets. |
Learning to Express in Knowledge-Grounded Conversation (2022.naacl-main)
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| Challenge: | Existing models focus on synthesizing a dialogue with proper knowledge, but neglect that the same knowledge could be expressed differently even under the same context. |
| Approach: | They propose a model that ground dialogue generation by extra knowledge by analyzing the structure of the response and the content style of each part. |
| Outcome: | The proposed model can learn the structure style defined by a few examples and generate responses in desired content style. |
How to Write Summaries with Patterns? Learning towards Abstractive Summarization through Prototype Editing (D19-1)
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| Challenge: | Extensive experiments on a large-scale real-world text summarization dataset show that PESG achieves the state-of-the-art performance in terms of both automatic metrics and human evaluations. |
| Approach: | They propose a model that learns summary patterns and prototype facts from a prototype document . they use a fact checker to estimate mutual information between the input document and generated summary . |
| Outcome: | Experiments on a large-scale real-world text summarization dataset show that PESG achieves state-of-the-art performance. |
SpARK: An Embarrassingly Simple Sparse Watermarking in LLMs with Enhanced Text Quality (2026.findings-eacl)
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| Challenge: | Existing methods for detecting and monitoring generated text face a trade-off between the quality of the generated text and the effectiveness of the watermarking process. |
| Approach: | They propose a new type of LLM watermark, Sparse WatermARK, which uses watermarks to a small subset of generated tokens distributed across the text. |
| Outcome: | The proposed method outperforms existing methods in detectability and quality while maintaining generated text quality. |
Semi-supervised Text Style Transfer: Cross Projection in Latent Space (D19-1)
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| Challenge: | Text style transfer task has long suffered from the shortage of parallel data . |
| Approach: | They propose a semi-supervised text style transfer model that combines parallel data with large-scale nonparallel data to train it. |
| Outcome: | The proposed model can transfer a sentence of one style to another while retaining its original content meaning while preserving its original meaning. |
SCALE: Synergized Collaboration of Asymmetric Language Translation Engines (2024.findings-acl)
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| Challenge: | In this paper, we introduce SCALE, a collaborative framework that connects a compact Specialized Translation Model (STM) and a general-purpose Large Language Model (LLM) as one unified translation engine. |
| Approach: | They propose a collaborative framework that connects a Specialized Translation Model (STM) and a general-purpose Large Language Model (LLM) as one unified translation engine. |
| Outcome: | The proposed framework outperforms both LLMs and supervised models in high-resource or challenging low-resourced settings. |
Few-Shot Class-Incremental Learning for Named Entity Recognition (2022.acl-long)
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| Challenge: | Existing models of Named Entity Recognition (NER) are trained on large datasets with predefined entity classes, but data of new classes arrives constantly. Existing work on NER relies on the assumption that there exists abundance of labeled data for the training of new class. |
| Approach: | They propose a few-shot class-incremental learning problem where NER model is trained with only few labeled samples of the new classes without forgetting knowledge of the old ones. |
| Outcome: | The proposed model improves over existing baselines by reconstructing training data of old classes and real data from the training set. |
PACAR: Automated Fact-Checking with Planning and Customized Action Reasoning Using Large Language Models (2024.lrec-main)
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| Challenge: | Existing studies rely on idealized "gold" evidence for predictions, which is unrealistic due to its limited availability in real-world scenarios. |
| Approach: | They propose a fact-checking framework based on planning and customized action reasoning using LLMs. |
| Outcome: | The proposed framework outperforms baseline methods across three datasets and with varying complexity levels. |
Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation (2020.acl-main)
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| Challenge: | Unsupervised neural machine translation (UNMT) can only translate between a single language pair and cannot produce translation results for multiple language pairs at the same time. |
| Approach: | They propose a method to translate between 13 languages using a single encoder and a decoder . they propose two knowledge distillation methods to further enhance multilingual UNMT performance . |
| Outcome: | The proposed method improves translation performance for all languages using multilingual data. |
There Is No Standard Answer: Knowledge-Grounded Dialogue Generation with Adversarial Activated Multi-Reference Learning (2022.emnlp-main)
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| Challenge: | Existing methods emphasize selecting one golden knowledge given a particular dialogue context, overlooking the one-to-many phenomenon in dialogue. |
| Approach: | They propose to use a multi-reference dataset to assess the one-to-many efficacy of existing KGC models. |
| Outcome: | The proposed model improves the mapping relationship between multiple knowledge and multiple responses by optimizing the model in a wake-sleep style. |
Cross-Domain Data Augmentation with Domain-Adaptive Language Modeling for Aspect-Based Sentiment Analysis (2023.acl-long)
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| Challenge: | Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to identify aspect-sentiment pairs in sentences from a target domain. |
| Approach: | They propose a domain-adaptive language model to generate labeled data from a source domain. |
| Outcome: | The proposed approach outperforms existing methods on ABSA and Aspect Extraction tasks. |
Syntax in End-to-End Natural Language Processing (2021.emnlp-tutorials)
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| Challenge: | tutorial focuses on syntactic parsing and syntax in end-to-end natural language processing (NLP) tasks. |
| Approach: | tutorial will introduce syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks. |
| Outcome: | This tutorial will introduce the background and the latest progress of syntactic parsing and SRL/NMT. |
Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios (2021.naacl-main)
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| Challenge: | Existing methods that use monolingual corpora for translation are not suitable for low-resource languages such as Estonian. |
| Approach: | They propose unsupervised neural machine translation (UNMT) that relies on monolingual corpora to train a robust UNMT system and improve its performance. |
| Outcome: | The proposed methods outperform conventional UNMT systems on several language pairs. |
PreQuant: A Task-agnostic Quantization Approach for Pre-trained Language Models (2023.findings-acl)
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Zhuocheng Gong, Jiahao Liu, Qifan Wang, Yang Yang, Jingang Wang, Wei Wu, Yunsen Xian, Dongyan Zhao, Rui Yan
| Challenge: | Quantization is a viable solution for pre-trained language models, but most existing methods are task-specific and require customized training and quantization with a large number of trainable parameters. |
| Approach: | They propose a "quantize before fine-tuning" framework that allows for quantization with a large number of trainable parameters on each individual task. |
| Outcome: | The proposed framework is compatible with quantization-aware training and post-training quantization and corrects quantization errors. |
SORTIE: Dependency-Aware Symbolic Reasoning for Logical Data-to-text Generation (2023.findings-acl)
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| Challenge: | Existing studies on logical data-to-text generation rely on neural language models to generate the final table description, but they have difficulty working out key entities in the description. |
| Approach: | They propose a symbolic reasoning framework that reasons out each entity in the table description with a table-compatible programming language. |
| Outcome: | The proposed framework outperforms existing methods on three datasets and three backbones with an absolute improvement of 5.7%11.5% on SP-Acc. |
Selective Contrastive Learning For Gloss Free Sign Language Translation (2026.acl-long)
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| Challenge: | Recent SLT systems adopt CLIP-like Vision-Language pretraining, but the random in-batch contrast provides few, batch-dependent negatives. |
| Approach: | They propose a method to train sign video-text similarity over a time period of 3 months . they use a random in-batch contrast strategy to track negative video- text similarity . |
| Outcome: | The proposed system improves sign language translation by focusing on challenging negatives . the results show that the random in-batch contrast provides few negatives and noisy supervision . |
Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation (D19-1)
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Zhangming Chan, Xiuying Chen, Yongliang Wang, Juntao Li, Zhiqiang Zhang, Kun Gai, Dongyan Zhao, Rui Yan
| Challenge: | Existing models for product description generation do not take the product attribute information into account. |
| Approach: | They propose a model that takes the embedding and the entity label of each word into account . they establish a keyword memory that stores the entity labels as keys and keywords as values . |
| Outcome: | The proposed model increases the fidelity of the generated descriptions by 25%. |
Learning Relation Alignment for Calibrated Cross-modal Retrieval (2021.acl-long)
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| Challenge: | despite advances in multimodal pre-training, cross-modal retrieval remains challenging . lack of relation consistency impairs contextualized representation of image-text pairs . |
| Approach: | They propose a new metric to quantify the relation consistency by measuring the semantic distance between linguistic and visual relations. |
| Outcome: | The proposed method boosts the performance of prevailing models on Flickr30k and MS COCO datasets by a considerable margin. |
High-order Semantic Role Labeling (2020.findings-emnlp)
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| Challenge: | Experimental results show that high-order structural learning techniques are beneficial to SRL models . high-level features and structure learning are not common in deep neural networks . |
| Approach: | They propose a high-order graph structure for a neural semantic role labeling model . it explicitly considers the isolated predicate-argument pairs and interaction between them . |
| Outcome: | The proposed model can explicitly consider the isolated predicate-argument pairs and the interaction between the predicates-argoments pairs. |
Loose lips sink ships: Mitigating Length Bias in Reinforcement Learning from Human Feedback (2023.findings-emnlp)
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| Challenge: | Experimental results prove that language models can learn from human feedback better, irrespective of sequence length . emergence of length bias often induces the model to favor longer outputs . |
| Approach: | They propose to separate reward modeling from the influence of sequence length by using the Product-of-Experts technique. |
| Outcome: | The proposed approach shows that language models perform better regardless of sequence length . the main expert is focused on understanding human intents, while the biased expert targets the identification and capture of length bias. |
Federated Domain Adaptation for Named Entity Recognition via Distilling with Heterogeneous Tag Sets (2023.findings-acl)
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| Challenge: | a federated domain adaptation approach is used to learn with NER datasets from multiple platforms while not violating data privacy. |
| Approach: | They propose to use a distillation approach to facilitate knowledge transfer across platforms. |
| Outcome: | The proposed model performs better in the clinic domain. |
Can an Individual Manipulate the Collective Decisions of Multi-Agents? (2025.emnlp-main)
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| Challenge: | Recent studies show that coordinated multi-agent systems exhibit enhanced decision-making and reasoning abilities through collaboration. |
| Approach: | They propose a framework that simulates agent interactions within a multi-agent system to generate adversarial samples and use them to manipulate the target agent in the target system. |
| Outcome: | The proposed framework generates adversarial samples that are used to manipulate the target agent in the target system, misleading the system’s decision-making process. |
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation (P19-1)
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| Challenge: | Unsupervised bilingual word embedding (UBWE) has helped unsupervised neural machine translation (UNMT) achieve remarkable results in several language pairs. |
| Approach: | They propose two methods that train UNMT with UBWE agreement . they propose to use UBwe to initialize word embedding in UNMT . |
| Outcome: | The proposed methods outperform conventional methods on several language pairs. |
What Makes Pre-trained Language Models Better Zero-shot Learners? (2023.acl-long)
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| Challenge: | Current methods for prompt learning in zero-shot scenarios rely on a development set with sufficient human-annotated data to select the best-performing prompt template. |
| Approach: | They propose a method for screening reasonable prompt templates in zero-shot text classification using language discrepancy. |
| Outcome: | The proposed method improves prediction performance in a realistic zero-shot setting, eliminating the need for labelled examples. |
FedDQC: Data Quality Control in Federated Instruction-tuning of Large Language Models (2025.findings-acl)
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| Challenge: | Federated Learning (FL) enables privacy-preserving collaborative instruction tuning of large language models. |
| Approach: | They propose a federated instruction tuning framework with dynamic data quality control to solve this problem. |
| Outcome: | The proposed framework improves performance on mixed-quality datasets on synthetic and real-world datasets. |
Large Language Models are Complex Table Parsers (2023.emnlp-main)
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| Challenge: | Extensive experiments and results on Complex Table QA datasets, i.e., the open-domain dataset HiTAB and the aviation domain dataset AIT-QA show that our approach significantly outperforms previous work on both datasets. |
| Approach: | They propose to incorporate Generative Pre-trained Transformer 3.5 to address the specific challenges posed by Complex Table QA by reconstructing tables into tuples and using prompt templates to create dialogues. |
| Outcome: | The proposed approach outperforms previous work on complex table parsing datasets and leads to state-of-the-art (SOTA) performance. |
ProphetChat: Enhancing Dialogue Generation with Simulation of Future Conversation (2022.acl-long)
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| Challenge: | generative dialogue models use dialogue histories to generate the response . however, generating a response based on the historical information is not easy . |
| Approach: | They propose a framework that utilizes simulated dialogue futures to enhance response generation. |
| Outcome: | The proposed framework can generate better responses over strong baselines on two open-domain dialogue datasets. |
Signer Diversity-driven Data Augmentation for Signer-Independent Sign Language Translation (2024.findings-naacl)
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| Challenge: | Existing methods for sign language translation (SLT) rely on signer identity labels, which is often impractical and costly in real-world applications. |
| Approach: | They propose a signer diversity-driven data augmentation method that can generalize to signers not encountered during training. |
| Outcome: | The proposed method achieves state-of-the-art results without relying on signer identity labels. |
On the Compositional Generalization in Versatile Open-domain Dialogue (2023.acl-long)
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| Challenge: | Existing approaches to multi-task learning suffer from interference among datasets or fail to effectively reuse knowledge and skills learned from other datasets. |
| Approach: | They propose a sparsely activated modular network with a well-rounded set of operators and instantiate each operator with an independent module. |
| Outcome: | The proposed model outperforms state-of-the-art supervised approaches on 4 datasets with only 10% training data thanks to the modular architecture and multi-task learning. |
SSP: Self-Supervised Post-training for Conversational Search (2023.findings-acl)
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| Challenge: | Existing methods to search for information are limited by data scarcity and lack contextual semantic understanding. |
| Approach: | They propose a new post-training paradigm with three self-supervised tasks to initialize the conversational search model to enhance dialogue structure and contextual semantic understanding. |
| Outcome: | The proposed method can initialize the conversational search model to enhance dialogue structure and contextual semantic understanding. |
TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing (2021.acl-demo)
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Xiao Wang, Qin Liu, Tao Gui, Qi Zhang, Yicheng Zou, Xin Zhou, Jiacheng Ye, Yongxin Zhang, Rui Zheng, Zexiong Pang, Qinzhuo Wu, Zhengyan Li, Chong Zhang, Ruotian Ma, Zichu Fei, Ruijian Cai, Jun Zhao, Xingwu Hu, Zhiheng Yan, Yiding Tan, Yuan Hu, Qiyuan Bian, Zhihua Liu, Shan Qin, Bolin Zhu, Xiaoyu Xing, Jinlan Fu, Yue Zhang, Minlong Peng, Xiaoqing Zheng, Yaqian Zhou, Zhongyu Wei, Xipeng Qiu, Xuanjing Huang
| Challenge: | Existing approaches to textual robustness evaluation focus on slightly modifying the input data, which maintains the original meaning and results in a different prediction. |
| Approach: | They propose a multilingual robustness evaluation toolkit for NLP that integrates universal text transformations, task-specific transformations and adversarial attack. |
| Outcome: | The toolkit includes universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. |
Retrieval-Augmented Multilingual Keyphrase Generation with Retriever-Generator Iterative Training (2022.findings-naacl)
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| Challenge: | Existing studies on keyphrase generation on non-English languages haven’t been vastly investigated. |
| Approach: | They propose a retrieval-augmented method for multilingual keyphrase generation that leverages keyphrase annotations in English datasets to facilitate generating keyphrases in low-resource languages. |
| Outcome: | The proposed model outperforms baselines on non-English keyphrase generation datasets and the proposed model is scalable. |
Advances and Challenges in Unsupervised Neural Machine Translation (2021.eacl-tutorials)
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| Challenge: | Unsupervised neural machine translation (UNMT) has achieved impressive results, but there are still several challenges for the technology. |
| Approach: | They present a framework for unsupervised neural machine translation (UNMT) they examine the latest progress and challenges of UNMT and examine how it holds up . |
| Outcome: | The proposed method has achieved impressive results but still faces challenges. |
Detecting What Queries Seek: Steering LLM Safety with FFN Output Activation Monitoring (2026.acl-long)
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| Challenge: | Existing methods for detecting malicious queries rely on residual stream activations, resulting in limited discriminative power and unreliable interventions. |
| Approach: | They propose to use feed-forward networks (FFNs) to generate more discriminative signals for intervention, since these activations more explicitly reflect the intent of a query. |
| Outcome: | Experiments show that the proposed approach achieves state-of-the-art defense performance against various jailbreak attacks while maintaining the model's original performance on benign tasks. |
MetaBench: A Multi-task Benchmark for Assessing LLMs in Metabolomics (2026.acl-long)
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Yuxing Lu, Xukai Zhao, J. Ben Tamo, Micky C. Nnamdi, Rui Peng, Shuang Zeng, Xingyu Hu, Jinzhuo Wang, May Dongmei Wang
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities on general text, but their proficiency in specialized scientific domains remains uncharacterized. |
| Approach: | They evaluate the capabilities of large language models in metabolomics research using MetaBench . they found that models perform well on text generation tasks, but cross-database identifier grounding remains challenging . |
| Outcome: | The evaluation of 25 open- and closed-source LLMs reveals distinct performance patterns across metabolomics tasks. |