Papers by Yuwei Wang
Retrieval Augmentation for Commonsense Reasoning: A Unified Approach (2022.emnlp-main)
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| Challenge: | Existing methods for retrieving encyclopedic knowledge lack a large corpus and effective commonsense retriever. |
| Approach: | They propose a framework for retrieval-augmented commonsense reasoning with a large commonsensense corpus and a commonseense retriever. |
| Outcome: | The proposed framework outperforms existing methods on commonsense reasoning tasks. |
Beyond Token Length: Step Pruner for Efficient and Accurate Reasoning in Large Language Models (2026.findings-acl)
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| Challenge: | Existing reinforcement learning methods for large reasoning models suffer from excessive verbosity, known as "overthinking." Existing models penalize generated tokens to promote conciseness, but these methods encounter two challenges: they may develop hacking behavior in later stages of training by discarding reasoning steps. |
| Approach: | They propose a framework that steers large reasoning models toward more efficient reasoning . they prioritize correctness while imposing penalties for redundant steps . |
| Outcome: | The proposed framework reduces token usage by 69.7% on AIME24. |
ZoFia: Zero-Shot Fake News Detection with Entity-Guided Retrieval and Multi-LLM Interaction (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are limited by knowledge cutoff and can generate factual hallucinations when handling time-sensitive news. |
| Approach: | They propose a two-stage zero-shot fake news detection framework that uses a hierarchical salience and saliency-calibrated minimum margin of relevance algorithm to extract core entities accurately. |
| Outcome: | The proposed framework outperforms existing zero-shot baselines and even most few-shot methods on two public datasets. |
LightningDOT: Pre-training Visual-Semantic Embeddings for Real-Time Image-Text Retrieval (2021.naacl-main)
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| Challenge: | Existing pre-trained models suffer from slow inference speed due to cross-modal attention in transformer architecture. |
| Approach: | They propose a multimodal approach that accelerates the inference time of ITR by thousands of times . they extract pre-cached feature indexes offline and employ instant dot-product matching online . |
| Outcome: | The proposed approach outperforms existing models that consume 1000 times magnitude of computational hours using the same features. |
Cluster-Former: Clustering-based Sparse Transformer for Question Answering (2021.findings-acl)
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| Challenge: | Existing models for encoding long sequences in deep learning suffer from high latency and memory demands. |
| Approach: | They propose a clustering-based sparse Transformer framework to perform attention across chunked sequences. |
| Outcome: | The proposed framework achieves state-of-the-art on several major QA benchmarks. |
A Slot Is Not Built in One Utterance: Spoken Language Dialogs with Sub-Slots (2022.findings-acl)
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| Challenge: | Sub-Slot based task-oriented dialogs provide slot values segment by segment over multiple turns. |
| Approach: | They define a task called Sub-Slot based Task-Oriented Dialog (SSTOD) they build a Chinese dialog dataset SSD for boosting research on SSTOD. |
| Outcome: | The proposed task is called Sub-Slot based Task-Oriented Dialog (SSTOD) it includes 40K dialogs and 500K utterances from Chinese names, phone numbers, ID numbers and license plate numbers . the dataset is well annotated with sub-slot values, slot values, dialog states and actions . |
MultiCAT: Multimodal Communication Annotations for Teams (2025.findings-naacl)
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Adarsh Pyarelal, John M Culnan, Ayesha Qamar, Meghavarshini Krishnaswamy, Yuwei Wang, Cheonkam Jeong, Chen Chen, Md Messal Monem Miah, Shahriar Hormozi, Jonathan Tong, Ruihong Huang
| Challenge: | Recent flagship models from OpenAI and Google are only capable of 1-on-1 interactions with humans, limiting the potential for integration into human-machine teams of the future. |
| Approach: | They propose a dataset that allows team members to make multiple types of predictions on the same dataset. |
| Outcome: | The proposed dataset builds upon data from teams working collaboratively to save victims in a simulated search and rescue mission. |
ClusterLLM: Large Language Models as a Guide for Text Clustering (2023.emnlp-main)
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| Challenge: | Extensive experiments on 14 datasets show that ClusterLLM consistently improves clustering quality, at an average cost of $0.6 per dataset. |
| Approach: | They propose a text clustering framework that leverages feedback from an instruction-tuned large language model, such as ChatGPT. |
| Outcome: | Extensive experiments on 14 datasets show that ClusterLLM consistently improves clustering quality, at an average cost of $0.6 per dataset. |
Contrastive Distillation on Intermediate Representations for Language Model Compression (2020.emnlp-main)
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| Challenge: | Existing methods to compress language models use a simple L_2 loss to distill knowledge in the intermediate representations of a large BERT model to a smaller one. |
| Approach: | They propose a method that uses knowledge distillation to distill knowledge through intermediate layers of the teacher via a contrastive objective. |
| Outcome: | The proposed method outperforms state-of-the-art methods on the GLUE benchmark. |
GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation (2025.findings-emnlp)
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Wen Ye, Zhaocheng Liu, Gui Yuwei, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang
| Challenge: | Existing methods for text-to-image synthesis lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness. |
| Approach: | They propose a plug-and-play multi-agent system called GenPilot that integrates error analysis, clustering-based adaptive exploration, fine-grained verification and a memory module for iterative optimization. |
| Outcome: | The proposed method improves text consistency and structural coherence on images with a plug-and-play system. |
Chain-of-History Reasoning for Temporal Knowledge Graph Forecasting (2024.findings-acl)
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| Challenge: | Existing graph-based models excel at capturing structural information within TKGs but lack semantic comprehension abilities. |
| Approach: | They propose a plug-and-play module to enhance the performance of graph-based TKG models by exploring high-order histories step-by-step. |
| Outcome: | Experiments on three datasets and backbones show that CoH is effective in capturing high-order historical information for LLMs. |
Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition (2026.findings-acl)
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| Challenge: | Existing approaches to GMNER use MLLMs as auxiliary tools, causing cumulative error propagation and a lack of rigorous cross-modal verification. |
| Approach: | They propose a model that enforces structured cross-modal reasoning through Multi-style Reasoning Schema Injection and Constraint-guided Verifiable Optimization. |
| Outcome: | The proposed model enforces structured cross-modal reasoning through multi-style Reasoning Schema Injection and Constraint-guided Verifiable Optimization. |
Diagnosing and Remedying Representation Deficiencies for Deterministic Reasoning in KGQA (2026.acl-long)
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| Challenge: | Existing approaches to solve non-deterministic reasoning problems in large language models are limited by their complexity and lack of a clear understanding of the problem. |
| Approach: | They propose a method to diagnose and correct non-deterministic reasoning behaviors in large language models. |
| Outcome: | The proposed method outperforms baselines and WebQSP benchmarks on the widely used WebQ SP and CWQ benchmarks. |
Cross-Thought for Sentence Encoder Pre-training (2020.emnlp-main)
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| Challenge: | Existing models to pretrain sentence encoders with large unlabeled corpus are lacking in linguistic information retrieval. |
| Approach: | They propose a novel approach to pre-training sequence encoder using transformers . they propose to train a Transformer-based sequence encoded over a large set of short sequences based on a set of masked words . |
| Outcome: | The proposed approach outperforms state-of-the-art encoders on hotpotQA by improving intermediate information retrieval performance. |
VIMI: Grounding Video Generation through Multi-modal Instruction (2024.emnlp-main)
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Yuwei Fang, Willi Menapace, Aliaksandr Siarohin, Tsai-Shien Chen, Kuan-Chieh Wang, Ivan Skorokhodov, Graham Neubig, Sergey Tulyakov
| Challenge: | Existing text-to-video diffusion models rely on text-only encoders for their pretraining, restricting their versatility and application in multimodal integration. |
| Approach: | They propose a multimodal conditional video generation framework for pretraining on augmented text prompts and then utilize a two-stage training strategy to enable diverse video generation tasks within a model. |
| Outcome: | The proposed model can synthesize consistent and temporally coherent videos with large motion while retaining the semantic control. |
LoPT: Lossless Parallel Tokenization Acceleration for Long Context Inference of Large Language Model (2026.acl-long)
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| Challenge: | Existing parallel tokenization methods suffer from inconsistent results due to boundary artifacts that occur after merging. |
| Approach: | They propose a Lossless Parallel Tokenization framework that ensures output identical to standard sequential tokenization. |
| Outcome: | The proposed method achieves significant speedup while guaranteeing lossless tokenization. |
LLM as Prompter: Low-resource Inductive Reasoning on Arbitrary Knowledge Graphs (2024.findings-acl)
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| Challenge: | Knowledge Graph (KG) inductive reasoning is widely adopted in various applications. |
| Approach: | They propose a framework for low-resource inductive reasoning using Large Language Models to generate a graph-structural prompt for pre-trained KGs. |
| Outcome: | The proposed framework outperforms previous methods in three-shot, one-shot and zero-shot reasoning tasks. |
Leveraging Knowledge in Multilingual Commonsense Reasoning (2022.findings-acl)
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| Challenge: | Commonsense reasoning is a language-agnostic process, but most comprehensive knowledge sources are limited to a small number of languages, especially English. |
| Approach: | They propose to use English as a pivot language to integrate commonsense reasoning into models using a translate-retrieve-translate strategy. |
| Outcome: | The proposed model outperforms the state-of-the-art on the XCSR benchmarks. |
Learning Latent Relations for Temporal Knowledge Graph Reasoning (2023.acl-long)
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| Challenge: | Existing methods for Temporal Knowledge Graph reasoning capture intra- and inter-time latent relations between entities that appear at different times. |
| Approach: | They propose a Latent relations Learning method for TKG reasoning that captures latent relations between entities at different times. |
| Outcome: | The proposed method exploits the intra- and inter-time latent relations of entities at different times. |
FinMRAGBench: A Realistic and Complex Benchmark for Multi-Modal RAG in Financial Document Analysis (2026.findings-acl)
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Shouqing Yang, Qi Zhang, Yuhang Yang, Ruikang Xu, Yuwei Hou, Zhulin Jia, Lirong Gao, Haobo Wang, Jinglei Chen, Jiexiang Wang, Sheng Guo, Bo Zheng, Gang Chen
| Challenge: | Existing benchmarks for realistic financial analysis fail to capture realistic financial situations involving cross-document retrieval, multi-page evidence integration, and diverse analytical tasks. |
| Approach: | They propose a multi-modal financial RAG benchmark that evaluates large language models in realistic financial analysis settings. |
| Outcome: | The proposed framework achieves the strongest overall performance across all models. |
Hierarchical Graph Network for Multi-hop Question Answering (2020.emnlp-main)
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| Challenge: | Existing multi-hop question answering models focus on multi-level reasoning across multiple documents or paragraphs. |
| Approach: | They propose a hierarchical graph network that aggregates clues from scattered texts . they use a set of contextual encoders to initialize nodes on different levels of granularity . |
| Outcome: | The proposed model outperforms existing multi-hop QA approaches on the HotpotQA benchmark. |
m3P: Towards Multimodal Multilingual Translation with Multimodal Prompt (2024.lrec-main)
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Jian Yang, Hongcheng Guo, Yuwei Yin, Jiaqi Bai, Bing Wang, Jiaheng Liu, Xinnian Liang, LinZheng Chai, Liqun Yang, Zhoujun Li
| Challenge: | Existing multimodal neural machine translation models focus on bilingual translation, but experimental results show that they outperform the text-only baselines and multilingual multimodal methods by a large margin. |
| Approach: | They propose a framework to leverage the multimodal prompt to guide the Multimodal Multilingual Neural Machine Translation (m3P) this framework aligns the representations of different languages with the same meaning and generates the conditional vision-language memory for translation. |
| Outcome: | The proposed framework outperforms previous text-only baselines and multilingual multimodal methods by a large margin. |
UniCoder: Scaling Code Large Language Model via Universal Code (2024.acl-long)
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Tao Sun, Linzheng Chai, Jian Yang, Yuwei Yin, Hongcheng Guo, Jiaheng Liu, Bing Wang, Liqun Yang, Zhoujun Li
| Challenge: | Experimental results show that UniCoder with the universal code significantly outperforms the previous prompting methods by a large margin. |
| Approach: | They introduce the universal code (UniCode) as the intermediate representation of algorithm steps using conventions of programming languages. |
| Outcome: | The proposed model outperforms previous prompting methods by a large margin . the proposed model is based on a dataset of natural-language questions and code solutions . |
Dict-BERT: Enhancing Language Model Pre-training with Dictionary (2022.findings-acl)
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Wenhao Yu, Chenguang Zhu, Yuwei Fang, Donghan Yu, Shuohang Wang, Yichong Xu, Michael Zeng, Meng Jiang
| Challenge: | Pre-trained language models (PLMs) capture word semantics in different contexts, hence the embeddings of rare words on the tail are poorly optimized. |
| Approach: | They propose to leverage definitions of rare words in dictionaries to enhance language model pre-training by leveraging dictionary definitions. |
| Outcome: | The proposed model improves understanding of rare words and boosts performance on various NLP downstream tasks. |
LSEG: A Fine-tuning Free Method for NL2FOL via Logic-Structure and Entropy Guided Inference Controlling (2026.findings-acl)
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| Challenge: | Large language models struggle with natural language to first order logic (NL2FOL) translation due to logical hallucination. |
| Approach: | They propose a fine-tuning free framework to correct hidden state deviation by leveraging logical stability across logic preserving perturbations of the input. |
| Outcome: | The proposed framework improves logical consistency during inference and improves accuracy over baselines. |
KG-FiD: Infusing Knowledge Graph in Fusion-in-Decoder for Open-Domain Question Answering (2022.acl-long)
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Donghan Yu, Chenguang Zhu, Yuwei Fang, Wenhao Yu, Shuohang Wang, Yichong Xu, Xiang Ren, Yiming Yang, Michael Zeng
| Challenge: | Open-Domain Question Answering (ODQA) models typically include a retrieving module and a reading module. |
| Approach: | They propose a new open-domain question-answering framework that uses a knowledge-enhanced version of FiD to improve the approach. |
| Outcome: | The proposed model improves on ODQA benchmark datasets with less than 40% computation cost. |
Answer is All You Need: Instruction-following Text Embedding via Answering the Question (2024.acl-long)
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| Challenge: | Existing methods for encoding instruction information fail to be sensitive to clearer criteria like “evaluate similarity based on emotion” . instead, we propose a different approach, which treats the instruction as a “question” about the input text and encodes the expected answers to obtain the representation accordingly. |
| Approach: | They propose a text embedder that captures characteristics of texts specified by user instructions clarifying the similarity criterion. |
| Outcome: | The proposed model improves instruction-following capabilities when applied to large language models and encoder-based LMs. |
Task Compass: Scaling Multi-task Pre-training with Task Prefix (2022.findings-emnlp)
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Zhuosheng Zhang, Shuohang Wang, Yichong Xu, Yuwei Fang, Wenhao Yu, Yang Liu, Hai Zhao, Chenguang Zhu, Michael Zeng
| Challenge: | Existing studies show that multi-task learning with large-scale supervised tasks suffers from negative effects across tasks. |
| Approach: | They propose a task prefix guided multi-task pre-training framework to explore the relationships among tasks. |
| Outcome: | The proposed model can be used as a foundation backbone for a wide range of tasks and as augmentation tool for data augmentation with complementary tasks. |
Siamese Network-Based Supervised Topic Modeling (D18-1)
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| Challenge: | Label-specific topics are widely used for supporting personality psychology, aspectlevel sentiment analysis, and crossdomain sentiment classification. |
| Approach: | They propose a supervised topic model based on the Siamese network which trades off label-specific word distributions with document-specific label distributions in a uniform framework. |
| Outcome: | The proposed model can trade off label-specific word distributions with document-specific label distributions in a uniform framework. |
Training Data is More Valuable than You Think: A Simple and Effective Method by Retrieving from Training Data (2022.acl-long)
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| Challenge: | Experimental results show that REtrieving from the traINing datA only can lead to significant gains on multiple NLG and NLU tasks. |
| Approach: | They propose to retrieve training instances from traINing datA and concatenate them with input to generate output. |
| Outcome: | The proposed method achieves state-of-the-art results on XSum, BigPatent, and CommonsenseQA. |