Papers by Yuwei Fang
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. |
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. |
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data (2024.findings-naacl)
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Ziyi Yang, Mahmoud Khademi, Yichong Xu, Reid Pryzant, Yuwei Fang, Chenguang Zhu, Dongdong Chen, Yao Qian, Xuemei Gao, Yi-Ling Chen, Robert Gmyr, Naoyuki Kanda, Noel Codella, Bin Xiao, Yu Shi, Lu Yuan, Takuya Yoshioka, Michael Zeng, Xuedong Huang
| Challenge: | i-Code V2 is one of the first models capable of generating natural language from any combination of Vision, Language, and Speech data. |
| Approach: | They propose to create a model that can generate natural language from any combination of Vision, Language, and Speech data. |
| Outcome: | i-Code V2 matches or outperforms state-of-the-art single- and dual-modality baselines on 7 multimodal tasks. |
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. |
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. |
MACSum: Controllable Summarization with Mixed Attributes (2023.tacl-1)
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Yusen Zhang, Yang Liu, Ziyi Yang, Yuwei Fang, Yulong Chen, Dragomir Radev, Chenguang Zhu, Michael Zeng, Rui Zhang
| Challenge: | Existing work on controllable summarization with mixed attributes lacks designated annotations. |
| Approach: | They propose a human-annotated summarization benchmark for controllable summarizing with mixed attributes based on news and dialogue sources . |
| Outcome: | The proposed dataset contains human-annotated summarization datasets with mixed attributes . hard prompt models yield the best performance on most metrics and human evaluations . mixed-attribute control is still challenging for summarizing tasks . |
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. |
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. |
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. |
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. |
PLUG: Leveraging Pivot Language in Cross-Lingual Instruction Tuning (2024.acl-long)
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| Challenge: | Instruction tuning has advanced large language models (LLMs) but its application in lower-resource languages faces challenges due to the imbalanced foundational abilities of LLMs across different languages. |
| Approach: | They propose a pivot language guided generation approach that utilizes a high-resource language as the pivot to enhance instruction tuning in lower-resourced languages. |
| Outcome: | The proposed approach improves instruction-following abilities of LLMs by 29% on average compared to directly responding in the target language alone. |
Evaluating Very Long-Term Conversational Memory of LLM Agents (2024.acl-long)
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| Challenge: | Existing studies on long-term open-domain dialogues focus on evaluating responses within contexts spanning no more than five chat sessions. |
| Approach: | They propose a machine-human pipeline to generate very long-term dialogues by leveraging LLMs and retrieval augmented generation techniques. |
| Outcome: | The proposed pipeline generates very long-term dialogues using LLMs and RAGs . the generated conversations are verified and edited by human annotators for long-range consistency and grounding to the event graphs. |
i-Code Studio: A Configurable and Composable Framework for Integrative AI (2024.emnlp-demo)
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Yuwei Fang, Mahmoud Khademi, Chenguang Zhu, Ziyi Yang, Reid Pryzant, Yichong Xu, Yao Qian, Takuya Yoshioka, Lu Yuan, Michael Zeng, Xuedong Huang
| Challenge: | Existing frameworks for Integrative AI lack flexibility and composability to handle multimodal tasks. |
| Approach: | They propose a configurable framework for Integrative AI that orchestrates multiple pre-trained models to conduct complex multimodal tasks. |
| Outcome: | The proposed framework achieves impressive results on zero-shot multimodal tasks . it can communicate and personalize for users, and it can be used in a multimodal agent . |
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. |
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. |
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. |