Papers by Zhuyun Dai
Modularized Transfomer-based Ranking Framework (2020.emnlp-main)
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| Challenge: | Recent innovations in Transformer-based ranking models have advanced the state-of-the-art in information retrieval. |
| Approach: | They propose to modularize a Transformer ranker into separate modules for text representation and interaction. |
| Outcome: | The proposed model is faster than previous models and is easier to interpret and understand. |
RARR: Researching and Revising What Language Models Say, Using Language Models (2023.acl-long)
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Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, Kelvin Guu
| Challenge: | Language models (LMs) excel at many tasks but often produce unsupported or misleading content. |
| Approach: | They propose a system that finds attribution for any text generation model and post-edits it to fix unsupported content. |
| Outcome: | The proposed system improves attribution while preserving the original output. |
COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List (2021.naacl-main)
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| Challenge: | Recent neural IR models shift towards soft matching all query document terms, but they lose the computation efficiency of exact match systems. |
| Approach: | They propose a contextualized exact match retrieval architecture where scoring is based on overlapping query document tokens’ contextualized representations. |
| Outcome: | The proposed architecture outperforms classical lexical retrieval systems and state-of-the-art deep language models with smaller latency. |
Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling (2020.findings-emnlp)
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| Challenge: | Existing methods to verify scientifically false online information are limited by the lack of training data in the scientific domain. |
| Approach: | They propose an in-domain language modeling method for fact extraction and verification systems . they use SCIFACT to extract scientifically false online information . |
| Outcome: | The proposed method improves accuracy 30% on SCIFACT dataset . state-of-the-art model achieves only 46.6% precision, which is hard to be trusted for users. |
LOFT: Scalable and More Realistic Long-Context Evaluation (2025.findings-naacl)
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Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Séb Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu
| Challenge: | Long-context language models (LCLMs) can be used to perform tasks traditionally reliant on external tools like retrieval systems or databases. |
| Approach: | They propose a benchmark to evaluate LCLMs' performance on in-context retrieval and reasoning tasks using a set of tokens. |
| Outcome: | The proposed model outperforms state-of-the-art retrieval and RAG systems on in-context retrieval tasks while still requiring prompting strategies. |
Large Dual Encoders Are Generalizable Retrievers (2022.emnlp-main)
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Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernandez Abrego, Ji Ma, Vincent Zhao, Yi Luan, Keith Hall, Ming-Wei Chang, Yinfei Yang
| Challenge: | Experimental results show that dual encoders outperform sparse and dense retrievers on the BEIR dataset significantly. |
| Approach: | They challenge belief that bottleneck layer is too limited for out-of-domain generalization . they scale up the model while keeping bottleneck as a single dot-product with a fixed size . |
| Outcome: | The proposed model outperforms sparse and dense retrievers on the BEIR dataset significantly. |
PaRaDe: Passage Ranking using Demonstrations with LLMs (2023.findings-emnlp)
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Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, Kai Hui
| Challenge: | Existing studies show that large language models can be instructed to perform zero-shot passage re-ranking . Existing work like UPR demonstrate promising results for zero- shot ranking using LLMs . |
| Approach: | They propose a demonstration selection strategy based on difficulty rather than semantic similarity . they propose to include only one demonstration in the prompt to improve re-ranking . |
| Outcome: | The proposed method improves LLM-based re-ranking by adding one demonstration to the prompt. |