Search Results for author: Zhuyun Dai

Found 23 papers, 13 papers with code

PaRaDe: Passage Ranking using Demonstrations with Large Language Models

no code implementations22 Oct 2023 Andrew Drozdov, Honglei Zhuang, Zhuyun Dai, Zhen Qin, Razieh Rahimi, Xuanhui Wang, Dana Alon, Mohit Iyyer, Andrew McCallum, Donald Metzler, Kai Hui

Recent studies show that large language models (LLMs) can be instructed to effectively perform zero-shot passage re-ranking, in which the results of a first stage retrieval method, such as BM25, are rated and reordered to improve relevance.

Passage Ranking Passage Re-Ranking +6

Dr.ICL: Demonstration-Retrieved In-context Learning

no code implementations23 May 2023 Man Luo, Xin Xu, Zhuyun Dai, Panupong Pasupat, Mehran Kazemi, Chitta Baral, Vaiva Imbrasaite, Vincent Y Zhao

In-context learning (ICL), teaching a large language model (LLM) to perform a task with few-shot demonstrations rather than adjusting the model parameters, has emerged as a strong paradigm for using LLMs.

In-Context Learning Language Modelling +2

Multi-Vector Retrieval as Sparse Alignment

no code implementations2 Nov 2022 Yujie Qian, Jinhyuk Lee, Sai Meher Karthik Duddu, Zhuyun Dai, Siddhartha Brahma, Iftekhar Naim, Tao Lei, Vincent Y. Zhao

With sparsified unary saliences, we are able to prune a large number of query and document token vectors and improve the efficiency of multi-vector retrieval.

Argument Retrieval Information Retrieval +1

Promptagator: Few-shot Dense Retrieval From 8 Examples

no code implementations23 Sep 2022 Zhuyun Dai, Vincent Y. Zhao, Ji Ma, Yi Luan, Jianmo Ni, Jing Lu, Anton Bakalov, Kelvin Guu, Keith B. Hall, Ming-Wei Chang

To amplify the power of a few examples, we propose Prompt-base Query Generation for Retriever (Promptagator), which leverages large language models (LLM) as a few-shot query generator, and creates task-specific retrievers based on the generated data.

Information Retrieval Natural Questions +1

Dialog Inpainting: Turning Documents into Dialogs

1 code implementation18 May 2022 Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Zhao, Aida Amini, Qazi Mamunur Rashid, Mike Green, Kelvin Guu

Our approach takes the text of any document and transforms it into a two-person dialog between the writer and an imagined reader: we treat sentences from the article as utterances spoken by the writer, and then use a dialog inpainter to predict what the imagined reader asked or said in between each of the writer's utterances.

Conversational Question Answering Retrieval

Large Dual Encoders Are Generalizable Retrievers

2 code implementations15 Dec 2021 Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ji Ma, Vincent Y. Zhao, Yi Luan, Keith B. Hall, Ming-Wei Chang, Yinfei Yang

With multi-stage training, surprisingly, scaling up the model size brings significant improvement on a variety of retrieval tasks, especially for out-of-domain generalization.

Domain Generalization Retrieval +1

COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List

1 code implementation NAACL 2021 Luyu Gao, Zhuyun Dai, Jamie Callan

Classical information retrieval systems such as BM25 rely on exact lexical match and carry out search efficiently with inverted list index.

Information Retrieval Retrieval

Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline

1 code implementation21 Jan 2021 Luyu Gao, Zhuyun Dai, Jamie Callan

Pre-trained deep language models~(LM) have advanced the state-of-the-art of text retrieval.

Retrieval Text Retrieval

PGT: Pseudo Relevance Feedback Using a Graph-Based Transformer

1 code implementation20 Jan 2021 HongChien Yu, Zhuyun Dai, Jamie Callan

Most research on pseudo relevance feedback (PRF) has been done in vector space and probabilistic retrieval models.

Retrieval

Understanding BERT Rankers Under Distillation

no code implementations21 Jul 2020 Luyu Gao, Zhuyun Dai, Jamie Callan

Deep language models such as BERT pre-trained on large corpus have given a huge performance boost to the state-of-the-art information retrieval ranking systems.

Information Retrieval Retrieval

Summarizing and Exploring Tabular Data in Conversational Search

1 code implementation23 May 2020 Shuo Zhang, Zhuyun Dai, Krisztian Balog, Jamie Callan

We propose to generate natural language summaries as answers to describe the complex information contained in a table.

Conversational Search

Complementing Lexical Retrieval with Semantic Residual Embedding

no code implementations29 Apr 2020 Luyu Gao, Zhuyun Dai, Tongfei Chen, Zhen Fan, Benjamin Van Durme, Jamie Callan

This paper presents CLEAR, a retrieval model that seeks to complement classical lexical exact-match models such as BM25 with semantic matching signals from a neural embedding matching model.

Information Retrieval Retrieval

Modularized Transfomer-based Ranking Framework

no code implementations EMNLP 2020 Luyu Gao, Zhuyun Dai, Jamie Callan

Recent innovations in Transformer-based ranking models have advanced the state-of-the-art in information retrieval.

Information Retrieval Retrieval

Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval

2 code implementations23 Oct 2019 Zhuyun Dai, Jamie Callan

When applied to passages, DeepCT-Index produces term weights that can be stored in an ordinary inverted index for passage retrieval.

Passage Retrieval Retrieval +1

Deeper Text Understanding for IR with Contextual Neural Language Modeling

1 code implementation22 May 2019 Zhuyun Dai, Jamie Callan

Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations.

Ad-Hoc Information Retrieval Language Modelling +2

Where is this? Video geolocation based on neural network features

no code implementations22 Oct 2018 Salvador Medina, Zhuyun Dai, Yingkai Gao

In this paper, we propose a family of voting-based methods to aggregate frame-wise geolocation results which boost the video geolocation result.

Image Retrieval Retrieval

Consistency and Variation in Kernel Neural Ranking Model

no code implementations27 Sep 2018 Mary Arpita Pyreddy, Varshini Ramaseshan, Narendra Nath Joshi, Zhuyun Dai, Chenyan Xiong, Jamie Callan, Zhiyuan Liu

This paper studies the consistency of the kernel-based neural ranking model K-NRM, a recent state-of-the-art neural IR model, which is important for reproducible research and deployment in the industry.

Word Embeddings

Convolutional Neural Networks for Soft Matching N-Grams in Ad-hoc Search

no code implementations WSDM 2018 2018 Zhuyun Dai, Chenyan Xiong, Jamie Callan, Zhiyuan Liu

This paper presents Conv-KNRM, a Convolutional Kernel-based Neural Ranking Model that models n-gram soft matches for ad-hoc search.

Learning-To-Rank

End-to-End Neural Ad-hoc Ranking with Kernel Pooling

1 code implementation20 Jun 2017 Chenyan Xiong, Zhuyun Dai, Jamie Callan, Zhiyuan Liu, Russell Power

Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similarities via word embeddings, a new kernel-pooling technique that uses kernels to extract multi-level soft match features, and a learning-to-rank layer that combines those features into the final ranking score.

Document Ranking Learning-To-Rank +2

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