1.2k citations · 2.5k across the 21 of their papers we have counts for
21 papers
Dr.ICL: Demonstration-Retrieved In-context Learning
Man Luo, Xin Xu, Zhuyun Dai +5
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…
Rethinking the Role of Token Retrieval in Multi-Vector Retrieval
Jinhyuk Lee, Zhuyun Dai, Sai Meher Karthik Duddu +4
Multi-vector retrieval models such as ColBERT [Khattab and Zaharia, 2020] allow token-level interactions between queries and documents, and hence achieve state of the art on many i…
Multi-Vector Retrieval as Sparse Alignment
Yujie Qian, Jinhyuk Lee, Sai Meher Karthik Duddu +5
Multi-vector retrieval models improve over single-vector dual encoders on many information retrieval tasks. In this paper, we cast the multi-vector retrieval problem as sparse alig…
Scaling Instruction-Finetuned Language Models
Hyung Won Chung, Le Hou, Shayne Longpre +32
Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we expl…
RARR: Researching and Revising What Language Models Say, Using Language Models
Luyu Gao, Zhuyun Dai, Panupong Pasupat +8
Language models (LMs) now excel at many tasks such as few-shot learning, question answering, reasoning, and dialog. However, they sometimes generate unsupported or misleading conte…
Promptagator: Few-shot Dense Retrieval From 8 Examples
Zhuyun Dai, Vincent Y. Zhao, Ji Ma +7
Much recent research on information retrieval has focused on how to transfer from one task (typically with abundant supervised data) to various other tasks where supervision is lim…
Dialog Inpainting: Turning Documents into Dialogs
Zhuyun Dai, Arun Tejasvi Chaganty, Vincent Zhao +4
Many important questions (e.g. "How to eat healthier?") require conversation to establish context and explore in depth. However, conversational question answering (ConvQA) systems…
Large Dual Encoders Are Generalizable Retrievers
Jianmo Ni, Chen Qu, Jing Lu +8
It has been shown that dual encoders trained on one domain often fail to generalize to other domains for retrieval tasks. One widespread belief is that the bottleneck layer of a du…
COIL: Revisit Exact Lexical Match in Information Retrieval with Contextualized Inverted List
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. Recent neural IR models shifts towards s…
Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipeline
Luyu Gao, Zhuyun Dai, Jamie Callan
Pre-trained deep language models~(LM) have advanced the state-of-the-art of text retrieval. Rerankers fine-tuned from deep LM estimates candidate relevance based on rich contextual…
PGT: Pseudo Relevance Feedback Using a Graph-Based Transformer
HongChien Yu, Zhuyun Dai, Jamie Callan
Most research on pseudo relevance feedback (PRF) has been done in vector space and probabilistic retrieval models. This paper shows that Transformer-based rerankers can also benefi…
CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search
Chenyan Xiong, Zhenghao Liu, Si Sun +7
Neural rankers based on deep pretrained language models (LMs) have been shown to improve many information retrieval benchmarks. However, these methods are affected by their the cor…
Understanding BERT Rankers Under Distillation
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. Knowledge embedded…
Summarizing and Exploring Tabular Data in Conversational Search
Shuo Zhang, Zhuyun Dai, Krisztian Balog +1
Tabular data provide answers to a significant portion of search queries. However, reciting an entire result table is impractical in conversational search systems. We propose to gen…
Complementing Lexical Retrieval with Semantic Residual Embedding
Luyu Gao, Zhuyun Dai, Tongfei Chen +3
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 match…
Modularized Transfomer-based Ranking Framework
Luyu Gao, Zhuyun Dai, Jamie Callan
Recent innovations in Transformer-based ranking models have advanced the state-of-the-art in information retrieval. However, these Transformers are computationally expensive, and t…
Context-Aware Sentence/Passage Term Importance Estimation For First Stage Retrieval
Zhuyun Dai, Jamie Callan
Term frequency is a common method for identifying the importance of a term in a query or document. But it is a weak signal, especially when the frequency distribution is flat, such…
Deeper Text Understanding for IR with Contextual Neural Language Modeling
Zhuyun Dai, Jamie Callan
Neural networks provide new possibilities to automatically learn complex language patterns and query-document relations. Neural IR models have achieved promising results in learnin…
Where is this? Video geolocation based on neural network features
Salvador Medina, Zhuyun Dai, Yingkai Gao
In this work we propose a method that geolocates videos within a delimited widespread area based solely on the frames visual content. Our proposed method tackles video-geolocation…
Consistency and Variation in Kernel Neural Ranking Model
Mary Arpita Pyreddy, Varshini Ramaseshan, Narendra Nath Joshi +4
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 deplo…
End-to-End Neural Ad-hoc Ranking with Kernel Pooling
Chenyan Xiong, Zhuyun Dai, Jamie Callan +2
This paper proposes K-NRM, a kernel based neural model for document ranking. Given a query and a set of documents, K-NRM uses a translation matrix that models word-level similariti…