575 citations · 1.7k across the 39 of their papers we have counts for
40 papers
Agentic Search in the Wild: Intents and Trajectory Dynamics from 14M+ Real Search Requests
Jingjie Ning, João Coelho, Yibo Kong +5
LLM-powered search agents are increasingly being used for multi-step information seeking tasks, yet the IR community lacks empirical understanding of how agentic search sessions un…
DeepResearchGym: A Free, Transparent, and Reproducible Evaluation Sandbox for Deep Research
João Coelho, Jingjie Ning, Jingyuan He +8
Deep research systems represent an emerging class of agentic information retrieval methods that generate comprehensive and well-supported reports to complex queries. However, most…
Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval
João Coelho, Bruno Martins, João Magalhães +2
This study investigates the existence of positional biases in Transformer-based models for text representation learning, particularly in the context of web document retrieval. We b…
Building Retrieval Systems for the ClueWeb22-B Corpus
Harshit Mehrotra, Jamie Callan, Zhen Fan
The ClueWeb22 dataset containing nearly 10 billion documents was released in 2022 to support academic and industry research. The goal of this project was to build retrieval baselin…
Multi-Objective Improvement of Android Applications
James Callan, Justyna Petke
Non-functional properties, such as runtime or memory use, are important to mobile app users and developers, as they affect user experience. Previous work on automated improvement o…
Active Retrieval Augmented Generation
Zhengbao Jiang, Frank F. Xu, Luyu Gao +6
Despite the remarkable ability of large language models (LMs) to comprehend and generate language, they have a tendency to hallucinate and create factually inaccurate output. Augme…
Precise Zero-Shot Dense Retrieval without Relevance Labels
Luyu Gao, Xueguang Ma, Jimmy Lin +1
While dense retrieval has been shown effective and efficient across tasks and languages, it remains difficult to create effective fully zero-shot dense retrieval systems when no re…
Retrieval as Attention: End-to-end Learning of Retrieval and Reading within a Single Transformer
Zhengbao Jiang, Luyu Gao, Jun Araki +4
Systems for knowledge-intensive tasks such as open-domain question answering (QA) usually consist of two stages: efficient retrieval of relevant documents from a large corpus and d…
ClueWeb22: 10 Billion Web Documents with Visual and Semantic Information
Arnold Overwijk, Chenyan Xiong, Xiao Liu +2
ClueWeb22, the newest iteration of the ClueWeb line of datasets, provides 10 billion web pages affiliated with rich information. Its design was influenced by the need for a high qu…
PAL: Program-aided Language Models
Luyu Gao, Aman Madaan, Shuyan Zhou +5
Large language models (LLMs) have recently demonstrated an impressive ability to perform arithmetic and symbolic reasoning tasks, when provided with a few examples at test time ("f…
Long Document Re-ranking with Modular Re-ranker
Luyu Gao, Jamie Callan
Long document re-ranking has been a challenging problem for neural re-rankers based on deep language models like BERT. Early work breaks the documents into short passage-like chunk…
Tevatron: An Efficient and Flexible Toolkit for Dense Retrieval
Luyu Gao, Xueguang Ma, Jimmy Lin +1
Recent rapid advancements in deep pre-trained language models and the introductions of large datasets have powered research in embedding-based dense retrieval. While several good r…
Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback
HongChien Yu, Chenyan Xiong, Jamie Callan
Dense retrieval systems conduct first-stage retrieval using embedded representations and simple similarity metrics to match a query to documents. Its effectiveness depends on encod…
Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval
Luyu Gao, Jamie Callan
Recent research demonstrates the effectiveness of using fine-tuned language models~(LM) for dense retrieval. However, dense retrievers are hard to train, typically requiring heavil…
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…
Condenser: a Pre-training Architecture for Dense Retrieval
Luyu Gao, Jamie Callan
Pre-trained Transformer language models (LM) have become go-to text representation encoders. Prior research fine-tunes deep LMs to encode text sequences such as sentences and passa…
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…
Assessing the Benefits of Model Ensembles in Neural Re-Ranking for Passage Retrieval
Luís Borges, Bruno Martins, Jamie Callan
Our work aimed at experimentally assessing the benefits of model ensembling within the context of neural methods for passage reranking. Starting from relatively standard neural mod…
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…
Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup
Luyu Gao, Yunyi Zhang, Jiawei Han +1
Contrastive learning has been applied successfully to learn vector representations of text. Previous research demonstrated that learning high-quality representations benefits from…
Generating Categories for Sets of Entities
Shuo Zhang, Krisztian Balog, Jamie Callan
Category systems are central components of knowledge bases, as they provide a hierarchical grouping of semantically related concepts and entities. They are a unique and valuable re…
Ranking Clarification Questions via Natural Language Inference
Vaibhav Kumar, Vikas Raunak, Jamie Callan
Given a natural language query, teaching machines to ask clarifying questions is of immense utility in practical natural language processing systems. Such interactions could help i…
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…