855 citations · 2.6k across the 59 of their papers we have counts for
57 papers · 1 filter
Unsupervised Large Language Model Alignment for Information Retrieval via Contrastive Feedback
Qian Dong, Yiding Liu, Qingyao Ai +6
Large language models (LLMs) have demonstrated remarkable capabilities across various research domains, including the field of Information Retrieval (IR). However, the responses ge…
Information Retrieval Meets Large Language Models: A Strategic Report from Chinese IR Community
Qingyao Ai, Ting Bai, Zhao Cao +30
The research field of Information Retrieval (IR) has evolved significantly, expanding beyond traditional search to meet diverse user information needs. Recently, Large Language Mod…
An Intent Taxonomy of Legal Case Retrieval
Yunqiu Shao, Haitao Li, Yueyue Wu +5
Legal case retrieval is a special Information Retrieval~(IR) task focusing on legal case documents. Depending on the downstream tasks of the retrieved case documents, users' inform…
I^3 Retriever: Incorporating Implicit Interaction in Pre-trained Language Models for Passage Retrieval
Qian Dong, Yiding Liu, Qingyao Ai +5
Passage retrieval is a fundamental task in many information systems, such as web search and question answering, where both efficiency and effectiveness are critical concerns. In re…
Mitigating Exploitation Bias in Learning to Rank with an Uncertainty-aware Empirical Bayes Approach
Tao Yang, Cuize Han, Chen Luo +3
Ranking is at the core of many artificial intelligence (AI) applications, including search engines, recommender systems, etc. Modern ranking systems are often constructed with lear…
THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval
Haitao Li, Weihang Su, Changyue Wang +3
Legal case retrieval techniques play an essential role in modern intelligent legal systems. As an annually well-known international competition, COLIEE is aiming to achieve the sta…
CaseEncoder: A Knowledge-enhanced Pre-trained Model for Legal Case Encoding
Yixiao Ma, Yueyue Wu, Weihang Su +2
Legal case retrieval is a critical process for modern legal information systems. While recent studies have utilized pre-trained language models (PLMs) based on the general domain s…
FARA: Future-aware Ranking Algorithm for Fairness Optimization
Tao Yang, Zhichao Xu, Zhenduo Wang +1
Ranking systems are the key components of modern Information Retrieval (IR) applications, such as search engines and recommender systems. Besides the ranking relevance to users, th…
Unconfounded Propensity Estimation for Unbiased Ranking
Dan Luo, Lixin Zou, Qingyao Ai +4
The goal of unbiased learning to rank (ULTR) is to leverage implicit user feedback for optimizing learning-to-rank systems. Among existing solutions, automatic ULTR algorithms that…
THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank
Jia Chen, Haitao Li, Weihang Su +2
This paper introduces the approaches we have used to participate in the WSDM Cup 2023 Task 1: Unbiased Learning to Rank. In brief, we have attempted a combination of both tradition…
Constructing Tree-based Index for Efficient and Effective Dense Retrieval
Haitao Li, Qingyao Ai, Jingtao Zhan +4
Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness…
SAILER: Structure-aware Pre-trained Language Model for Legal Case Retrieval
Haitao Li, Qingyao Ai, Jia Chen +5
Legal case retrieval, which aims to find relevant cases for a query case, plays a core role in the intelligent legal system. Despite the success that pre-training has achieved in a…
Metric-agnostic Ranking Optimization
Qingyao Ai, Xuanhui Wang, Michael Bendersky
Ranking is at the core of Information Retrieval. Classic ranking optimization studies often treat ranking as a sorting problem with the assumption that the best performance of rank…
Reward-free Policy Imitation Learning for Conversational Search
Zhenduo Wang, Zhichao Xu, Qingyao Ai
Existing conversational search studies mainly focused on asking better clarifying questions and/or improving search result quality. These works aim at retrieving better responses a…
An In-depth Investigation of User Response Simulation for Conversational Search
Zhenduo Wang, Zhichao Xu, Qingyao Ai +1
Conversational search has seen increased recent attention in both the IR and NLP communities. It seeks to clarify and solve users' search needs through multi-turn natural language…
Towards Better Web Search Performance: Pre-training, Fine-tuning and Learning to Rank
Haitao Li, Jia Chen, Weihang Su +2
This paper describes the approach of the THUIR team at the WSDM Cup 2023 Pre-training for Web Search task. This task requires the participant to rank the relevant documents for eac…
Zero-shot Clarifying Question Generation for Conversational Search
Zhenduo Wang, Yuancheng Tu, Corby Rosset +3
A long-standing challenge for search and conversational assistants is query intention detection in ambiguous queries. Asking clarifying questions in conversational search has been…
Diverse legal case search
Ruizhe Zhang, Qingyao Ai, Yueyue Wu +2
In last decades, legal case search has received more and more attention. Legal practitioners need to work or enhance their efficiency by means of class case search. In the process…
Counterfactual Editing for Search Result Explanation
Zhichao Xu, Hemank Lamba, Qingyao Ai +2
Search Result Explanation (SeRE) aims to improve search sessions' effectiveness and efficiency by helping users interpret documents' relevance. Existing works mostly focus on factu…
Marginal-Certainty-aware Fair Ranking Algorithm
Tao Yang, Zhichao Xu, Zhenduo Wang +2
Ranking systems are ubiquitous in modern Internet services, including online marketplaces, social media, and search engines. Traditionally, ranking systems only focus on how to get…
Hard Negatives or False Negatives: Correcting Pooling Bias in Training Neural Ranking Models
Yinqiong Cai, Jiafeng Guo, Yixing Fan +3
Neural ranking models (NRMs) have become one of the most important techniques in information retrieval (IR). Due to the limitation of relevance labels, the training of NRMs heavily…
Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval
Jingtao Zhan, Qingyao Ai, Yiqun Liu +4
Recent advance in Dense Retrieval (DR) techniques has significantly improved the effectiveness of first-stage retrieval. Trained with large-scale supervised data, DR models can enc…
Reinforcement Learning to Rank Using Coarse-grained Rewards
Yiteng Tu, Zhichao Xu, Tao Yang +6
Learning to rank (LTR) plays a crucial role in various Information Retrieval (IR) tasks. Although supervised LTR methods based on fine-grained relevance labels (e.g., document-leve…
Model-based Unbiased Learning to Rank
Dan Luo, Lixin Zou, Qingyao Ai +3
Unbiased Learning to Rank (ULTR) that learns to rank documents with biased user feedback data is a well-known challenge in information retrieval. Existing methods in unbiased learn…