activity
20202023
most citedTHUIR@COLIEE-2020: Leveraging Semantic Understanding and Exact Matching for Legal Case Retrieval and Entailment

8 citations · 8 across the 3 of their papers we have counts for

collaborators
Showing cs.IRShow all

10 papers · 1 filter

cs.IR2023

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…

cs.IR20236 cited

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…

cs.IR20231 cited

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…

cs.IR202331 cited

A Unified Generative Retriever for Knowledge-Intensive Language Tasks via Prompt Learning

Jiangui Chen, Ruqing Zhang, Jiafeng Guo +4

Knowledge-intensive language tasks (KILTs) benefit from retrieving high-quality relevant contexts from large external knowledge corpora. Learning task-specific retrievers that retu…

cs.IR2023

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…

cs.IR2023

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…