# THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank

arXiv: https://arxiv.org/abs/2304.12650 · HTML: https://mlsanity.com/paper/2304.12650/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2023.

## Built on by (most cited first, 11 of 11)

- [Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models](https://mlsanity.com/paper/2403.06448/) (2024)
- [DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models](https://mlsanity.com/paper/2403.10081/) (2024)
- [Mitigating Entity-Level Hallucination in Large Language Models](https://mlsanity.com/paper/2407.09417/) (2024)
- [THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrieval](https://mlsanity.com/paper/2305.06812/) (2023)
- [Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrieval](https://mlsanity.com/paper/2312.10661/) (2023)
- [LeKUBE: A Legal Knowledge Update BEnchmark](https://mlsanity.com/paper/2407.14192/) (2024)
- [THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailment](https://mlsanity.com/paper/2305.06817/) (2023)
- [STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals](https://mlsanity.com/paper/2406.15313/) (2024)
- [An Intent Taxonomy of Legal Case Retrieval](https://mlsanity.com/paper/2307.13298/) (2023)
- [Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessions](https://mlsanity.com/paper/2503.00501/) (2025)
- [Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rank](https://mlsanity.com/paper/2408.09817/) (2024)

## Abstract (arXiv)

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 traditional IR models and transformer-based cross-encoder architectures. To further enhance the ranking performance, we also considered a series of features for learning to rank. As a result, we won 2nd place on the final leaderboard.