mlsanityData to 14 Oct 2026

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

arXiv 2304.12650 ↗First posted 25 Apr 2023cs.IRcited by 11

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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.

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

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Citing papers per year: 2023: 4; 2024: 6; 2025: 1.

11 papers.

  1. 2024Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Modelscited by 86
  2. 2024DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Modelscited by 63
  3. 2024Mitigating Entity-Level Hallucination in Large Language Modelscited by 17
  4. 2023THUIR@COLIEE 2023: Incorporating Structural Knowledge into Pre-trained Language Models for Legal Case Retrievalcited by 16
  5. 2023Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrievalcited by 15
  6. 2024LeKUBE: A Legal Knowledge Update BEnchmarkcited by 12
  7. 2023THUIR@COLIEE 2023: More Parameters and Legal Knowledge for Legal Case Entailmentcited by 11
  8. 2024STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionalscited by 10
  9. 2023An Intent Taxonomy of Legal Case Retrievalcited by 6
  10. 2025Qilin: A Multimodal Information Retrieval Dataset with APP-level User Sessionscited by 4
  11. 2024Contextual Dual Learning Algorithm with Listwise Distillation for Unbiased Learning to Rankcited by 0