THUIR2 at NTCIR-16 Session Search (SS) Task
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Abstract (arXiv)
Our team(THUIR2) participated in both FOSS and POSS subtasks of the NTCIR-161 Session Search (SS) Task. This paper describes our approaches and results. In the FOSS subtask, we submit five runs using learning-to-rank and fine-tuned pre-trained language models. We fine-tuned the pre-trained language model with ad-hoc data and session information and assembled them by a learning-to-rank method. The assembled model achieves the best performance among all participants in the preliminary evaluation. In the POSS subtask, we used an assembled model which also achieves the best performance in the preliminary evaluation.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 3 of its 9 references that are in the map. First posted on arXiv 1 Jul 2023.
Builds on
3 of 9 references.
References · 3
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2020Overview of the TREC 2019 deep learning trackcited by 282
- 2021Rethink Training of BERT Rerankers in Multi-Stage Retrieval Pipelinecited by 65
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Citing papers per year: 2023: 1; 2024: 5; 2025: 6.
12 papers.
- 2024Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Modelscited by 84
- 2024DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Modelscited by 63
- 2025Parametric Retrieval Augmented Generationcited by 38
- 2025RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defectscited by 27
- 2025JuDGE: Benchmarking Judgment Document Generation for Chinese Legal Systemcited by 18
- 2024Mitigating Entity-Level Hallucination in Large Language Modelscited by 17
- 2023Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrievalcited by 15
- 2025SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generationcited by 14
- 2025Joint Evaluation of Answer and Reasoning Consistency for Hallucination Detection in Large Reasoning Modelscited by 13
- 2024LeKUBE: A Legal Knowledge Update BEnchmarkcited by 12
- 2024STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionalscited by 10
- 2025Towards Unification of Hallucination Detection and Fact Verification for Large Language Modelscited by 2
- 2025Towards Unification of Hallucination Detection and Fact Verification for Large Language Models2 Dec 2025
- 2025SurGE: A Benchmark and Evaluation Framework for Scientific Survey Generation21 Aug 2025
- 2025Joint Evaluation of Answer and Reasoning Consistency for Hallucination Detection in Large Reasoning Models5 Jun 2025
- 2025JuDGE: Benchmarking Judgment Document Generation for Chinese Legal System18 Mar 2025
- 2025RbFT: Robust Fine-tuning for Retrieval-Augmented Generation against Retrieval Defects30 Jan 2025
- 2025Parametric Retrieval Augmented Generation27 Jan 2025
- 2024LeKUBE: A Legal Knowledge Update BEnchmark19 Jul 2024
- 2024Mitigating Entity-Level Hallucination in Large Language Models12 Jul 2024
- 2024STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionals21 Jun 2024
- 2024DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models15 Mar 2024
- 2024Unsupervised Real-Time Hallucination Detection based on the Internal States of Large Language Models11 Mar 2024
- 2023Wikiformer: Pre-training with Structured Information of Wikipedia for Ad-hoc Retrieval17 Dec 2023