1 code implementation • 4 Apr 2024 • Zechun Niu, Jiaxin Mao, Qingyao Ai, Ji-Rong Wen
Counterfactual learning to rank (CLTR) has attracted extensive attention in the IR community for its ability to leverage massive logged user interaction data to train ranking models.
no code implementations • 27 Mar 2024 • Shengjie Ma, Chong Chen, Qi Chu, Jiaxin Mao
Nonetheless, the method of employing a general large language model for reliable relevance judgments in legal case retrieval is yet to be thoroughly explored.
no code implementations • 27 Mar 2024 • Yan Fang, Jingtao Zhan, Qingyao Ai, Jiaxin Mao, Weihang Su, Jia Chen, Yiqun Liu
In this study, we investigate whether the performance of dense retrieval models follows the scaling law as other neural models.
no code implementations • 26 Mar 2024 • Yiqun Chen, Jiaxin Mao, Yi Zhang, Dehong Ma, Long Xia, Jun Fan, Daiting Shi, Zhicong Cheng, Simiu Gu, Dawei Yin
The objective of search result diversification (SRD) is to ensure that selected documents cover as many different subtopics as possible.
no code implementations • 20 Mar 2024 • Qi Liu, Gang Guo, Jiaxin Mao, Zhicheng Dou, Ji-Rong Wen, Hao Jiang, Xinyu Zhang, Zhao Cao
Based on these findings, we then propose several simple document pruning methods to reduce the storage overhead and compare the effectiveness of different pruning methods on different late-interaction models.
no code implementations • 14 Mar 2024 • Erhan Zhang, Xingzhu Wang, Peiyuan Gong, Yankai Lin, Jiaxin Mao
However, the potential of using LLMs in simulating search behaviors has not yet been fully explored.
1 code implementation • 26 Feb 2024 • Yiding Sun, Feng Wang, Yutao Zhu, Wayne Xin Zhao, Jiaxin Mao
The ability of the foundation models heavily relies on large-scale, diverse, and high-quality pretraining data.
1 code implementation • 9 Feb 2024 • Peiyuan Gong, Jiamian Li, Jiaxin Mao
Hence, to better support the research in collaborative search, in this demo, we propose CoSearchAgent, a lightweight collaborative search agent powered by LLMs.
no code implementations • 23 Jan 2024 • Haonan Chen, Zhicheng Dou, Jiaxin Mao
Besides, it infers session-level relevance labels based on implicit feedback.
no code implementations • 16 Dec 2023 • Peiyuan Gong, Jiaxin Mao
Specifically, for a given aspect to evaluate, we first prompt the LLM to generate a chain of aspects that are relevant to the target aspect and could be useful for the evaluation.
no code implementations • 25 Jul 2023 • Yunqiu Shao, Haitao Li, Yueyue Wu, Yiqun Liu, Qingyao Ai, Jiaxin Mao, Yixiao Ma, Shaoping Ma
Through a laboratory user study, we reveal significant differences in user behavior and satisfaction under different search intents in legal case retrieval.
1 code implementation • 24 Apr 2023 • Haitao Li, Qingyao Ai, Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Zheng Liu, Zhao Cao
Unfortunately, while ANN can improve the efficiency of DR models, it usually comes with a significant price on retrieval performance.
no code implementations • 17 Oct 2022 • Yiqun Chen, Hangyu Mao, Jiaxin Mao, Shiguang Wu, Tianle Zhang, Bin Zhang, Wei Yang, Hongxing Chang
Furthermore, we introduce a novel paradigm named Personalized Training with Distilled Execution (PTDE), wherein agent-personalized global information is distilled into the agent's local information.
1 code implementation • 11 Aug 2022 • Jingtao Zhan, Qingyao Ai, Yiqun Liu, Jiaxin Mao, Xiaohui Xie, Min Zhang, Shaoping Ma
By making the REM and DAMs disentangled, DDR enables a flexible training paradigm in which REM is trained with supervision once and DAMs are trained with unsupervised data.
1 code implementation • 25 Apr 2022 • Jingtao Zhan, Xiaohui Xie, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma
For example, representation-based retrieval models perform almost as well as interaction-based retrieval models in terms of interpolation but not extrapolation.
no code implementations • 7 Apr 2022 • Wenqiang Lei, Yao Zhang, Feifan Song, Hongru Liang, Jiaxin Mao, Jiancheng Lv, Zhenglu Yang, Tat-Seng Chua
To this end, we contribute to advance the study of the proactive dialogue policy to a more natural and challenging setting, i. e., interacting dynamically with users.
3 code implementations • 22 Feb 2022 • Chongming Gao, Shijun Li, Wenqiang Lei, Jiawei Chen, Biao Li, Peng Jiang, Xiangnan He, Jiaxin Mao, Tat-Seng Chua
The progress of recommender systems is hampered mainly by evaluation as it requires real-time interactions between humans and systems, which is too laborious and expensive.
no code implementations • 27 Nov 2021 • Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma
Dense Retrieval (DR) reaches state-of-the-art results in first-stage retrieval, but little is known about the mechanisms that contribute to its success.
4 code implementations • 12 Oct 2021 • Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma
However, the efficiency of most existing DR models is limited by the large memory cost of storing dense vectors and the time-consuming nearest neighbor search (NNS) in vector space.
1 code implementation • 7 Sep 2021 • Zeyang Liu, Ke Zhou, Jiaxin Mao, Max L. Wilson
Conversational search systems, such as Google Assistant and Microsoft Cortana, provide a new search paradigm where users are allowed, via natural language dialogues, to communicate with search systems.
5 code implementations • 2 Aug 2021 • Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma
Compared with previous DR models that use brute-force search, JPQ almost matches the best retrieval performance with 30x compression on index size.
4 code implementations • 16 Apr 2021 • Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo, Min Zhang, Shaoping Ma
ADORE replaces the widely-adopted static hard negative sampling method with a dynamic one to directly optimize the ranking performance.
no code implementations • 24 Dec 2020 • Yunqiu Shao, Bulou Liu, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma
We participated in the two case law tasks, i. e., the legal case retrieval task and the legal case entailment task.
2 code implementations • 20 Oct 2020 • Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma
Through this process, it teaches the DR model how to retrieve relevant documents from the entire corpus instead of how to rerank a potentially biased sample of documents.
3 code implementations • 20 Aug 2020 • Shaoyun Shi, Hanxiong Chen, Weizhi Ma, Jiaxin Mao, Min Zhang, Yongfeng Zhang
Both reasoning and generalization ability are important for prediction tasks such as recommender systems, where reasoning provides strong connection between user history and target items for accurate prediction, and generalization helps the model to draw a robust user portrait over noisy inputs.
3 code implementations • 28 Jun 2020 • Jingtao Zhan, Jiaxin Mao, Yiqun Liu, Min Zhang, Shaoping Ma
Although exact term match between queries and documents is the dominant method to perform first-stage retrieval, we propose a different approach, called RepBERT, to represent documents and queries with fixed-length contextualized embeddings.
no code implementations • 28 Apr 2020 • Qingyao Ai, Tao Yang, Huazheng Wang, Jiaxin Mao
While their definitions of \textit{unbiasness} are different, these two types of ULTR algorithms share the same goal -- to find the best models that rank documents based on their intrinsic relevance or utility.