14 citations · 46 across the 28 of their papers we have counts for
13 papers · 1 filter
ConvMix: A Mixed-Criteria Data Augmentation Framework for Conversational Dense Retrieval
Fengran Mo, Jinghan Zhang, Yuchen Hui +4
Conversational search aims to satisfy users' complex information needs via multiple-turn interactions. The key challenge lies in revealing real users' search intent from the contex…
CSPLADE: Learned Sparse Retrieval with Causal Language Models
Zhichao Xu, Aosong Feng, Yijun Tian +2
In recent years, dense retrieval has been the focus of information retrieval (IR) research. While effective, dense retrieval produces uninterpretable dense vectors, and suffers fro…
A Survey of Model Architectures in Information Retrieval
Zhichao Xu, Fengran Mo, Zhiqi Huang +5
The period from 2019 to the present marks one of the most significant paradigm shifts in information retrieval (IR) and natural language processing (NLP), culminating in the emerge…
RankMamba: Benchmarking Mamba's Document Ranking Performance in the Era of Transformers
Zhichao Xu
Transformer structure has achieved great success in multiple applied machine learning communities, such as natural language processing (NLP), computer vision (CV) and information r…
FARA: Future-aware Ranking Algorithm for Fairness Optimization
Tao Yang, Zhichao Xu, Zhenduo Wang +1
Ranking systems are the key components of modern Information Retrieval (IR) applications, such as search engines and recommender systems. Besides the ranking relevance to users, th…
A Lightweight Constrained Generation Alternative for Query-focused Summarization
Zhichao Xu, Daniel Cohen
Query-focused summarization (QFS) aims to provide a summary of a document that satisfies information need of a given query and is useful in various IR applications, such as abstrac…
Reward-free Policy Imitation Learning for Conversational Search
Zhenduo Wang, Zhichao Xu, Qingyao Ai
Existing conversational search studies mainly focused on asking better clarifying questions and/or improving search result quality. These works aim at retrieving better responses a…
An In-depth Investigation of User Response Simulation for Conversational Search
Zhenduo Wang, Zhichao Xu, Qingyao Ai +1
Conversational search has seen increased recent attention in both the IR and NLP communities. It seeks to clarify and solve users' search needs through multi-turn natural language…
Counterfactual Editing for Search Result Explanation
Zhichao Xu, Hemank Lamba, Qingyao Ai +2
Search Result Explanation (SeRE) aims to improve search sessions' effectiveness and efficiency by helping users interpret documents' relevance. Existing works mostly focus on factu…
Marginal-Certainty-aware Fair Ranking Algorithm
Tao Yang, Zhichao Xu, Zhenduo Wang +2
Ranking systems are ubiquitous in modern Internet services, including online marketplaces, social media, and search engines. Traditionally, ranking systems only focus on how to get…
Reinforcement Learning to Rank Using Coarse-grained Rewards
Yiteng Tu, Zhichao Xu, Tao Yang +6
Learning to rank (LTR) plays a crucial role in various Information Retrieval (IR) tasks. Although supervised LTR methods based on fine-grained relevance labels (e.g., document-leve…
Learning to Rank Rationales for Explainable Recommendation
Zhichao Xu, Yi Han, Tao Yang +2
State-of-the-art recommender system (RS) mostly rely on complex deep neural network (DNN) model structure, which makes it difficult to provide explanations along with RS decisions.…
Vertical Allocation-based Fair Exposure Amortizing in Ranking
Tao Yang, Zhichao Xu, Qingyao Ai
Result ranking often affects consumer satisfaction as well as the amount of exposure each item receives in the ranking services. Myopically maximizing customer satisfaction by rank…