299 citations · 531 across the 12 of their papers we have counts for
6 papers · 1 filter
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Ying-Chun Lin, Jennifer Neville, Jack W. Stokes +14
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their sati…
GenSERP: Large Language Models for Whole Page Presentation
Zhenning Zhang, Yunan Zhang, Suyu Ge +4
The advent of large language models (LLMs) brings an opportunity to minimize the effort in search engine result page (SERP) organization. In this paper, we propose GenSERP, a frame…
Generic Intent Representation in Web Search
Hongfei Zhang, Xia Song, Chenyan Xiong +4
This paper presents GEneric iNtent Encoder (GEN Encoder) which learns a distributed representation space for user intent in search. Leveraging large scale user clicks from Bing sea…
An Axiomatic Approach to Regularizing Neural Ranking Models
Corby Rosset, Bhaskar Mitra, Chenyan Xiong +3
Axiomatic information retrieval (IR) seeks a set of principle properties desirable in IR models. These properties when formally expressed provide guidance in the search for better…
Optimizing Query Evaluations using Reinforcement Learning for Web Search
Corby Rosset, Damien Jose, Gargi Ghosh +2
In web search, typically a candidate generation step selects a small set of documents---from collections containing as many as billions of web pages---that are subsequently ranked…
Neural Ranking Models with Multiple Document Fields
Hamed Zamani, Bhaskar Mitra, Xia Song +2
Deep neural networks have recently shown promise in the ad-hoc retrieval task. However, such models have often been based on one field of the document, for example considering docu…