Search Results for author:
Found 6 papers, 1 papers with code
End-to-End Query Term Weighting
These inferred weights and terms can be used directly by a retrieval system to perform a query search.
Ranked #1 on
Passage Retrieval
on MS MARCO
Learning to Rank when Grades Matter
In this paper, we conduct a rigorous study of learning to rank with grades, where both ranking performance and grade prediction performance are important.
Exploring the Viability of Synthetic Query Generation for Relevance Prediction
While our experiments demonstrate that these modifications help improve performance of QGen techniques, we also find that QGen approaches struggle to capture the full nuance of the relevance label space and as a result the generated queries are not faithful to the desired relevance label.
QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation
Thus, in this paper we make the following contributions: (1) We demonstrate that Retrieval Augmentation of queries provides LLMs with valuable additional context enabling improved understanding.
Retrieval Augmentation for T5 Re-ranker using External Sources
Retrieval augmentation has shown promising improvements in different tasks.
Learning-to-Rank with BERT in TF-Ranking
This paper describes a machine learning algorithm for document (re)ranking, in which queries and documents are firstly encoded using BERT [1], and on top of that a learning-to-rank (LTR) model constructed with TF-Ranking (TFR) [2] is applied to further optimize the ranking performance.
