Search Results for author:
Found 4 papers, 1 papers with code
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks
Large language models have revolutionized the field of NLP by achieving state-of-the-art performance on various tasks.
Explicit and Implicit Semantic Ranking Framework
The core challenge in numerous real-world applications is to match an inquiry to the best document from a mutable and finite set of candidates.
MIMICS: A Large-Scale Data Collection for Search Clarification
In this paper, we introduce MIMICS, a collection of search clarification datasets for real web search queries sampled from the Bing query logs.
Analyzing and Learning from User Interactions for Search Clarification
We also propose a model for learning representation for clarifying questions based on the user interaction data as implicit feedback.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
