Search Results for author:
Found 7 papers, 3 papers with code
How does Feedback Signal Quality Impact Effectiveness of Pseudo Relevance Feedback for Passage Retrieval?
Pseudo-Relevance Feedback (PRF) assumes that the top results retrieved by a first-stage ranker are relevant to the original query and uses them to improve the query representation for a second round of retrieval.
From Little Things Big Things Grow: A Collection with Seed Studies for Medical Systematic Review Literature Search
However, we show pseudo seed studies are not representative of real seed studies used by information specialists.
Improving Chest X-Ray Report Generation by Leveraging Warm-Starting
Our experimental investigation demonstrates that the Convolutional vision Transformer (CvT) ImageNet-21K and the Distilled Generative Pre-trained Transformer 2 (DistilGPT2) checkpoints are best for warm-starting the encoder and decoder, respectively.
Semantic Search for Large Scale Clinical Ontologies
Finding concepts in large clinical ontologies can be challenging when queries use different vocabularies.
Pseudo Relevance Feedback with Deep Language Models and Dense Retrievers: Successes and Pitfalls
Text-based PRF results show that the use of PRF had a mixed effect on deep rerankers across different datasets.
