60 citations · 289 across the 23 of their papers we have counts for
4 papers · 1 filter
Distribution-Aligned Fine-Tuning for Efficient Neural Retrieval
Jurek Leonhardt, Marcel Jahnke, Avishek Anand
Dual-encoder-based neural retrieval models achieve appreciable performance and complement traditional lexical retrievers well due to their semantic matching capabilities, which mak…
Supervised Contrastive Learning Approach for Contextual Ranking
Abhijit Anand, Jurek Leonhardt, Koustav Rudra +1
Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models ten…
BAGEL: A Benchmark for Assessing Graph Neural Network Explanations
Mandeep Rathee, Thorben Funke, Avishek Anand +1
The problem of interpreting the decisions of machine learning is a well-researched and important. We are interested in a specific type of machine learning model that deals with gra…
BERT Rankers are Brittle: a Study using Adversarial Document Perturbations
Yumeng Wang, Lijun Lyu, Avishek Anand
Contextual ranking models based on BERT are now well established for a wide range of passage and document ranking tasks. However, the robustness of BERT-based ranking models under…