25 citations · 29 across the 5 of their papers we have counts for
5 papers
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection
Sophia Althammer, Guido Zuccon, Sebastian Hofstätter +2
Search methods based on Pretrained Language Models (PLM) have demonstrated great effectiveness gains compared to statistical and early neural ranking models. However, fine-tuning P…
Ranger: A Toolkit for Effect-Size Based Multi-Task Evaluation
Mete Sertkan, Sophia Althammer, Sebastian Hofstätter
In this paper, we introduce Ranger - a toolkit to facilitate the easy use of effect-size-based meta-analysis for multi-task evaluation in NLP and IR. We observed that our communiti…
TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval
Sophia Althammer, Sebastian Hofstätter, Suzan Verberne +1
Robust test collections are crucial for Information Retrieval research. Recently there is a growing interest in evaluating retrieval systems for domain-specific retrieval tasks, ho…
Multi-Task Retrieval-Augmented Text Generation with Relevance Sampling
Sebastian Hofstätter, Jiecao Chen, Karthik Raman +1
This paper studies multi-task training of retrieval-augmented generation models for knowledge-intensive tasks. We propose to clean the training set by utilizing a distinct property…
Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems
Sebastian Hofstätter, Nick Craswell, Bhaskar Mitra +2
Recently, several dense retrieval (DR) models have demonstrated competitive performance to term-based retrieval that are ubiquitous in search systems. In contrast to term-based mat…