17 citations · 65 across the 15 of their papers we have counts for
22 papers
FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation
Sebastian Hofstätter, Jiecao Chen, Karthik Raman +1
Retrieval-augmented generation models offer many benefits over standalone language models: besides a textual answer to a given query they provide provenance items retrieved from an…
Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction
Sebastian Hofstätter, Omar Khattab, Sophia Althammer +2
Recent progress in neural information retrieval has demonstrated large gains in effectiveness, while often sacrificing the efficiency and interpretability of the neural model compa…
Establishing Strong Baselines for TripClick Health Retrieval
Sebastian Hofstätter, Sophia Althammer, Mete Sertkan +1
We present strong Transformer-based re-ranking and dense retrieval baselines for the recently released TripClick health ad-hoc retrieval collection. We improve the - originally too…
A Time-Optimized Content Creation Workflow for Remote Teaching
Sebastian Hofstätter, Sophia Althammer, Mete Sertkan +1
We describe our workflow to create an engaging remote learning experience for a university course, while minimizing the post-production time of the educators. We make use of ubiqui…
Linguistically Informed Masking for Representation Learning in the Patent Domain
Sophia Althammer, Mark Buckley, Sebastian Hofstätter +1
Domain-specific contextualized language models have demonstrated substantial effectiveness gains for domain-specific downstream tasks, like similarity matching, entity recognition…
Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
Sebastian Hofstätter, Sheng-Chieh Lin, Jheng-Hong Yang +2
A vital step towards the widespread adoption of neural retrieval models is their resource efficiency throughout the training, indexing and query workflows. The neural IR community…