1 code implementation • 29 Apr 2024 • Pat Verga, Sebastian Hofstatter, Sophia Althammer, Yixuan Su, Aleksandra Piktus, Arkady Arkhangorodsky, Minjie Xu, Naomi White, Patrick Lewis
As Large Language Models (LLMs) have become more advanced, they have outpaced our abilities to accurately evaluate their quality.
no code implementations • 19 Apr 2021 • Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani, Nick Craswell
The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark -- and can be considered to be an efficient (but slightly less effective) alternative to other Transformer-based architectures that employ (i) large-scale pretraining (high training cost), (ii) joint encoding of query and document (high inference cost), and (iii) larger number of Transformer layers (both high training and high inference costs).
no code implementations • 14 Nov 2020 • Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani, Nick Craswell
We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track.
1 code implementation • 20 Jul 2020 • Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani, Nick Craswell
In this work, we extend the TK architecture to the full retrieval setting by incorporating the query term independence assumption.