Moshe Berchansky


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Optimizing Retrieval-augmented Reader Models via Token Elimination
Moshe Berchansky | Peter Izsak | Avi Caciularu | Ido Dagan | Moshe Wasserblat
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Fusion-in-Decoder (FiD) is an effective retrieval-augmented language model applied across a variety of open-domain tasks, such as question answering, fact checking, etc. In FiD, supporting passages are first retrieved and then processed using a generative model (Reader), which can cause a significant bottleneck in decoding time, particularly with long outputs. In this work, we analyze the contribution and necessity of all the retrieved passages to the performance of reader models, and propose eliminating some of the retrieved information, at the token level, that might not contribute essential information to the answer generation process. We demonstrate that our method can reduce run-time by up to 62.2%, with only a 2% reduction in performance, and in some cases, even improve the performance results.


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How to Train BERT with an Academic Budget
Peter Izsak | Moshe Berchansky | Omer Levy
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

While large language models a la BERT are used ubiquitously in NLP, pretraining them is considered a luxury that only a few well-funded industry labs can afford. How can one train such models with a more modest budget? We present a recipe for pretraining a masked language model in 24 hours using a single low-end deep learning server. We demonstrate that through a combination of software optimizations, design choices, and hyperparameter tuning, it is possible to produce models that are competitive with BERT-base on GLUE tasks at a fraction of the original pretraining cost.