most citedOptimizing Retrieval-augmented Reader Models via Token Elimination

1 citations · 2 across the 2 of their papers we have counts for

collaborators
Showing cs.CLShow all

2 papers · 1 filter

cs.CL2024★ 1 cited

RAG Foundry: A Framework for Enhancing LLMs for Retrieval Augmented Generation

Daniel Fleischer, Moshe Berchansky, Moshe Wasserblat +1

Implementing Retrieval-Augmented Generation (RAG) systems is inherently complex, requiring deep understanding of data, use cases, and intricate design decisions. Additionally, eval…

cs.CL2023★ 1 cited

Optimizing Retrieval-augmented Reader Models via Token Elimination

Moshe Berchansky, Peter Izsak, Avi Caciularu +2

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, su…