30 citations · 69 across the 13 of their papers we have counts for
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cs.CL2024
Too Big to Fool: Resisting Deception in Language Models
Mohammad Reza Samsami, Mats Leon Richter, Juan Rodriguez +3
Large language models must balance their weight-encoded knowledge with in-context information from prompts to generate accurate responses. This paper investigates this interplay by…
cs.CL2023★ 12 cited
Continual Pre-Training of Large Language Models: How to (re)warm your model?
Kshitij Gupta, Benjamin Thérien, Adam Ibrahim +5
Large language models (LLMs) are routinely pre-trained on billions of tokens, only to restart the process over again once new data becomes available. A much cheaper and more effici…