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How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 31 -
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries
Paper • 2406.12824 • Published • 21 -
Tokenization Falling Short: The Curse of Tokenization
Paper • 2406.11687 • Published • 16 -
Iterative Length-Regularized Direct Preference Optimization: A Case Study on Improving 7B Language Models to GPT-4 Level
Paper • 2406.11817 • Published • 13
Mayank Sharma
mayank1729
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Papers
-
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 31 -
From RAGs to rich parameters: Probing how language models utilize external knowledge over parametric information for factual queries
Paper • 2406.12824 • Published • 21 -
Tokenization Falling Short: The Curse of Tokenization
Paper • 2406.11687 • Published • 16 -
Iterative Length-Regularized Direct Preference Optimization: A Case Study on Improving 7B Language Models to GPT-4 Level
Paper • 2406.11817 • Published • 13
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