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cs.CL2024
Fewer Truncations Improve Language Modeling
Hantian Ding, Zijian Wang, Giovanni Paolini +4
In large language model training, input documents are typically concatenated together and then split into sequences of equal length to avoid padding tokens. Despite its efficiency,…
cs.CL2024★ 3 cited
General Purpose Verification for Chain of Thought Prompting
Robert Vacareanu, Anurag Pratik, Evangelia Spiliopoulou +6
Many of the recent capabilities demonstrated by Large Language Models (LLMs) arise primarily from their ability to exploit contextual information. In this paper, we explore ways to…