most citedBeyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

7 citations · 13 across the 5 of their papers we have counts for

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cs.CL2024★ 1 cited

Training LLMs over Neurally Compressed Text

Brian Lester, Jaehoon Lee, Alex Alemi +4

In this paper, we explore the idea of training large language models (LLMs) over highly compressed text. While standard subword tokenizers compress text by a small factor, neural t…

cs.CL2023

Frontier Language Models are not Robust to Adversarial Arithmetic, or "What do I need to say so you agree 2+2=5?

C. Daniel Freeman, Laura Culp, Aaron Parisi +27

We introduce and study the problem of adversarial arithmetic, which provides a simple yet challenging testbed for language model alignment. This problem is comprised of arithmetic…