Summary of Language Models “grok” to Copy, by Ang Lv et al.

Language Models “Grok” to Copy

by Ang Lv, Ruobing Xie, Xingwu Sun, Zhanhui Kang, Rui Yan

First submitted to arxiv on: 14 Sep 2024

GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary The abstract proposes a novel perspective on the pre-training dynamics of language models, particularly their ability to copy text from preceding context, which is essential for various applications including in-context learning and retrieval-augmented generation. The authors suggest that Transformer-based language models develop this skill similarly to grokking, where sudden generalization occurs on test sets long after training. The experiments yield three key findings: the pre-training loss decreases rapidly, while the context copying ability of models initially lags before saturating; the speed of developing copying ability is independent of the number of tokens trained; and induction heads form from shallow to deep layers during training, mirroring the development of circuits in deeper layers during grokking. The authors argue that this connection can provide valuable insights for more effective language model training, ultimately improving in-context performance.
Low	GrooveSquid.com (original content)	Low Difficulty Summary Language models are getting better at copying text from previous sentences. This is important because it helps them learn and generate new text on their own. Researchers have found that these models develop this skill over time, similar to how people learn. They discovered three key things: the model’s ability to copy text improves quickly at first, then gets stuck; how much data the model sees doesn’t affect how fast it learns; and the model’s “attention heads” (special parts that help it focus on certain words) start working in a specific order during training. This new understanding can help make language models better and more useful.