Language Models "Grok" to Copy (2409.09281v2)

Abstract: We examine the pre-training dynamics of LLMs, focusing on their ability to copy text from preceding context--a fundamental skill for various LLM applications, including in-context learning (ICL) and retrieval-augmented generation (RAG). We propose a novel perspective that Transformer-based LLMs develop copying abilities similarly to grokking, which refers to sudden generalization on test set long after the model fit to the training set. Our experiments yield three arguments: (1) The pre-training loss decreases rapidly, while the context copying ability of models initially lags and then abruptly saturates. (2) The speed of developing copying ability is independent of the number of tokens trained, similarly to how grokking speed is unaffected by dataset size as long as the data distribution is preserved. (3) Induction heads, the attention heads responsible for copying, form from shallow to deep layers during training, mirroring the development of circuits in deeper layers during grokking. We contend that the connection between grokking and context copying can provide valuable insights for more effective LLM training, ultimately improving in-context performance. For example, we demonstrated that techniques that enhance grokking, such as regularization, either accelerate or enhance the development of context copying.

• The paper demonstrates that language models achieve a sudden improvement in context copying accuracy after pre-training loss stabilizes, resembling grokking dynamics.
• It shows that the development of copying abilities is independent of token count, underscoring the impact of optimization intensity and learning dynamics.
• The research details how induction heads evolve from shallow to deep layers, with regularization techniques accelerating the grokking process in advanced in-context learning.

An Examination of Context Copying Dynamics in LLMs

The paper "LLMs 'Grok' to Copy" provides a meticulous exploration of the pre-training dynamics in LLMs (LMs), particularly focusing on their ability to copy context—a crucial trait for facilitating applications like in-context learning (ICL) and retrieval-augmented generation (RAG). This paper posits a compelling hypothesis, aligning the development of context copying capabilities in LMs with the phenomenon of "grokking."

Key Insights and Arguments

The research hinges on three primary experimental findings that collectively suggest a resemblance between the grokking phenomenon and the development of context copying skills in LLMs:

1. Delayed Improvement in Accuracy: The paper presents empirical evidence that context copying accuracy exhibits a marked increase only after the pre-training loss has stabilized. This characteristic mirrors the grokking process, where models suddenly improve their generalization long after fitting the training set.
2. Independence from Token Count: It is asserted that the speed of developing copying capabilities is independent of the total number of tokens processed during training. This finding aligns with the grokking’s independence from data size, provided the distribution remains unchanged.
3. Formation of Induction Heads: Detailed analysis reveals that induction heads—attention heads responsible for copying tokens—evolve from shallow to deep layers, akin to the development of circuits in deeper layers typically observed in grokking.

Methodology

The research employed 12-layer Llama models, trained over 40 billion tokens from the RedPajama dataset using a fixed architecture and hyper-parameters framework. The context copying capability was evaluated by prompting the trained models to complete input contexts with unique prefixes and measuring the completion accuracy. Particular attention was placed on tracking the formation and role of induction heads across different training checkpoints.

Empirical Results and Analysis

Significant insights surfaced during experiments, specifically through the manipulation of batch sizes and learning rates to probe the robustness of grokking-like dynamics:

• Consistent with the grokking hypothesis, the models' context copying abilities improved independently of the token count when batch sizes varied, pointing towards a reliance on optimization intensity over raw data quantities.
• Higher learning rates expedited the grokking effect, suggesting a crucial relationship between learning dynamics and the emergence of advanced capabilities such as context copying.

Additionally, the incorporation of regularization techniques, like attention dropout and weight decay, was shown to hasten the grokking process and enhance final accuracy, reinforcing regularization's pivotal role.

Implications and Future Directions

The findings underscore the potential to optimize LLM training by leveraging insights from grokking studies. By equating the context copying capability with grokking, this research provides a novel angle for accelerating model efficiency and effectiveness—particularly by utilizing smaller, synthetic datasets to extrapolate insights applicable to large-scale LLMs.

Moving forward, deepening our understanding of the grokking phenomenon in LMs could unveil pathways to architect models with enhanced in-context performance. This work encourages a paradigm where advancements in AI can be informed by examining the mechanistic underpinnings prevalent in smaller, controlled environments, ultimately translating these insights to broader scale deployments.