Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling (2304.01373v2)

Abstract: How do LLMs develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce \textit{Pythia}, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We intend \textit{Pythia} to facilitate research in many areas, and we present several case studies including novel results in memorization, term frequency effects on few-shot performance, and reducing gender bias. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics. Trained models, analysis code, training code, and training data can be found at \url{https://github.com/EleutherAI/pythia}.

• The paper introduces Pythia, a suite designed for precise analysis of LLM training dynamics and scaling by using uniform data and 154 checkpoints per model.
• It employs a controlled experimental design across 16 models ranging from 70 million to 12 billion parameters to investigate bias, memorization, and performance.
• The suite’s standardized framework provides actionable insights into LLM evolution and establishes a benchmark for future research in AI model behavior.

Analyzing LLM Behaviors Leveraging Pythia

Introduction to Pythia

LLMs have remarkably improved the state of the art in various fields such as natural language processing, image synthesis, and even coding. However, the detailed understanding of how such models evolve during training, and how they scale, has been relatively nebulous. To facilitate research into these questions, Pythia has been introduced. Pythia is not just another suite of LLMs; it is one meticulously constructed to allow for precise, controlled analyses of LLM behavior across different model sizes—from 70 million to a substantial 12 billion parameters. Each model within the suite has been trained on identical data, in the exact same sequence. The suite makes it possible to closely paper LLM behavior, offering unprecedented access to 154 checkpoints for each of the 16 models in its lineup.

Relevance of Training Dynamics and Scaling

Training dynamics—how a model learns over time—and scaling—how model behavior changes with increased complexity—are substantial factors in the performance of LLMs. Pythia addresses an essential gap by providing a standard format for these studies, which previously was not fully possible due to varied training methods and lack of access to intermediate checkpoints. The access to highly granular data throughout training history means researchers can now ask and answer more precise questions about LLMs.

Case Studies Enabled by Pythia

Pythia's well-documented, consistent setup allows for new and insightful research, as showcased by several case studies. These studies have explored how modifying gendered terms affects model biases, whether the memorization of data is influenced by the sequence of training inputs, and the impact of pretraining term frequencies on task performance. For instance, interventions where masculine pronouns are switched to feminine in training data lead to reduced gender bias without significantly affecting LLM performance. Analysis of memorization within LLMs has revealed that it largely follows a Poisson distribution, indicating that where data appears in the training sequence has little impact on the likelihood of memorization. Furthermore, the correlation between pretraining term frequencies and task performance appears to be an emergent property prominent in larger models.

Design Decisions and Accessibility

The overarching goal in developing Pythia was to enable and encourage rigorous research. This focus shaped numerous decisions regarding model design and training data. For example, all models used the same training data and followed the same data order—a departure from earlier works that did not facilitate direct comparisons between models. By training multiple models at different scales and at various checkpoints, the suite promises to be essential in understanding how these technologies make decisions and evolve.

Conclusion

By making available an entire suite of powerful LLMs collectively trained on the same data in the same sequence, Pythia opens up opportunities to investigate the inner workings and progressions of LLMs. Researchers now have the tools to dissect how large-scale model behaviors emerge, alter under transformations, and depend on data sampling—all critical in advancing the field of artificial intelligence responsibly and transparently.