Trenton Bricken and Sholto Douglas discuss how transfer learning demonstrates that Large Language Models (LLMs) are not just “stochastic parrots” but can reason and generalize beyond their training data. Their conversation highlights the importance of considering transfer learning and model interpretability to uncover the true capabilities of LLMs in various tasks and modalities.
Trenton Bricken and Sholto Douglas discuss how transfer learning demonstrates that Large Language Models (LLMs) are not merely “stochastic parrots.” They mention a book recommendation, “The Symbolic Species,” which argues that language has evolved over tens of thousands of years to suit the development of young minds. While some feel that language prediction in LLMs is simple compared to other modalities like computer vision, there is debate over the extent of positive transfer between various modalities. An interesting finding is that fine-tuning LLMs on math problems leads to improved entity recognition, suggesting latent capabilities acquired from understanding images could aid in tasks like coding.
The conversation delves into research on model interpretability, where studies like David B’s lab’s work on attention heads shed light on how LLMs evolve during fine-tuning. They touch upon how training LLMs on code improves reasoning abilities, challenging the assumption that LLMs are mere predictors. The connection between coding and language suggests a deeper level of understanding within the models, indicating that true reasoning processes occur in LLMs, rather than purely statistical prediction.
Examples are provided to illustrate instances where LLMs generalize well beyond the dataset they were trained on, such as in game sequences and influential data points analysis. This generalization showcases the models’ ability to reason and infer beyond their training data, providing evidence that they are not just memorizing patterns. The discussion highlights the significance of transfer learning in LLMs and how it can lead to improved reasoning capabilities across different tasks and modalities.
Overall, the conversation emphasizes the importance of considering transfer learning and model interpretability when assessing the true capabilities of LLMs. The evidence presented suggests that LLMs possess the ability to reason and generalize beyond their training data, challenging simplistic views of these models as “stochastic parrots.” By exploring the nuanced connections between different modalities and reasoning processes, researchers can uncover the full potential of LLMs in various applications.