SelfIE: Self-Interpretation of Large Language Model Embeddings

SelfIE: Interpreting and Controlling Large Language Model Embeddings

Introduction

Interpretability and control over LLMs have increasingly become topics of significant interest within the machine learning community. The ability to understand the internal mechanisms of these models and to modify their behaviors in a meaningful way opens up new avenues for research and application. The paper introduces SelfIE (Self-Interpretation of Embeddings), an innovative framework designed to interpret the hidden embeddings of LLMs using the model's own natural language processing capabilities. This method not only provides insights into the reasoning processes of LLMs but also establishes a foundation for controlling model behavior by editing concepts directly within the model's embeddings.

Self-Interpretation of Embeddings

The core innovation of SelfIE lies in its approach to leveraging the LLM's capacity to generate explanations for its own state without additional training. By prompting the model to describe its internal embeddings, researchers can achieve a natural language interpretation of the model's thought process. This process involves manipulating the forward pass of the LLM to include a specific embedding of interest, prompting the model to generate text that explains this embedding.

Significantly, SelfIE is capable of interpreting high-level, open-world concepts encapsulated in the model's embeddings. This zero-shot approach to embedding interpretation is both practical and versatile, suitable for a range of LLM architectures and applications. Through extensive experimentation, SelfIE has demonstrated its ability to faithfully convey information present in hidden embeddings, effectively "opening the black box" of LLM reasoning.

Leveraging Self-Interpretation for Model Control

Beyond merely interpreting model embeddings, SelfIE introduces methods for controlling model behavior at a granular level. The framework proposes two novel approaches for model control based on embeddings:

1. Supervised Control: This method involves directly editing the embeddings associated with specific concepts or behaviors. By specifying desired changes at the embedding level, researchers can induce the model to exhibit new behaviors or alter its reasoning process. This editing technique is particularly groundbreaking for its precision and efficiency.
2. Reinforcement Control: An extension of reinforcement learning principles to the level of model embeddings. By assigning rewards or penalties based on the desirability of embedding interpretations, this method guides the model toward more favorable reasoning pathways. This approach is especially notable for its ability to effect change without the need for explicit target behaviors, relying instead on high-level objectives.

Empirical Validation and Applications

The paper provides a thorough empirical evaluation of SelfIE, showcasing its efficacy and the potential for practical applications. Through a series of experiments, the authors demonstrate how SelfIE can be used to reveal the LLM's internal reasoning across various scenarios, from ethical decision-making to understanding prompt injections. Additionally, the paper illustrates how SelfIE-based control methods can be employed to erase harmful knowledge from LLMs, adjust model behaviors in response to ethical considerations, and edit the model's understanding of complex concepts.

Implications and Future Directions

The development of SelfIE represents a significant step forward in the ongoing efforts to make LLMs more interpretable and controllable. By providing a method for natural language interpretation of model embeddings, SelfIE enhances our understanding of how LLMs process and reason with information. Moreover, the introduction of embedding-level control methods opens new possibilities for model customization and alignment with human values.

Looking ahead, the foundation laid by SelfIE invites further exploration into the mechanisms of LLM reasoning and the potential for more nuanced model manipulations. As the field of generative AI continues to evolve, methodologies like SelfIE will play a crucial role in shaping the development of more transparent, reliable, and adaptable LLMs.

Conclusion

SelfIE articulates a novel paradigm for the interpretation and control of LLM embeddings, marking a significant advancement in the quest for more interpretable and malleable machine learning models. Through its innovative approach and promising applications, SelfIE sets the stage for future research into the intricacies of LLM behavior and the broader implications for AI development and deployment.