Understanding Hidden Representations in LLMs
Introduction
Examining the underlying representations in LLMs is invaluable for comprehending their decision-making processes and ensuring alignment with human values. While LLMs excel in generating coherent text, researchers have realized that these models can also serve as their own interpreters. A new framework has emerged, known as Patchscopes, which systematically decodes information embedded within LLM representations and produces explanations in natural language.
Unifying Interpretability Techniques
Patchscopes offers a modular approach which subsumes several previous interpretability methods under its umbrella. Classical methods typically either employed linear classifiers as probes on top of hidden layers or projected hidden layer representations into the vocab space to make sense of model predictions. However, these often failed in the early layers or lacked the expressiveness provided by a detailed language-based explanation.
The Patchscopes framework, on the other hand, patches targeted model representations into a specifically designed inference prompt, enabling the analysis of a model's reasoning at any layer. This not only unifies but also addresses the shortcomings of previous techniques by obviating the need for supervised training and allowing for robust inspection across multiple layers of the LLM.
Advancing Model Interpretability
Patchscopes introduce capabilities that prior tools have not explored. In particular, the framework can probe how LLMs process input tokens in the initial layers, revealing the contextualization and entity resolution strategies employed by the model. Interestingly, it also boosts expressiveness by utilizing a more advanced model to interpret the inner workings of a less sophisticated one.
Researchers have demonstrated Patchscopes' efficacy in a variety of experimental settings. For instance, it significantly improves the accuracy of next-token prediction when compared to existing projection-based methods. It also outperforms traditional probing in the extraction of specific attributes from LLM representations, particularly shining in tasks that require fine-grained analysis of the model’s understanding of context.
Practical Applications and Future Work
Beyond interpretability, Patchscopes have shown practical value in helping models self-correct multistep reasoning errors. By strategically rerouting representations during inference, the framework enhances the model's ability to connect separate reasoning steps and arrive at a coherent conclusion.
This framework only begins to explore the potential applications of self-interpreting LLMs. Future research could expand Patchscopes to other domains, develop variations for multitoken analysis, and construct guidelines for deploying task-specific Patchscopes. The push towards understanding the cognitive underpinnings of AI not only promotes transparency but also paves the way for models that better align with human reasoning.