ParaScopes: What do Language Models Activations Encode About Future Text?

A recent article published on arXiv introduces a novel framework called Residual Stream Decoders designed to enhance the interpretability of language model activations. This framework enables researchers to probe model activations at both paragraph and document scales, providing deeper insights into how language models plan and generate longer sequences of text. By focusing on the residual stream within the model, the approach reveals what information is encoded about future text during the generation process. The ability to analyze activations at multiple scales marks a significant advancement in understanding the internal workings of language models, particularly in their capacity to handle extended tasks. This development aligns with ongoing research efforts to demystify the decision-making processes of artificial intelligence systems. The framework's benefits include improved transparency and potential applications in refining model design and evaluation. Overall, Residual Stream Decoders represent a promising tool for advancing the study of language model behavior in complex text generation scenarios.