Patchscope: A Unifying Framework for Inspecting Hidden Representations
  of Language Models
Authors: name: Asma Ghandeharioun, name: Avi Caciularu, name: Adam Pearce, name: Lucas Dixon, name: Mor Geva

Inspecting the information encoded in hidden representations of large
language models (LLMs) can explain models' behavior and verify their alignment
with human values. Given the capabilities of LLMs in generating
human-understandable text, we propose leveraging the model itself to explain
its internal representations in natural language. We introduce a framework
called Patchscopes and show how it can be used to answer a wide range of
research questions about an LLM's computation. We show that prior
interpretability methods based on projecting representations into the
vocabulary space and intervening on the LLM computation, can be viewed as
special instances of this framework. Moreover, several of their shortcomings
such as failure in inspecting early layers or lack of expressivity can be
mitigated by a Patchscope. Beyond unifying prior inspection techniques,
Patchscopes also opens up new possibilities such as using a more capable model
to explain the representations of a smaller model, and unlocks new applications
such as self-correction in multi-hop reasoning.