title: Patchscope: A Unifying Framework for Inspecting Hidden Representations
    of Language Models
summary: 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.
paper_authors._id: 65a0a8ae33d5d9ca30dbb594
paper_authors.name: Asma Ghandeharioun
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paper_authors.name: Avi Caciularu
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paper_authors.fullname: Avi Caciularu
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paper_authors.user: codevan
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paper_authors.name: Adam Pearce
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paper_authors.fullname: Adam Pearce
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paper_authors._id: 65a0a8ae33d5d9ca30dbb597
paper_authors.name: Lucas Dixon
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paper_authors.fullname: Lucas Dixon
paper_authors.type: user
paper_authors.user: iislucas
paper_authors._id: 65a0a8ae33d5d9ca30dbb598
paper_authors.name: Mor Geva
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paper_authors.fullname: Mor Geva
paper_authors.type: user
paper_authors.user: mega
paper_title: Patchscope: A Unifying Framework for Inspecting Hidden Representations
    of Language Models
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) hidden representations human-understandable text Patchscopes research questions computation interpretability methods vocabulary space intervention early layers expressivity self-correction multi-hop