title: Frame Representation Hypothesis: Multi-Token LLM Interpretability and
    Concept-Guided Text Generation
summary: Interpretability is a key challenge in fostering trust for Large Language
  Models (LLMs), which stems from the complexity of extracting reasoning from
  model's parameters. We present the Frame Representation Hypothesis, a
  theoretically robust framework grounded in the Linear Representation Hypothesis
  (LRH) to interpret and control LLMs by modeling multi-token words. Prior
  research explored LRH to connect LLM representations with linguistic concepts,
  but was limited to single token analysis. As most words are composed of several
  tokens, we extend LRH to multi-token words, thereby enabling usage on any
  textual data with thousands of concepts. To this end, we propose words can be
  interpreted as frames, ordered sequences of vectors that better capture
  token-word relationships. Then, concepts can be represented as the average of
  word frames sharing a common concept. We showcase these tools through Top-k
  Concept-Guided Decoding, which can intuitively steer text generation using
  concepts of choice. We verify said ideas on Llama 3.1, Gemma 2, and Phi 3
  families, demonstrating gender and language biases, exposing harmful content,
  but also potential to remediate them, leading to safer and more transparent
  LLMs. Code is available at
  https://github.com/phvv-me/frame-representation-hypothesis.git
paper_authors._id: 675901290af2c04c55dea9e2
paper_authors.name: Pedro H. V. Valois
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-11T13:22:25.202Z
paper_authors._id: 658e9a59a02954c982dda930
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/lUtXFdHAJE6wE0t5miyeP.png
paper_authors.fullname: Pedro H. V. Valois
paper_authors.type: user
paper_authors.user: pvalois
paper_authors._id: 675901290af2c04c55dea9e3
paper_authors.name: Lincon S. Souza
paper_authors._id: 675901290af2c04c55dea9e4
paper_authors.name: Erica K. Shimomoto
paper_authors._id: 675901290af2c04c55dea9e5
paper_authors.name: Kazuhiro Fukui
paper_title: Frame Representation Hypothesis: Multi-Token LLM Interpretability and
    Concept-Guided Text Generation
paper_summary: Interpretability is a key challenge in fostering trust for Large Language
  Models (LLMs), which stems from the complexity of extracting reasoning from
  model's parameters. We present the Frame Representation Hypothesis, a
  theoretically robust framework grounded in the Linear Representation Hypothesis
  (LRH) to interpret and control LLMs by modeling multi-token words. Prior
  research explored LRH to connect LLM representations with linguistic concepts,
  but was limited to single token analysis. As most words are composed of several
  tokens, we extend LRH to multi-token words, thereby enabling usage on any
  textual data with thousands of concepts. To this end, we propose words can be
  interpreted as frames, ordered sequences of vectors that better capture
  token-word relationships. Then, concepts can be represented as the average of
  word frames sharing a common concept. We showcase these tools through Top-k
  Concept-Guided Decoding, which can intuitively steer text generation using
  concepts of choice. We verify said ideas on Llama 3.1, Gemma 2, and Phi 3
  families, demonstrating gender and language biases, exposing harmful content,
  but also potential to remediate them, leading to safer and more transparent
  LLMs. Code is available at
  https://github.com/phvv-me/frame-representation-hypothesis.git
paper_ai_keywords: Frame Representation Hypothesis Linear Representation Hypothesis multi-token words ordered sequences of vectors token-word relationships concepts Top-k Concept-Guided Decoding text generation gender and language biases harmful content safer and more transparent LLMs