Frame Representation Hypothesis: Multi-Token LLM Interpretability and
  Concept-Guided Text Generation
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
675901290af2c04c55dea9e2
Pedro H. V. Valois
admin_assigned
2024-12-11T13:22:25.202Z
658e9a59a02954c982dda930
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/lUtXFdHAJE6wE0t5miyeP.png
Pedro H. V. Valois
user
pvalois
675901290af2c04c55dea9e3
Lincon S. Souza
675901290af2c04c55dea9e4
Erica K. Shimomoto
675901290af2c04c55dea9e5
Kazuhiro Fukui
Frame Representation Hypothesis: Multi-Token LLM Interpretability and
  Concept-Guided Text Generation
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
A framework extending the Linear Representation Hypothesis to multi-token words improves interpretability and control of Large Language Models, demonstrating applications in guided text generation and bias detection.
Large Language Models (LLMs) Frame Representation Hypothesis Linear Representation Hypothesis (LRH) multi-token words frames vector sequences Top-k Concept-Guided Decoding Llama 3.1 Gemma 2 Phi 3 gender biases language biases harmful content interpretability control safety transparency