Frame Representation Hypothesis: Multi-Token LLM Interpretability and Concept-Guided Text Generation
Pedro H. V. Valois, Lincon S. Souza, Erica K. Shimomoto, Kazuhiro Fukui
2024-12-11
Summary
This paper talks about the Frame Representation Hypothesis, a new approach to help understand and control large language models (LLMs) by interpreting words as multi-token frames.
What's the problem?
Large language models are complex and often act like a 'black box,' making it hard to understand how they generate text or make decisions. This lack of interpretability can lead to mistrust, especially when these models produce biased or harmful content. Current methods only look at single tokens (the smallest units of meaning), which limits their ability to capture the full meaning of words that are made up of multiple tokens.
What's the solution?
The authors propose the Frame Representation Hypothesis, which suggests that words can be seen as frames—ordered sequences of vectors that represent the relationships between tokens within a word. This allows for better understanding of how words relate to concepts. They also introduce a method called Top-k Concept-Guided Decoding, which helps guide text generation based on chosen concepts. By applying this framework to various LLMs, they demonstrate improvements in identifying biases and harmful content while also showing potential for addressing these issues.
Why it matters?
This research is important because it enhances the transparency of large language models, making them easier to understand and trust. By improving how we interpret the decisions made by these models, we can work towards creating safer and more reliable AI systems that better serve users and reduce the risk of generating biased or harmful outputs.
Abstract
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