Frame Representation Hypothesis: Multi-Token LLM Interpretability and Concept-Guided Text Generation (2412.07334v2)

Abstract: Interpretability is a key challenge in fostering trust for 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

• The paper introduces the Frame Representation Hypothesis to represent multi-token words as ordered frames, enabling a more precise mapping of linguistic concepts in LLMs.
• It establishes a Semantic Frame Space and Concept Frames that aggregate token sequences into meaningful clusters for improved concept interpretation.
• The study demonstrates Top-k Concept-Guided Decoding to steer text generation, reducing biases and enhancing operational control in LLM outputs.

Frame Representation Hypothesis: Multi-Token LLM Interpretability and Concept-Guided Text Generation

This paper introduces the Frame Representation Hypothesis (FRH) as an advanced framework extending the existing Linear Representation Hypothesis (LRH) to achieve improved interpretability and operational control in LLMs. While the LRH connects linguistic concepts to LLM representations, its reliance on single-token analysis limits its application, particularly in languages where many words are multi-token. The core contribution of this research is to propose that multi-token words in LLMs are better represented as frames—sequences of vectors rather than single vectors—thereby allowing for a more accurate mapping of linguistic concepts and enabling a broader range of interpretability tasks.

Key Contributions

1. The Concept of Framing: The paper defines a word as a multidimensional frame, an ordered sequence of token vectors. This framing method proposes that words and concepts can be better represented in LLMs by treating token sequences as geometrical frames. This is an extension beyond the simplistic linear operations on single-token vectors suggested by LRH.
2. Semantic Frame Space and Concept Frames: The authors introduce the idea of a Semantic Frame Space constituted by all word frames and propose Concept Frames as centroids of sets of word frames sharing a common linguistic concept. The practical implication is that these frames enable the aggregation and representation of complex concepts that span multiple tokens.
3. Top-$k$ Concept-Guided Decoding: Leveraging the new framework, the paper presents an application that uses Concept Frames to guide the text generation process in LLMs. This technique selects tokens that maximize a chosen concept during text generation, addressing issues like gender and language biases and potentially harmful content generation by LLMs.

Methodological Approach

The authors ground their hypothesis in manifold theory by considering words as elements of the non-compact Stiefel manifolds—spaces that allow an ordered representation of vector sequences. Through mathematical frameworks and empirical analyses on Llama 3.1, Gemma 2, and Phi 3 model families, it is verified that over 99% of tested words exhibit linear independence among their token vectors, supporting the full applicability of the Frame Representation Hypothesis.

Empirical Results

• High Relevance of Frame Structures: The analysis reveals that multi-token words are effectively modeled as frames with significant linear independence. The translation of words into frames, and further into Concept Frames for semantic interpretation, shows stronger and more meaningful clustering than traditional single-token interpretations.
• Impact on Text Generation: Utilizing Top-$k$ Concept-Guided Decoding, the researchers demonstrate how LLM outputs can be steered towards or away from specific concepts. The experimental results underline the method's ability to modify biases in LLM outputs, as shown in practical examples with gender-related concepts, showcasing the model's predisposition and corrective steering using the FRH framework.

Implications and Future Work

Practically, the adoption of FRH can enhance the transparency and control over LLM outputs, suggesting its integration into domains where understanding model biases and intent is crucial. Theoretically, FRH broadens the potential for LLM interpretability by providing a framework capable of exploring higher-order conceptual mappings in NLP applications.

The research opening up future directions includes potential automated concept extraction without reliance on pre-existing ontological databases like WordNet, the exploration of concept hierarchies beyond simple frames, and advanced applications in cognitive modeling and artificial ontology development within LLMs.

In summary, while the Frame Representation Hypothesis does not discuss revolutionary concepts, its introduction allows for an extended understanding and management of linguistic concepts in LLMs. This can serve as a stepping stone for future innovations in creating safer, more unbiased LLM models equipped for a variety of cognitive tasks.