Face-Based Attention Circuits (FBAC): A Theoretical Framework for Context-Aware Embeddings - [MI Research] - [M.F.D.M]
A theoretical framework aimed at improving the adaptability, interpretability, and efficiency of token embeddings in machine learning. Motivated by the limitations of high-dimensional static embedding spaces, FBAC conceptualizes each embedding as a multi-faceted “cube,” in which each face specializes in a distinct linguistic or modal property (e.g., semantics, syntax, pragmatics, cross-modal features). An attention mechanism identifies which faces are most relevant for a given context. Upon this identification, a sparse unfolding of the cube is performed so that only the activated geometric path is “flattened” and integrated into a geometric lattice activation circuit. By modularly structuring embeddings and leveraging dynamic activation, FBAC may offer improved interpretability—facilitating clearer analysis of how various linguistic features influence model decisions—and provide context-aware adaptation without the need for task-specific embeddings.
This repo will contain the theoretical foundations of FBAC, outline its hypothesized benefits, and discusses principal challenges and future research directions.
FBAC is based on the hypothesis that a structured, modular approach to embedding representation leads to more efficient, adaptable, and interpretable models.
The potential benefits that could arise include:
By encoding multi-faceted information within a single embedding cube, FBAC can capture a broader range of linguistic or modal properties with less redundancy.
The selective activation and sparse unfolding enable a single embedding to fulfill different roles across contexts and tasks without relying on specialized embeddings for each scenario.
The modular design provides a clearer view of how different linguistic features contribute to a model’s decisions. Visualizing activated faces or analyzing the geometric lattice pathways can yield more transparent insights into the embedding process.
Potential exists for computational and memory savings by activating only the relevant faces and focusing attention on core token attributes, thus reducing the overhead associated with uniform, high-dimensional embeddings.
The “Embedding Cube” forms the foundation of FBAC. Each token is represented by this multi-faceted cube, where each face corresponds to a distinct attention circuit specialized for a specific linguistic or modal property.
Encodes the core meaning of the token and its relationships with other words, potentially using techniques such as distributional semantics or knowledge graph embeddings.
Captures the grammatical role of the token, encoding information such as part-of-speech tags, dependency relations, and grammatical functions.
Represents the token’s role within its immediate context, capturing relationships with surrounding words and phrases (e.g., via recurrent or convolutional neural networks).
For multi-modal tasks, encodes information from other modalities, such as visual or auditory features.
Focuses on aspects related to intended meaning and communicative function, such as sentiment, irony, or speaker intent.
The number and type of faces can be tailored to specific applications and domains.
