Probabilistic Subspace Manifolds for Contextual Inference in Large Language Models

TL;DR

This paper introduces probabilistic subspace manifolds for token embeddings in large language models, enhancing contextual inference, robustness, and adaptability while maintaining computational efficiency.

Contribution

It proposes a novel probabilistic embedding framework that improves semantic coherence, robustness, and domain adaptability in large language models.

Findings

Enhanced neighborhood consistency and reduced redundancy in embeddings.

Increased robustness against adversarial and perturbation-based attacks.

Better domain adaptation with minimal retraining requirements.

Abstract

Representing token embeddings as probability distributions over learned manifolds allows for more flexible contextual inference, reducing representational rigidity while enhancing semantic granularity. Comparative evaluations demonstrate that probabilistic embeddings improve neighborhood consistency and decrease redundancy, ensuring that token relationships remain more structurally coherent across fine-tuning iterations. The integration of probabilistic subspaces within attention mechanisms facilitates more adaptive contextual weighting, enabling models to capture latent dependencies that would otherwise be obscured in conventional embeddings. Experimental results highlight increased robustness against adversarial modifications, with probabilistic embeddings preserving contextual integrity even under perturbation-based evaluation scenarios. Performance assessments indicate that probabilistic representations achieve greater adaptability in domain-specific applications, mitigating the need for extensive retraining when shifting across linguistic domains. Computational trade-offs remain within operationally feasible limits, with marginal increases in inference latency balanced against the benefits of enhanced representation stability and contextual expressiveness. The capacity to encode structured uncertainty provides advantages in generative modeling tasks, particularly where maintaining coherence across extended sequences requires a representation framework capable of handling ambiguous or context-dependent linguistic constructs.