Probabilistic Subspace Manifolds for Contextual Inference in Large Language Models
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910917660246016 |
|---|---|
| author | Nightingale, Christopher Lavington, Dominic Thistlethwaite, Jonathan Penhaligon, Sebastian Belinski, Thomas Boldo, David |
| author_facet | Nightingale, Christopher Lavington, Dominic Thistlethwaite, Jonathan Penhaligon, Sebastian Belinski, Thomas Boldo, David |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_05346 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Probabilistic Subspace Manifolds for Contextual Inference in Large Language Models Nightingale, Christopher Lavington, Dominic Thistlethwaite, Jonathan Penhaligon, Sebastian Belinski, Thomas Boldo, David Computation and Language 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. |
| title | Probabilistic Subspace Manifolds for Contextual Inference in Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2502.05346 |