Abstract
A Six-Dimensional Taxonomy of Post-Training Adaptation Techniques with Applications in AI Governance
- Authors
- Fardin Afdideh, Fernando Seoane, Farhad Abtahi
- Categories
- cs.LG
Brief
The survey synthesizes post-training adaptation literature and presents a six-dimensional taxonomy (mechanism, goal, data requirement, persistence, structural scope, model type) to disambiguate methods such as fine-tuning, retrieval augmentation, and prompting. It maps method relationships and historical evolution across model classes, offers a vocabulary to support documentation and governance, and flags evaluation, reproducibility, inference-time adaptation, unlearning, multimodal adaptation, and workflow challenges.
Why it matters
Introduces a six-dimensional taxonomy for post-training adaptation (mechanism, goal, data requirement, persistence, structural scope, model type) to classify techniques like retraining, fine-tuning, parameter-efficient methods, alignment, retrieval augmentation, model editing, unlearning, calibration, and multimodal instruction tuning.
Key details
- Clarifies commonly conflated terms (fine-tuning vs. retrieval augmentation vs. prompting), maps technique relationships (inheritance, supersession, hybridization, layered deployment stacks), and traces adaptation evolution from traditional ML → deep learning → foundation models → LLMs → multimodal LLMs.
- Paper by Fardin Afdideh, Fernando Seoane, and Farhad Abtahi (arXiv:2608.06246v1), published 2026-08-06, proposes a governance-oriented vocabulary for documentation and model-change tracking and highlights open challenges: evaluation, reproducibility, persistent inference-time adaptation, unlearning, multimodal adaptation, and governance-aware workflows.