How do you add domain expertise without losing general reasoning?

Methods for adapting LLMs to specific domains, their hidden costs, and why specialization inevitably trades breadth for depth.

Topic Hub · 18 linked notes · 4 sections

View as

Sub-Topic Maps

1 note

How do domain training techniques actually reshape model behavior?

What methods best inject specialized domain knowledge into language models, and what hidden costs do they carry? This explores the trade-offs between depth, generalization, and reasoning quality.

Foundational Frameworks

5 notes

Does model access level determine which specialization techniques work?

Different specialization approaches require different levels of access to a model's internals. Understanding this constraint helps practitioners choose realistic techniques for their domain adaptation goals.

How do knowledge injection methods trade off flexibility and cost?

When and how should domain knowledge enter an AI system? This explores the speed, training cost, and adaptability trade-offs across four injection paradigms, and when each approach suits different deployment constraints.

Can prompt optimization teach models knowledge they lack?

Explores whether sophisticated prompting techniques can inject new domain knowledge into language models, or if they're limited to activating existing training knowledge.

Why do specialized models fail outside their domain?

Deep domain optimization creates sharp performance cliffs at domain boundaries. Specialized models generate plausible-sounding but ungrounded responses when queries fall outside their training scope, and often fail to signal their own ignorance.

Can organizing knowledge structures beat raw training data volume?

Does structuring domain knowledge into taxonomies during training enable models to learn more efficiently than simply increasing the amount of training data? This challenges assumptions about scaling knowledge injection.

Reasoning Quality in Domain Contexts

6 notes

Does supervised fine-tuning actually improve reasoning quality?

While SFT boosts final-answer accuracy, does it degrade the quality and informativeness of the reasoning steps that justify those answers? This matters for high-stakes domains requiring auditable decision-making.

Does RL improve domain reasoning by adding knowledge or removing it?

When reinforcement learning improves reasoning in specialized domains like medicine, is it teaching models new facts or preventing them from using wrong ones? Understanding this distinction matters for how we design RL training.

Can simple rewards alone teach complex domain reasoning?

Does reinforcement learning on difficult problems with basic accuracy rewards produce sophisticated reasoning strategies without explicit chain-of-thought training? This challenges assumptions about what domain AI models need to learn effectively.

Why doesn't mathematical reasoning transfer to medicine?

Can models trained to reason well about math apply those skills to medical domains through fine-tuning? This explores whether reasoning ability is truly domain-agnostic or constrained by domain-specific knowledge requirements.

Does medical AI need knowledge or reasoning more?

Medical and mathematical domains may require fundamentally different AI training priorities. If medical accuracy depends primarily on factual knowledge while math depends on reasoning quality, should we build and evaluate these systems differently?

Does training objective determine which direction models fail at abstention?

Calibration failures might not be universal—different training approaches could push models toward opposite extremes of refusing or overconfidently answering. Understanding whether the training objective, not just model capability, drives these failures could reshape how we think about fixing them.