Latent Knowledge as a Predictor of Fact Acquisition in Fine-Tuned Large Language Models

TL;DR

This study investigates how latent knowledge in large language models influences the speed of fact learning and generalization, revealing that latent knowledge predicts rapid acquisition and affects the retention of unseen facts.

Contribution

It introduces a novel analysis of latent knowledge as a predictor of fact acquisition and generalization in fine-tuned large language models using survival analysis methods.

Findings

Latent knowledge strongly predicts faster fact learning.

Fine-tuning significantly improves deterministic recall from 2.8% to 71.9%.

Limited generalization occurs for unseen facts, but is more likely with latent knowledge.

Abstract

Large language models store biomedical facts with uneven strength after pretraining: some facts are present in the weights but are not reliably accessible under deterministic decoding (latent knowledge), while others are scarcely represented. We fine tuned Llama 3.1 8B Instruct to learn ontology term identifier mappings from the Human Phenotype Ontology (800 pairs) and the Gene Ontology (400 training pairs), withholding 400 GO pairs to test generalization. Treating learning as a time to event process across 20 epochs, we used stochastic decoding to detect latent knowledge at baseline and Cox proportional hazards models to identify predictors of acquisition, generalization, and degradation. Baseline deterministic recall for HPO was 2.8%, rising to 71.9% after fine-tuning. Latent knowledge was the strongest predictor of faster fact acquisition (HR 2.6) and was associated with earlier, higher peak learning rates and faster convergence; identifier frequency and curated annotation counts had smaller effects. Generalization to withheld GO facts was uncommon (5.8%) but more likely when latent knowledge was present. Previously correct GO mappings degraded more often for withheld (unseen) terms than for trained (seen) terms, suggesting a protective effect of reinforcement during training. These results show that latent knowledge predicts both the speed of factual learning during fine-tuning and the limited generalization of unseen ontology facts, while resistance to degradation depends on whether facts are reinforced.