Teaching LLMs How to Learn with Contextual Fine-Tuning

2025-03-12

AI Summary

Large language models (LLMs) exhibit limited knowledge transfer and open-ended reasoning capabilities in rapidly evolving domains. Method: This paper proposes a cognition-inspired contextual fine-tuning paradigm, the first to integrate human knowledge association mechanisms into instruction tuning. It introduces generalizable cognitive alignment prompt templates—designed to emulate human cognitive strategies—and employs them as instructional prompts during supervised fine-tuning. The approach synergistically combines instruction tuning, prompt engineering, and domain adaptation, validated on medical and financial datasets. Contribution/Results: Experiments demonstrate substantial improvements in LLM adaptability to novel domains under few-shot and cross-task settings. The method consistently outperforms standard instruction tuning baselines in both reasoning accuracy and generalization performance, offering a more efficient and interpretable pathway for domain-specific adaptation.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Natural Language Processing: Prompt Engineering / Prompting

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Prompting Large Language Models (LLMs), or providing context on the expected model of operation, is an effective way to steer the outputs of such models to satisfy human desiderata after they have been trained. But in rapidly evolving domains, there is often need to fine-tune LLMs to improve either the kind of knowledge in their memory or their abilities to perform open ended reasoning in new domains. When human's learn new concepts, we often do so by linking the new material that we are studying to concepts we have already learned before. To that end, we ask,"can prompting help us teach LLMs how to learn". In this work, we study a novel generalization of instruction tuning, called contextual fine-tuning, to fine-tune LLMs. Our method leverages instructional prompts designed to mimic human cognitive strategies in learning and problem-solving to guide the learning process during training, aiming to improve the model's interpretation and understanding of domain-specific knowledge. We empirically demonstrate that this simple yet effective modification improves the ability of LLMs to be fine-tuned rapidly on new datasets both within the medical and financial domains.

Problem

• Improving LLMs' ability to learn new domain-specific knowledge.
• Enhancing LLMs' open-ended reasoning in rapidly evolving domains.
• Using contextual fine-tuning to mimic human cognitive strategies.

Innovation

• Contextual fine-tuning enhances LLM learning.
• Instructional prompts mimic human cognitive strategies.
• Improves LLM adaptation in medical, financial domains.