arXiv:2605.27774v1 Announce Type: new Abstract: In-context learning \ -- performing tasks based on examples given in the prompt \ -- is an important capability that has emerged in large language models and has received significant attention in both theory and practice. Existing theoretical work often focuses on settings where the learning uses information purely from the prompt. However, many practical instances of in-context learning require the model to retrieve factual knowledge stored in the model's parameters, with the context serving to identify which knowledge is relevant. In this work,
The paper directly addresses a core mechanism of how large language models function, specifically the interplay between in-context learning and stored factual knowledge.
Understanding the fine-tuning dynamics of factual recall in transformers is critical for developing more reliable, controllable, and efficient AI models.
This research provides deeper insight into how AI models learn and retrieve information, potentially leading to advancements in model architecture and training methodologies.
- · AI researchers
- · Large language model developers
- · AI-driven product companies
- · Developers of less robust AI training techniques
- · AI models prone to fact-hallucination
Improved understanding of in-context learning's dependence on factual memory within transformers.
Development of more accurate and steerable AI systems with enhanced factual recall capabilities.
Accelerated deployment of AI agents in domains requiring high factual fidelity and reduced 'AI hallucination'.
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Read at arXiv cs.LG