arXiv:2606.09525v1 Announce Type: cross Abstract: During instruction fine-tuning (IFT), large language models (LLMs) learn to follow instructions by using the provided context to answer a query. While prior work has studied how context characteristics correlate with context usage by the LLM, this analysis has been limited to inference time, leaving open how these relationships are acquired in the first place. Here, we measure how models' sensitivity to such characteristics shifts across successive IFT stages: supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement
The paper investigates the mechanisms by which LLMs acquire sensitivity to context characteristics during different instruction fine-tuning stages, providing insights into their learning process.
Understanding how LLMs learn to interpret context is crucial for improving their reliability, trustworthiness, and control, especially for deployment in critical applications.
This research provides a deeper understanding of the internal mechanics of LLM learning, moving beyond simple correlational studies at inference time.
- · AI researchers
- · LLM developers
- · Companies building on LLM agents
Improved understanding of LLM training dynamics will lead to more efficient and robust fine-tuning methods.
Better context handling may reduce hallucination rates and increase the factual grounding of LLM outputs.
More reliable LLMs could accelerate the adoption of autonomous AI agents across various industries.
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Read at arXiv cs.AI