arXiv:2605.22981v1 Announce Type: cross Abstract: Fill-in-the-middle (FIM) is a pretraining objective widely used to equip causal language models with infilling ability, yet its effect on verbatim memorization remains underexplored. We study the memorization dynamics of FIM in a controlled setting by pretraining matched Llama 3.2 models with FIM and standard left-to-right (LTR) objectives on a FineWeb-Gutenberg corpus containing repeated Gutenberg excerpts. With prefix-based probes, FIM more often recovers short or partially matching spans, while LTR more often assigns high confidence to long
This research builds on contemporary understanding of pretraining objectives in large language models, specifically addressing the under-explored area of memorization dynamics with fill-in-the-middle techniques.
Understanding how different pretraining objectives impact memorization is critical for developing more robust, less biased, and ultimately more reliable AI models, with implications for safety and intellectual property.
The findings suggest that the choice of pretraining objective (FIM vs. LTR) significantly influences the type and extent of verbatim memorization, guiding future model development strategies.
- · AI researchers
- · LLM developers
- · Companies seeking explainable AI
- · Data privacy advocates
- · Developers ignoring pretraining impact
- · Models prone to undesirable memorization
Further research and development will focus on mitigating unwanted memorization in FIM-trained models while retaining their infilling capabilities.
Improved pretraining methodologies could lead to more efficient and ethically sound deployment of advanced AI agents across industries.
A deeper understanding of memorization might influence intellectual property laws and data governance policies related to AI training datasets.
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Read at arXiv cs.LG