paper-with-me

홈 › Papers

Memorization Dynamics of Fill-in-the-Middle Pretraining

2026-05-21 · Tobias von Arx, Tanguy Dieudonné arxiv

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 exact continuations. We observe that verbatim extraction under FIM-training grows approximately linearly with repetitions over the tested range. Evaluating native FIM-format probes reveals that suffix context is not sufficient: verbatim recall under FIM-training remains strongly anchored in prefix context. Our results also show that evaluating only one span length or probing format can miss important nuances in memorization behavior.

📄 PDF Abstract BibTeX arXiv:2605.22981

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

Structure-Aware Fill-in-the-Middle Pretraining for Code

2025-05-30 · Linyuan Gong, Alvin Cheung, Mostafa Elhoushi, Sida Wang

Fill-in-the-Middle (FIM) is a common pretraining method for code LLMs, where models complete code segments given surrounding context. However, existing LLMs treat code as plain text and mask random character spans. We pr…

Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks

2024-03-07 · Linyuan Gong, Sida Wang, Mostafa Elhoushi, Alvin Cheung

We introduce Syntax-Aware Fill-In-the-Middle (SAFIM), a new benchmark for evaluating Large Language Models (LLMs) on the code Fill-in-the-Middle (FIM) task. This benchmark focuses on syntax-aware completions of program s…

Code Completion

Generalization v.s. Memorization: Tracing Language Models' Capabilities Back to Pretraining Data

2024-07-20 · Xinyi Wang, Antonis Antoniades, Yanai Elazar, Alfonso Amayuelas 외

The impressive capabilities of large language models (LLMs) have sparked debate over whether these models genuinely generalize to unseen tasks or predominantly rely on memorizing vast amounts of pretraining data. To expl…

Language ModellingMachine TranslationMathMemorization+4

Continual Memorization of Factoids in Large Language Models

2024-11-11 · Howard Chen, Jiayi Geng, Adithya Bhaskar, Dan Friedman 외

Large language models can absorb a massive amount of knowledge through pretraining, but pretraining is inefficient for acquiring long-tailed or specialized facts. Therefore, fine-tuning on specialized or new knowledge th…

Memorization

MosaicDiff: Training-free Structural Pruning for Diffusion Model Acceleration Reflecting Pretraining Dynamics

2025-10-13 · Bowei Guo, Shengkun Tang, Cong Zeng, Zhiqiang Shen arxiv

Diffusion models are renowned for their generative capabilities, yet their pretraining processes exhibit distinct phases of learning speed that have been entirely overlooked in prior post-training acceleration efforts in…