arXiv subjects

Allison Casasola

Publications and source records attributed to Allison Casasola.

3 recordsLinked to original sources

arXiv2026

Estimating near-verbatim extraction risk in language models with decoding-constrained beam search

Recent work shows that standard greedy-decoding extraction methods for quantifying memorization in LLMs miss how extraction risk varies across sequences. Probabilistic extraction -- computing the probability of generating a target suffix given a prefix under a decoding scheme -- addresses this, but is tractable only for verbatim memorization, missing near-verbatim instances that pose similar privacy and copyright risks. Quantifying near-verbatim extraction risk is expensive: the set of near-verbatim suffixes is combinatorially large, and reliable Monte Carlo (MC) estimation can require ~100,000 samples per sequence. To mitigate this cost, we introduce decoding-constrained beam search, which yields deterministic lower bounds on near-verbatim extraction risk at a cost comparable to ~20 MC samples per sequence. Across experiments, our approach surfaces information invisible to verbatim methods: many more extractable sequences, substantially larger per-sequence extraction mass, and patterns in how near-verbatim extraction risk manifests across model sizes and types of text.

cs.CL↗

arXiv2026

Extracting memorized pieces of (copyrighted) books from open-weight language models

Plaintiffs and defendants in copyright lawsuits over generative AI often make sweeping, opposing claims about the extent to which large language models (LLMs) memorize protected expression from books in their training data. We show that these polarized positions dramatically oversimplify the relationship between memorization and copyright. To do so, we develop a technique to measure memorization of books, which we apply to 200 books and 14 open-weight LLMs. Through over 3000 experiments, we show that memorization varies both by model and book. With respect to our specific extraction methodology, we find that most LLMs do not memorize most books -- either in whole or in part; however, there are notable exceptions. For instance, Llama 3.1 70B entirely memorizes some books, like Harry Potter and the Sorcerer's Stone; memorization is so extensive that one can deterministically extract the whole book almost verbatim using the book's first few words as an initial prompt. We discuss why our results have significant implications for copyright cases, though not ones that unambiguously favor either side.

cs.CL↗

arXiv2025

Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data

Current trends in pre-training Large Language Models (LLMs) primarily focus on the scaling of model and dataset size. While the quality of pre-training data is considered an important factor for training powerful LLMs, it remains a nebulous concept that has not been rigorously characterized. To this end, we propose a formalization of one key aspect of data quality -- measuring the variability of natural language data -- specifically via a measure we call the diversity coefficient. Our empirical analysis shows that the proposed diversity coefficient aligns with the intuitive properties of diversity and variability, e.g., it increases as the number of latent concepts increases. Then, we measure the diversity coefficient of publicly available pre-training datasets and demonstrate that their formal diversity is high compared to theoretical lower and upper bounds. Finally, we conduct a comprehensive set of controlled interventional experiments with GPT-2 and LLaMAv2 that demonstrate the diversity coefficient of pre-training data characterizes useful aspects of downstream model evaluation performance -- totaling 44 models of various sizes (51M to 7B parameters). We conclude that our formal notion of diversity is an important aspect of data quality that captures variability and causally leads to improved evaluation performance.

cs.CL↗