2 citations · 2 across the 4 of their papers we have counts for
4 papers
The KL3M Data Project: Copyright-Clean Training Resources for Large Language Models
Michael J Bommarito, Jillian Bommarito, Daniel Martin Katz
Practically all large language models have been pre-trained on data that is subject to global uncertainty related to copyright infringement and breach of contract. This creates pot…
Precise Legal Sentence Boundary Detection for Retrieval at Scale: NUPunkt and CharBoundary
Michael J Bommarito, Daniel Martin Katz, Jillian Bommarito
We present NUPunkt and CharBoundary, two sentence boundary detection libraries optimized for high-precision, high-throughput processing of legal text in large-scale applications su…
KL3M Tokenizers: A Family of Domain-Specific and Character-Level Tokenizers for Legal, Financial, and Preprocessing Applications
Michael J Bommarito, Daniel Martin Katz, Jillian Bommarito
We present the KL3M tokenizers, a family of specialized tokenizers for legal, financial, and governmental text. Despite established work on tokenization, specialized tokenizers for…
Towards Best Practices for Open Datasets for LLM Training
Stefan Baack, Stella Biderman, Kasia Odrozek +36
Many AI companies are training their large language models (LLMs) on data without the permission of the copyright owners. The permissibility of doing so varies by jurisdiction: in…