Starting from Grobid's XML output, peS2o filters papers that are too short, have453 incorrect metadata, are in languages other than English, and contain OCR errors using a combination454 of heuristic-and model-based filtering steps. We refer the reader to the datasheet and code for more455 details on this processing pipeline.456 The subset of peS2o included in the Common Pile starts from v3 of the corpus, which contains457 documents from January 1, 1970 to October 6, 2024. We retain full-text papers with CCBY,458 CCBY-SA, or CC0 licenses, or that have been labeled as public domain; metadata is provided459 by the Semantic Scholar APIs [85]. After filtering, this set contains 6.3 million papers, or 35.7460 billion whitespace-separated segments.

Training LLMs on openly licensed text presents a first step towards addressing these issues, but prior data collection efforts have yielded datasets too small or low-quality to produce performant LLMs. To address this gap, we collect, curate, and release the Common Pile v0.1, an eight terabyte collection of openly licensed text designed for LLM pretraining. The Common Pile comprises content from 30 sources that span diverse domains including research papers, code, books, encyclopedias, educational materials, audio transcripts, and more. Crucially, we validate our efforts by training two 7 billion parameter LLMs on text from the Common Pile: Comma v0.1-1T and Comma v0.1-2T, trained on 1 and 2 trillion tokens respectively. Both models attain competitive performance to LLMs trained on unlicensed text with similar computational budgets, such as Llama 1 and 2 7B. In addition to releasing the Common Pile v0.1 itself, we also release the code used in its creation as well as the training mixture and checkpoints for the Comma v0.1 models.

Our aim of this paper is to develop new map matching algorithms and to improve upon previous work. We address two key approaches: Analytic Hierarchy Process (AHP) map matching and fuzzy logic map matching. AHP is a decision-making method that combines mathematical analysis with human judgment, and fuzzy logic is an approach to computing based on the degree of truth and aims at modeling the imprecise modes of reasoning from 0 to 1 rather than the usual boolean logic. Of these algorithms, the way of our applying AHP to map matching is newly developed in this paper, meanwhile, our application of fuzzy logic to map matching is mostly the same as existing research except for some small changes. Because of the common characteristic that both methods are designed to handle imprecise information and simplicity for implementation, we decided to use these methods.