The text discusses the importance of data in machine learning and the challenges associated with training models on large datasets. It introduces a tool called WIMBD (What’s in My Big Data) that helps researchers examine the contents of large text corpora. The tool includes an Elasticsearch-based search tool and a MapReduce-built count capability for analyzing datasets. The authors conducted multiple studies using WIMBD on various corpora. The analyses are categorized into data statistics, data quality, community and society relevant measurements, and cross-corpora analysis. The tool provides insights on data distribution and anomalies, enabling better curation of corpora for higher-quality models.
Peeking Inside Pandora’s Box: Unveiling the Hidden Complexities of Language Model Datasets with ‘What’s in My Big Data’? (WIMBD)
Machine learning relies on data as its building block. New datasets are crucial for research and developing innovative models. However, training larger models on larger datasets has increased the computing cost of AI experiments. Some influential datasets are produced from the internet without documentation of their contents. This lack of knowledge poses challenges as language models are widely used and have a direct impact on people’s lives. Understanding the advantages and disadvantages of these models is critical.
To address this, researchers have developed a collection of tools called WIMBD: WHAT’S IN MY BIG DATA. WIMBD helps machine learning practitioners examine massive language datasets and compare them. It consists of an Elasticsearch (ES) index-based search tool and a MapReduce-built count capability. These tools allow for rapid iteration and analysis of large text corpora.
Key Features of WIMBD:
- Search Tool: The Elasticsearch-based search tool enables programmatic access to search for documents containing specific queries.
- Count Capability: The MapReduce-built count capability facilitates rapid iteration and extraction of relevant data, such as document character lengths, duplicates, domain counts, and identification of personally identifiable information (PII).
WIMBD has been used to analyze 10 different corpora, including C4, The Pile, and RedPajama. The analyses are categorized into data statistics, data quality, community- and society-relevant measurements, and cross-corpora analysis. The insights gained from WIMBD help in curating higher-quality corpora and understanding model behavior.
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