In recent years, AI technology has developed rapidly, and "Large-Scale Language Models (LLM)" in particular have been attracting attention. LLM is a language model that learns from large-scale text data and performs all kinds of language processing with just a few tasks.
For example, OpenAI's ChatGPT generates diverse and complex sentences by learning huge amounts of data in natural language processing. ChatGPT is based on GPT-3, one of the LLMs, and has learned about 45 terabytes of text data.
Therefore, LLM utilization requires a large amount of data collection. In this article, we will explain the role and process of data acquisition in LLM, and points to note using specific examples and data.
Data ingestion process
Generating large-scale language models requires collecting and processing vast amounts of language data. Here we explain the specific process.
1. Acquisition of data
Large-scale language models (LLMs) learn using text data on the Internet. As a method of data collection, web scraping and data acquisition from API are common.
Data acquisition via way of means of internet
Web scraping is a computer technique that extracts specific information from web pages. For example, you can collect text and numerical data from news sites, Wikipedia, etc. Programming development and scraping libraries such as Python are widely used for such data collection.
b. Get data from API
API (Application Programming Interface) is a mechanism for connecting programs to ensure compatibility. For example, web services such as Twitter (The X) and ChatGPT provide their own data via API, so you can collect data efficiently by using these.
In addition, APIs may have request limits and data volume limits, but it is possible to obtain a large amount of data by responding appropriately to these.
2. Data cleaning
The collected data may contain HTML tags and useless information. Data cleaning is the work of removing these and shaping them into a format suitable for the model. There are two main methods of data cleaning.
a. Removal of HTML tags
Some of the data collected by web scraping may contain HTML tags. These are pieces of information that LLM does not use and should be removed. For example, you can easily strip HTML tags using Python libraries.
b. filtering information
Text data may include information unrelated to LLM, such as advertisements and spam. Therefore, when working with LLM, it is important to pre-filter the information and ensure only high-quality data.
For example, regular expressions can be used to filter out certain patterns of text, or natural language processing tools can be used to analyze text content to remove irrelevant information.
3. Data conversion
After data cleaning, we need to convert the retrieved text data into a format that LLM can understand. Spacing and vectorization are done in this process.
a. Separation of text data
Spacing is the process of dividing text data into words and separating the words with spaces. This allows the language model to treat each word or sentence as an independent element.
However, unlike English, the division between words in Japanese is not clear, so it is not easy to process on a program. Libraries such as MeCab and Janome are used for Japanese spaces.
b. Vectorization of text data
Converting text data into a vector of numbers is called "vectorization". Vectorization is one of the text preprocessing techniques mainly used in natural language processing.
This allows the language model to parse text data using mathematical operations. Common vectorization techniques include TF-IDF, Word2Vec, and BERT. These techniques are used to convert text data into a format that the model can handle.
4. Data storage
The final step in data ingestion is to save the data. The captured data is stored in databases and files.
database
A database is a mechanism for efficiently managing data. For example, relational databases such as MySQL and PostgreSQL may be used. This makes it easy to search and update large amounts of data
File
Data may be saved in formats such as text files, CSV files, and JSON files. These file formats are easy to handle data and compatible with various tools. For example, you can easily read and write CSV and JSON files using Python's pandas library.
Notes on importing data
Ensuring data quality
During data ingestion, it is important to maintain data quality. Using high-quality data improves the accuracy of your language model. For example, GPT-3 has learned about 45 terabytes of high-quality text data from the Internet. As a result, it has high natural language generation ability.
Standardization of data format
Standardizing the data format is important for data acquisition. Using a standardized format makes the data easier to handle and less error-prone.
For example, by unifying the format of text data stored in a database, data processing and analysis can be performed efficiently.
pay attention to copyright law
It is also important to pay attention to ethics and legal regulations related to data ingestion. In Japan, in principle, when collecting data for building a large-scale language model, it can be used without the permission of the copyright holder. (Article 30-4 of the Copyright Act)
However, it does not mean that you can use the copyrighted work indefinitely. Use without the permission of the copyright holder is prohibited if it unfairly harms the type of data to be obtained, its intended use, or the interests of the copyright holder.
There is also concern about the issue of bias. It is important to build a language model that considers fairness and eliminates social prejudices in the process of data ingestion.
Summary
Data ingestion is an essential process for improving the performance of Large Language Models (LLMs). Proper data collection, cleaning, transformation, and storage ensure high-quality data ingestion.
In addition, more effective data ingestion can be achieved by paying attention to ensuring data quality and standardizing formats. Through concrete examples and explanations using data, you will be able to understand the importance and methods of data capture, and effectively work on the development and improvement of large-scale language models.
In the future, it is expected that the technology of large-scale language models will further evolve and the range of applications will expand. As technology advances, data ingestion methods and tools will continue to evolve.
Utilizing the latest technology is expected to further improve the efficiency and quality of data import. Paying attention to the latest technology trends and continuing to achieve efficient and high-quality data ingestion will lead to the success of large-scale language models.
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