AI BriefingThe Generative AI Era: Technical Directions for Large Language Models (LLMs) - LG AI Research BLOG
Key point
This provides a detailed analysis of data training, evaluation, and model architecture for Large Language Models (LLMs), the core of generative AI.
Details
Generative AI technology is boosting productivity for individuals and enterprises and accelerating the achievement of AGI (Artificial General Intelligence). The key factors determining LLM performance are training data, evaluation systems, and model architecture.
The scale of training data is rapidly increasing as model size grows. Web data such as CommonCrawl, Wikipedia, code data based on Github, and specialized books and paper data are used as major sources. Refinement processes such as filtering, deduplication, and de-identification are essential for data quality.
Evaluation data verifies a model's language generation ability, knowledge utilization ability, and reasoning ability from multiple angles. LAMBADA and XSum are used to evaluate language generation ability, Natural Questions is used to evaluate knowledge utilization, and PiQA and GSM8K are used to evaluate logical reasoning ability.
Pre-trained Language Models are divided into encoder, decoder, and encoder-decoder types depending on their structure. Recently, Decoder models such as the GPT series have been gaining attention. In particular, large models demonstrate high performance across diverse tasks through In-context Learning without additional training.
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