Biological Science Large Model Survey

Competitive product research on large biological models

1 Concept classification

• Large model: A large model usually refers to a machine learning model with a large number of parameters and deep layers, such as a deep neural network. These models have a large number of trainable parameters, and by training on large-scale datasets, they can better capture complex patterns and features in the data. Large models are widely used in various fields , including natural language processing, computer vision, speech recognition, etc. There is no clear limit for "big" here , and parameters from 0.x B to x00 B can be called large models.

• Large language model: A large language model refers to a language sequence processing model with large-scale training parameters. These models are trained at scale to understand and generate sequences of language. Although large language models are mainly used to process natural language text, in some cases, they can also be used to process non-natural language data , such as programming languages, proteins, domain-specific terminology, etc.

• Natural language model: specifically refers to a large language model whose goal is to simulate the ability of human language understanding and generation . Can be used in a variety of fields, including:

  • Machine Translation: Translating one natural language into another.

  • Text summarization: Extract key information from long texts and generate concise summaries.

  • Question Answering System: Answers questions posed by users and provides accurate answers based on the context of the text.

  • Text generation: Generate natural language texts such as articles, stories, and dialogues.

  • Sentiment analysis: analyze the emotional tendency in the text, such as positive, negative, neutral, etc.

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