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The current wave of LLMs default to conversational natural language — languages that humans communicate in like English. Parsing natural language is an extremely difficult task, no matter how much you pamper a prompt with rules like "respond in the form a bulleted list". Natural language might have structure, but it's hard for typical software to reconstruct it from raw text.output
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#NLP #software
The current wave of LLMs[^1] default to conversational natural language[^2] — languages that humans communicate in like English. Parsing[^3] natural language is an extremely difficult task, no matter how much you pamper a prompt with rules like "respond in the form a bulleted list". Natural language might have structure, but it's hard for typical software[^4] to reconstruct it from raw text.
[^1]: LLMs refers to large language models, which are machine learning models that are trained on vast amounts of text data to generate human-like language output. Examples include GPT-3 and BERT. [source](https://en.wikipedia.org/wiki/Large_language_model)
[^2]: Conversational natural language refers to natural language that is used in everyday conversations between humans, such as English, Spanish, or French.
[^3]: Parsing refers to the process of analyzing a sentence or phrase into its grammatical components, such as nouns, verbs, and adjectives.
[^4]: Software refers to computer programs that perform specific tasks, such as word processing, data analysis, or web browsing.