The LLM Is the New Parser
The article discusses how large language models (LLMs) have become the new
Why it matters
This article highlights the ongoing challenges of working with AI-generated outputs, even as the capabilities of large language models continue to grow. Understanding these limitations is crucial for effectively deploying LLMs in real-world applications.
Key Points
- 1LLMs are now used to process unstructured data like images and documents, similar to how parsers were used to extract meaning from HTML and XML in the past
- 2LLM outputs can be inconsistent, with issues like unbalanced braces, incorrect data types, and unexpected formats
- 3Developers have to write defensive code to handle the unreliable output from LLMs, similar to the patterns used for parsing web data decades ago
- 4The irony is that powerful AI models can't reliably close a curly brace, requiring the same workarounds as legacy parsing code
Details
The article draws a parallel between the parsing challenges of the early 2000s and the current state of large language models (LLMs). In the past, developers had to write complex, defensive code to extract meaningful data from HTML, XML, and other structured formats that were often inconsistent or malformed. Now, the author is facing similar issues when using LLMs to process unstructured data like images and documents. \n\nThe LLM output can include unexpected formatting, incorrect data types, and other issues that require the same kind of defensive coding patterns used for legacy parsing tasks. This includes stripping markdown code blocks, balancing braces, and catching JSON decoding errors. The irony is that these powerful AI models, capable of impressive feats like writing poetry and explaining quantum physics, still struggle with basic data formatting tasks that were common problems decades ago.\n\nThe author concludes that the LLM has become the new
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