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What do you think about structured outputs / JSON mode / constrained decoding / whatever you wish to call it? To me, it's a criminally underused tool. While "r

What do you think about structured outputs / JSON mode / constrained decoding / whatever you wish to call it? To me, it's a criminally underused tool. While "raw" LLMs are cool, they're annoying to use as anything but chatbots, as their output is unpredictable and basically impossible to parse programmatically. Structured outputs solve that problem neatly. In a way, they're "neural networks without the training". They can be used to solve similar problems as traditional neural networks, things like image classification or extracting information from messy text, but all they require is a Zod or Pydantic type definition and a prompt. No renting GPUs, labeling data and tuning hyperparameters necessary. They often also improve LLM performance significantly. Imagine you're trying to extract calories per 100g of product, but some product give you calories per serving and a serving size, calories per pound etc. The naive way to do this is a prompt like "give me calories per 100g", but that forces the LLM to do arithmetic, and LLMs are bad at arithmetic. With structured outputs, you just give it the fifteen different formats that you expect to see as alternatives, and use some simple Python to turn them all into calories per 100g on the backend side.

abdullin 1y ago
Even more than that. With Structured Outputs we essentially control layout of the response, so we can force LLM to go through different parts of the completion in a predefined order. One way teams exploit that - force LLM to go through a predefined task-specific checklist before answering. This custom hard-coded chain of thought boosts the accuracy and makes reasoning more auditable.
solaire_oa 1y ago
I also think that structured outputs are criminally underused, but it isn't perfect... and per your example, it might not even be good, because I've done something similar. I was trying to make a decent cocktail recipe database, and scraped the text of cocktails from about 1400 webpages. Note that this was just the text of the cocktail recipe, and cocktail recipes are comparatively small. I sent the text to an LLM for JSON structuring, and the LLM routinely miscategorized liquor types. It also failed to normalize measurements with explicit instructions and the temperature set to zero. I gave up.
- handfuloflight 1y ago
  Which LLM?
  - deleted 1y ago
    [deleted]
- hellovai 1y ago
  have you tried schema-aligned parsing yet? the idea is that instead of using JSON.parse, we create a custom Type.parse for each type you define. so if you want a: class Job { company: string[] } And the LLM happens to output: { "company": "Amazon" } We can upcast "Amazon" -> ["Amazon"] since you indicated that in your schema. https://www.boundaryml.com/blog/schema-aligned-parsing https://www.boundaryml.com/blog/schema-aligned-parsing and since its only post processing, the technique will work on every model :) for example, on BFCL benchmarks, we got SAP + GPT3.5 to beat out GPT4o ( https://www.boundaryml.com/blog/sota-function-calling https://www.boundaryml.com/blog/sota-function-calling )
  - solaire_oa 1y ago
    Interesting! I was using function calling in OpenAI and JSON mode in Ollama with zod. I may revisit the project with SAP.
  - instig007 1y ago
    so if you want a: class Job { company: string[] } We can upcast "Amazon" -> ["Amazon"] since you indicated that in your schema. Congratulations! You've discovered Applicative Lifting.
    - hellovai 1y ago
      its a bit more nuanced than applicative lifting. parts of of SAP is that, but there's also supporting strings that don't have quotation marks, supporting recursive types, supporting unescaped quotes like: `"hi i wanted to say "hi""`, supporting markdown blocks inside of things that look like "json", etc. but applicative lifting is a big part of it as well! gloochat.notion.site/benefits-of-baml
coderatlarge 1y ago
note the per 100g prompt might lead the llm to reach for the part of its training distribution that is actually written in terms of the 100g standard and just lead to different recall rather than a suboptimal calculation based on non-standardized per 100g training examples.

abdullin 1y ago

Even more than that. With Structured Outputs we essentially control layout of the response, so we can force LLM to go through different parts of the completion in a predefined order. One way teams exploit that - force LLM to go through a predefined task-specific checklist before answering. This custom hard-coded chain of thought boosts the accuracy and makes reasoning more auditable.

solaire_oa 1y ago

I also think that structured outputs are criminally underused, but it isn't perfect... and per your example, it might not even be good, because I've done something similar. I was trying to make a decent cocktail recipe database, and scraped the text of cocktails from about 1400 webpages. Note that this was just the text of the cocktail recipe, and cocktail recipes are comparatively small. I sent the text to an LLM for JSON structuring, and the LLM routinely miscategorized liquor types. It also failed to normalize measurements with explicit instructions and the temperature set to zero. I gave up.

handfuloflight 1y ago
Which LLM?
- deleted 1y ago
  [deleted]
hellovai 1y ago
have you tried schema-aligned parsing yet? the idea is that instead of using JSON.parse, we create a custom Type.parse for each type you define. so if you want a: class Job { company: string[] } And the LLM happens to output: { "company": "Amazon" } We can upcast "Amazon" -> ["Amazon"] since you indicated that in your schema. https://www.boundaryml.com/blog/schema-aligned-parsing https://www.boundaryml.com/blog/schema-aligned-parsing and since its only post processing, the technique will work on every model :) for example, on BFCL benchmarks, we got SAP + GPT3.5 to beat out GPT4o ( https://www.boundaryml.com/blog/sota-function-calling https://www.boundaryml.com/blog/sota-function-calling )
- solaire_oa 1y ago
  Interesting! I was using function calling in OpenAI and JSON mode in Ollama with zod. I may revisit the project with SAP.
- instig007 1y ago
  so if you want a: class Job { company: string[] } We can upcast "Amazon" -> ["Amazon"] since you indicated that in your schema. Congratulations! You've discovered Applicative Lifting.
  - hellovai 1y ago
    its a bit more nuanced than applicative lifting. parts of of SAP is that, but there's also supporting strings that don't have quotation marks, supporting recursive types, supporting unescaped quotes like: `"hi i wanted to say "hi""`, supporting markdown blocks inside of things that look like "json", etc. but applicative lifting is a big part of it as well! gloochat.notion.site/benefits-of-baml

coderatlarge 1y ago

note the per 100g prompt might lead the llm to reach for the part of its training distribution that is actually written in terms of the 100g standard and just lead to different recall rather than a suboptimal calculation based on non-standardized per 100g training examples.