Making large models output JSON is a must-have for many applications: interfaces need to be parsed, fields need to be stored, and processes need to be automated. The model is naturally generated word by word, and if you accidentally miss a quotation mark or multiple explanations, the JSON will be scrapped on the spot. Constrained Decoding is the technology that was born for this purpose.
1、 The problem lies in 'free generation'
The big model selects the most likely word from the vocabulary at each step, and it does not "know" that the output must conform to a certain grammar. So even if the prompt repeatedly emphasizes "only return JSON", the model may still add a sentence "Okay, here are the results" or include extra text in the field values, causing the parser to crash directly.
2、 Weld the grammar into the sampling process
The idea of constraint decoding is to use a finite state machine or syntax rule at each generation step to determine which words are only allowed in the current step. For candidate words that deviate from the target syntax (such as JSON, SQL, regular), simply reduce their probability to zero and only sample from valid words. In this way, the model is still 'generated', but each step is enclosed by the cage of syntax, and the final result must be legal.
3、 Common forms of implementation
There are several approaches in engineering: firstly, using JSON Schema to describe the target structure, and decoding according to schema constraints; The second is to use context free grammar (CFG) or regular expressions to define acceptable sequences of lexical elements; The third is to use a constrained reasoning framework to directly block illegal word elements at the bottom level. Their commonality is to transform 'format correctness' from a soft constraint in prompt words to a hard guarantee in the decoding stage.
4、 Don't mistake correct format for correct content
Constrained decoding ensures that the format is legitimate, but does not guarantee that the content is correct. The cage of syntax allows the output to be valid JSON, but whether the fields are filled in correctly or the values are calculated accurately still depends on the model's understanding and the quality of prompt words. So it usually needs to be used in conjunction with clear field descriptions and a few examples.
Summary
Constraint decoding transforms the large model from a "high probability of writing correctly" format to a "structurally valid" one, which is a key puzzle for the implementation of structured output. For projects that require stable integration with downstream systems, it is often more worry free than repeatedly polishing prompts.
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