Byte-Level Constrained Decoding for Structured Data Generation
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Solution Overview
Problem
Existing language models struggle to accurately and efficiently generate structured data such as programs and APIs, failing to meet the requirements of fast and efficient interaction with humans.
Innovation Solution
A language model that operates at the byte-level using a context-free grammar (CFG) transformed into a byte-level CFG, with a prediction mechanism to constrain the next token and utilize a trie or minimized finite state automaton (FSA) for efficient parsing and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If existing language models generate text token by token without constraints, then generation speed is maintained, but accuracy of structured data (programs, APIs) deteriorates
Solution Approach 1:
The patent transforms the context-free grammar into a byte-level CFG and constructs a minimized FSA beforehand, before the actual generation process. This preliminary preparation enables the model to efficiently constrain token generation while maintaining high accuracy for structured data, resolving the contradiction between precision and productivity.
Solution Approach 2:
The patent introduces a minimized finite state automaton (FSA) as an intermediary between the language model and the output generation. This intermediary structure efficiently represents the byte-level CFG constraints and guides token selection, enabling both high accuracy in structured data generation and maintained generation speed.
2Manufacturing precision
If byte-level CFG with minimized FSA is used to constrain decoding, then manufacturing precision improves, but device complexity increases
Solution Approach 1:
The grammar transformation and FSA minimization are performed in advance, converting the complex CFG into a streamlined byte-level representation. This preliminary processing reduces the complexity of the parsing mechanism during actual generation while maintaining high decoding accuracy.
Solution Approach 2:
The patent changes the operational level from token-level to byte-level processing, and transforms the grammar representation into a minimized FSA structure. These parameter changes simplify the parsing mechanism while improving decoding accuracy for structured data.
3Manufacturing precision
If context-free grammar is transformed to byte-level CFG, then manufacturing precision improves, but loss of time increases due to transformation process
Solution Approach 1:
The grammar transformation from CFG to byte-level CFG is performed as a preliminary step before generation begins. Although this transformation takes time, it enables significantly faster and more accurate constrained generation during the actual processing, reducing overall time loss.
Solution Approach 2:
The patent creates a byte-level representation (copy) of the original CFG that operates at the byte level rather than token level. This copied structure enables more precise control over structured data generation without requiring repeated transformations during generation.
Data Source
AI summary
A system provides a language model that utilizes an optimized parser and operates at the byte- level in a context free grammar (CFG) and a tokenizer vocabulary. The CFG is constrained and automatically transformed to a byte-level grammar. A prediction mechanism is used to predict the allowed sequences of bytes that can appear after a given prefix. In some instances, only the next token is constrained so that candidates for the next token can be intersected with tokens in the tokenizer vocabulary. To achieve this, lattice parsing is performed using a finite state automaton (FSA) that is generated and then minimized.