LLM Code Guard Rails Using Vector Grammar Lookup
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Solution Overview
Problem
Current methods for using large language models (LLMs) in code generation, such as for database schema manipulation, suffer from hallucination, require IT expertise, are costly, and are not scalable for large enterprise settings, leading to unreliable and costly solutions.
Innovation Solution
A vector database table is used to represent context-free language and grammar, guiding LLMs to generate reliable code by ensuring compliance with grammar rules through a look-up table, lists, and a pushdown automaton, reducing memory and search time by using a representative subset of token sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If re-prompting is used to reduce code errors, then the number of code errors is reduced, but LLM inference costs increase and IT-expert intervention is still required
Solution Approach 1:
A vector database serves as an intermediary between the LLM and the code generation process. It stores grammar rules and language structures that guide the LLM during decoding, enabling reliable code generation without requiring repeated re-prompting or expensive fine-tuning iterations.
Solution Approach 2:
Grammar rules and language structures are pre-computed and stored in the vector database before code generation begins. This preliminary preparation allows the LLM to access validated grammar information during decoding, eliminating the need for costly post-generation error correction through re-prompting.
2Reliability
If fine-tuning is applied to reduce code errors, then the number of code errors is reduced, but the model forgets previous knowledge and becomes non-scalable for large code repositories
Solution Approach 1:
The grammar knowledge is segmented into discrete rules and structures stored in the vector database, rather than being embedded in the model weights through fine-tuning. This allows the LLM to access specific grammar information as needed without retraining, maintaining scalability while improving code generation reliability.
3Reliability
If advanced models like GPT-4o are used to achieve low output error rate, then the output error rate is reduced, but costs increase significantly and model functionality is not comprehensible
Solution Approach 1:
The vector database acts as an intermediary that provides grammar validation and guidance to the LLM during decoding. This external knowledge source enables reliable code generation using more affordable models, eliminating the need to deploy expensive advanced models while maintaining low error rates.
4Productivity
If the LLM generates code without grammar guidance, then generation speed is fast, but hallucination occurs and code reliability is compromised
Solution Approach 1:
The vector database provides real-time feedback during the decoding process by guiding the LLM to select tokens that comply with grammar rules. This continuous feedback mechanism ensures code compilability is maintained throughout generation, preventing hallucinations while preserving generation speed.
Data Source
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AI summary
The present disclosure relates to method of generating a vector database table which stores all grammar rules of a given context-free language. The rules are being stored in the form of a pushdown automaton. An input token is fed to the vector database as a query and the vector database, returns any occurrences of that token through the language grammar rules. Any returned grammar rule, being represented by a push down automaton, algorithmically produces all possible next output tokens thus given any input query token, there will a set of output tokens that compile with the language grammar rules and are grammatically correct in the order of input token. The present disclosure also relates to a method of guiding a language model to produce grammatically correct code in context-free programming languages. Thereby next token prediction of a language model in code generation, will be fed into the vector database table, and only grammatically correct candid output tokens will be returned. This set of grammatically correct output tokens guide the language model by tweaking the sampling and decoding modules in the language model. The decoding module is a key part of language model output that defines the probability for the next token to be picked as output. This method increases the probabilities of correct tokens and decreases the probabilities of all other tokens which guarantees that any output token picked by the language model will be grammatically aligned with the input token. The present disclosure also relates to a method of storing grammar rules in an in-memory vector database, compromising memory usage to gain capped lookup time per next token and pruning the search space of correct grammar rules using the in-memory vector database rather than processing all grammar rules per next token. This method caps the guard-railing process time under a constant limit time of a full table scan per next token to prevent linear or exponential increase in the process time of generation. This method compromises to increase memory usage in favor of achieving relatively constant time per token guard-railing to not pay increasing cost of processing by increasing output tokens.