Causal Graph Generation With LLM Feedback and Statistical Validation
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Solution Overview
Problem
The generation of causal graphs with a large number of variables becomes computationally explosive, exceeding the capabilities of modern computers due to the vast number of possible arrangements and the time required to validate each graph, making it impractical to generate valid causal graphs within a reasonable timeframe.
Innovation Solution
A method and system utilizing a language model to iteratively generate and refine a causal graph by comparing it against a controlled set of randomly generated graphs, incorporating empirical validation and statistical feedback to modify prompts until a graph outperforms a predetermined threshold, thereby reducing the computational burden.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a language model generates a causal graph from a large number of variables, then the graph can represent complex relationships, but the number of possible arrangements becomes computationally explosive
Solution Approach 1:
The patent segments the causal graph generation problem into multiple independent steps: (1) generating candidate graphs using a language model, (2) validating candidates against statistical tests, (3) iteratively refining based on validation results. This segmentation avoids the need to evaluate all possible graph arrangements simultaneously, making the problem computationally tractable while handling large numbers of variables
Solution Approach 2:
The patent performs preliminary filtering by executing statistical tests on candidate causal graphs before final validation. This preliminary action eliminates invalid or low-quality graphs early in the process, preventing computationally expensive validation of poor candidates and reducing overall computational complexity
2Reliability
If all possible causal graph arrangements are validated to ensure correctness, then the generated graph is reliable, but the time required exceeds practical limits
Solution Approach 1:
Instead of validating all possible graph arrangements, the patent applies partial validation by testing a selected subset of candidate graphs generated by the language model. The system executes statistical tests on these candidates and iteratively refines the generation process based on results, achieving sufficient reliability without the exhaustive validation that would be time-prohibitive
Solution Approach 2:
The patent implements feedback loops where validation results from statistical tests are used to generate modified prompts for the language model. This feedback mechanism guides the model to generate improved candidates in subsequent iterations, increasing reliability over time while avoiding exhaustive validation of every possible graph
Data Source
AI summary
A method of enhancing a computational efficiency of a computer. The method includes iterating, until convergence, a number of steps including receiving a prompt, executing a language model on the prompt to output a directed graph data structure, executing a statistical test on the directed graph data structure to identify an error, identifying that the directed graph data structure fails to outperform a threshold number of predetermined additional directed graph data structures generated from the variables, generating, based on the error, a natural language constraint, adding the natural language constraint to the prompt, executing the language model on the modified prompt to output an updated directed graph data structure. A final graph data structure is returned when the updated directed graph data structure outperforms the threshold number of the number of predetermined additional directed graph data structures.