Hybrid LLM and Temporal Encoder for Quantitative Prediction
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Solution Overview
Problem
Existing prediction solutions fail to provide quantitative predictions for systems of interest using historical data and relatively rare complex events, which are crucial for making informed decisions in applications like salmon returns, healthcare, and food supply chain management.
Innovation Solution
A computational framework that utilizes large language models to encode knowledge related to the system of interest, correlates temporal data with complex events, and employs a temporal sequential encoder to generate accurate quantitative predictions for future outcomes, leveraging generative networks and reinforcement learning to handle high-dimensional and noisy input spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models are used to provide qualitative responses based on training data, then response accuracy for similar queries is improved, but quantitative prediction capability deteriorates
Solution Approach 1:
The patent combines large language models with temporal sequence models and generative networks to create a hybrid system. The LLM processes qualitative information from complex events while the temporal sequence model handles quantitative time-series data, and the generative network integrates both to produce accurate quantitative predictions that neither model could achieve alone.
Solution Approach 2:
The patent introduces a temporal sequential encoder as an intermediary component that bridges the LLM and the prediction output. This encoder processes the temporal relationships in the data and translates the LLM's qualitative understanding into quantitative predictions, mediating between the two different types of processing.
2Measurement precision
If complex events are incorporated into the prediction model, then prediction accuracy for system changes is improved, but model complexity increases
Solution Approach 1:
The patent segments the complex prediction task into distinct components: a LLM component for processing complex event information, a temporal sequence component for handling time-series data, and a generative network component for integration. This segmentation allows each component to specialize in specific aspects, managing overall complexity while improving prediction accuracy.
Solution Approach 2:
The generative network serves as a universal component that handles multiple functions: it processes output from the LLM, integrates temporal sequence data, and generates final predictions. This multi-functionality reduces the need for separate specialized components for each processing stage.
3Reliability
If iterative training with generative networks is employed, then correlation between temporal data and complex events is improved, but training time and computational resources increase
Solution Approach 1:
The patent performs preliminary encoding of temporal data and complex events into standardized representations before the iterative training process. The temporal sequential encoder pre-processes time-series data into meaningful features, and the LLM pre-processes complex events into structured information, reducing the computational burden during iterative training and accelerating the overall process.
Data Source
AI summary
This document relates to accurate quantitative predictions relating to various systems of interest. One example can obtain temporal data relating to a system from a first source and obtain complex events that can affect the system from a second source. The example can train a model iteratively using generative networks that correlate the temporal data from the first source and the complex events from the second source. The example can employ a temporal sequential encoder to control predictions for future temporal data utilizing the trained model.