Tabular Search LLM Editing Without Full Retraining
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Solution Overview
Problem
Existing tabular search large language models require full retraining when new information is introduced, which is cumbersome and can lead to catastrophic forgetting or hallucinations, impacting operational performance.
Innovation Solution
Implement data pre-processing, first and second fine-tuning operations, and optimization of gradient descent parameters using transductive learning and contextual bandits to update the model without full retraining, minimizing downtime and reducing the risk of forgetting or hallucinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If full retraining is performed to incorporate new information, then the model can handle new data, but the process is cumbersome and leads to catastrophic forgetting or hallucinations
Solution Approach 1:
The patent segments the training process into multiple fine-tuning operations rather than performing a single full retraining. The model is updated through incremental fine-tuning on new data while preserving previously learned knowledge, thus avoiding catastrophic forgetting while still adapting to new information.
Solution Approach 2:
The patent applies preliminary actions by performing data pre-processing and generating training data before fine-tuning. This includes creating synthetic training examples and preparing data in advance, which allows the model to be systematically updated without full retraining, maintaining reliability while improving adaptability.
2Adaptability or versatility
If full retraining is performed to incorporate new information, then the model can handle new data, but the process is time-consuming and reduces operational efficiency
Solution Approach 1:
The patent applies partial action by performing selective fine-tuning on specific components or parameters of the model rather than complete retraining. This allows the model to incorporate new information efficiently with minimal downtime, reducing the loss of time while maintaining adaptability.
Solution Approach 2:
The training process is segmented into multiple smaller fine-tuning operations that can be performed incrementally. This reduces the overall time required compared to full retraining, as each fine-tuning operation processes a subset of the data or focuses on specific model aspects.
3Reliability
If multiple fine-tuning operations are applied to update the model, then the model maintains performance on existing data, but the process complexity increases
Solution Approach 1:
The patent uses feedback mechanisms to monitor model performance across multiple fine-tuning operations. By evaluating the model at each stage and adjusting subsequent fine-tuning based on performance feedback, the system maintains consistency on existing data while managing the complexity of the multi-stage process.
Solution Approach 2:
The patent manages complexity by systematically changing parameters during fine-tuning operations, such as learning rates, batch sizes, or data sampling strategies. These controlled parameter changes allow multiple fine-tuning operations to be performed while maintaining performance consistency and managing process complexity.
Data Source
AI summary
A method for updating a tabular search large language model (LLM) includes performing data pre-processing on new data associated with the tabular dataset to obtain a set of sequences, applying a first fine-tuning operation on the tabular search LLM using the set of sequences, applying a second fine-tuning operation on the tabular search LLM using training data to obtain a set of final loss results and a set of updatable gradients, wherein the training data comprises at least the set of sentence predictions, applying an optimization function on the set of final loss results and the set of updatable gradients to obtain optimized gradient descent parameters, and applying the updated tabular search LLM to a new input associated with the new data to produce a new output.