Entropy-Based LLM Alignment Data Selection for Lower Training Cost
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Solution Overview
Problem
Existing alignment training for large language models (LLMs) is resource-intensive and inefficient, with high computational costs and the need for large datasets, while current subset selection methods for reducing dataset size are inconsistent in quality.
Innovation Solution
A method to generate alignment training datasets by calculating entropy changes for each data element and selecting a subset based on these values, reducing dataset size while maintaining effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If alignment training is performed using a large alignment training dataset, then the LLM achieves better alignment performance with higher ethical, factual, and helpful properties, but the computational costs, time, memory usage, and energy consumption become prohibitive
Solution Approach 1:
The patent extracts and removes redundant or low-value data elements from the alignment training dataset. By calculating entropy changes for each data element and selecting only those with significant entropy change values, the method extracts the essential informative content while discarding unnecessary elements, thereby reducing dataset size and computational cost while preserving alignment performance
Solution Approach 2:
The patent changes the parameter of dataset size by selecting a subset of data elements based on entropy change thresholds. This parameter transformation allows the training process to use a smaller, more efficient dataset that maintains the critical information needed for effective alignment training, thus reducing computational resources while preserving performance
2Productivity
If the alignment training dataset size is reduced to lower computational costs, then resource efficiency improves, but the quality and effectiveness of the selected subset becomes inconsistent
Solution Approach 1:
The patent introduces entropy change value as a new selection parameter to evaluate and filter data elements. By calculating the entropy change for each data element and selecting those above a certain threshold, the method transforms the subset selection process from random or simple sampling to a principled approach based on information content, ensuring consistent quality in the reduced dataset
Solution Approach 2:
The patent employs a feedback mechanism where entropy change values are calculated for each data element, and this information is used to guide the selection process. The entropy change metric provides feedback on the informational value of each element, allowing the system to iteratively select the most valuable subset, thereby maintaining high quality while reducing dataset size
3Reliability
If human feedback is gathered in the form of preferences for alignment training, then the LLM can be trained to generate responses with higher ethical, factual, and helpful properties, but the process becomes tedious and expensive
Solution Approach 1:
The patent extracts the essential signal from human feedback by focusing on preference data that demonstrates actual alignment improvements. By selecting only the data elements with significant entropy change values from the human feedback dataset, the method extracts the most valuable training signals while discarding redundant or less informative feedback, thereby reducing data collection and processing effort while maintaining response quality
Data Source
AI summary
Techniques for data efficient alignment of large language models retrieving a dataset comprising a plurality of alignment data elements; calculating an entropy for the dataset; for each alignment data element, calculate a respective entropy change value based on a difference between the entropy of the dataset and an entropy specific to the alignment data element; and generating an alignment training dataset comprising a subset of the plurality of alignment data elements that are identified based on the respective entropy change values.