Learning Data Evaluation Indices for LLM Alignment Selection
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Solution Overview
Problem
The difficulty in selecting appropriate learning data for aligning machine learning models, particularly Large Language Models (LLMs), hinders the ability to achieve outputs preferable for humans.
Innovation Solution
An information processing apparatus and method that includes an acquisition unit for gathering datasets with input and output data along with evaluation values, a generation unit for creating distributions of these evaluations, and a calculation unit for determining indices like diversity, universality, and abnormality based on these distributions, facilitating the selection of suitable learning data for model alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained using enormous learning data, then the model's basic learning capability is improved, but the difficulty in selecting appropriate data for alignment increases
Solution Approach 1:
The patent segments the learning data into multiple datasets with different characteristics (e.g., educational data, general knowledge data, specialized data). It then evaluates each dataset separately using index calculations to determine suitability for alignment, making the complex task of selecting from enormous data manageable through systematic categorization and evaluation.
Solution Approach 2:
The patent introduces an intermediary evaluation system that includes calculation units for computing indices (diversity index, universality index, abnormality index) and selection units that mediate between the raw datasets and the alignment process. This intermediary layer simplifies the selection complexity by providing quantitative metrics and automated decision support.
2Adaptability or versatility
If datasets with high diversity are used for alignment, then the model's adaptability is improved, but the selection difficulty increases due to lack of clear criteria
Solution Approach 1:
The patent transforms the qualitative assessment of dataset diversity into quantitative parameters through index calculations. By computing diversity indices, universality indices, and abnormality indices as measurable values, the system enables easy comparison and selection of datasets based on clear numerical criteria rather than subjective judgment.
Solution Approach 2:
The patent replaces manual, mechanical data selection processes with automated calculation and evaluation systems. The calculation units automatically compute indices based on dataset characteristics, and selection units automatically identify suitable datasets, substituting human manual selection with systematic computational methods.
Data Source
AI summary
An information processing apparatus according to the present disclosure includes an acquisition unit for acquiring a set of datasets including input data to be input to a machine learning model, output data to be output from the machine learning model according to the input data, and an evaluation value indicating evaluation of the output data for the input data, a generation unit for generating a distribution of the evaluation value in the set of the datasets, and a calculation unit for calculating a value of a preset index in the dataset, based on the distribution.