Diagnosing sources of noise in an evaluation

The invention describes a computer method for understanding natural language text. It works by first converting text into very long lists of numbers (vectors, each with over 500 values) that represent its meaning. Then, it uses a special computer program called an encoder to shorten these lists of numbers, making them easier to work with, and can display them visually to show semantic content.

Why it matters: The core method of creating and reducing embeddings was established by 2024. However, the rapid proliferation of large language models (LLMs) since then has created an even greater need for tools to diagnose and understand the 'noise' or biases in their evaluations, which this invention's visualization capabilities could address.

Status: Under examinationA pending application. It has not been granted, and it has not been abandoned.
How hard to build: SpecializedNLP models, machine learning, visualization software

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