International business machines corporation (20240289558). Large Language Model Evaluation with Enhanced Interpretability by K-Nearest Neighbor Search simplified abstract
Contents
Large Language Model Evaluation with Enhanced Interpretability by K-Nearest Neighbor Search
Organization Name
international business machines corporation
Inventor(s)
Masayasu Muraoka of Tokyo (JP)
Large Language Model Evaluation with Enhanced Interpretability by K-Nearest Neighbor Search - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240289558 titled 'Large Language Model Evaluation with Enhanced Interpretability by K-Nearest Neighbor Search
Simplified Explanation: The patent application discusses techniques for fine-tuning the evaluation of large language models to enhance interpretability by using a debiased output probability distribution and a probability distribution from a k-nearest neighbor search.
Key Features and Innovation:
- Construction of a datastore by applying the language model to a training set.
- Applying the language model to a prompt-applied sentence from a testing set to obtain a feature vector.
- Performing a k-nearest neighbor search of the datastore using the feature vector as a query.
- Interpolating a probability distribution from the k-nearest neighbor search and the output probability distribution of the language model for downstream tasks.
Potential Applications: This technology can be applied in natural language processing tasks, sentiment analysis, chatbots, and machine translation.
Problems Solved: The technology addresses the need for improved interpretability and fine-tuning of large language models for more accurate predictions in downstream tasks.
Benefits:
- Enhanced interpretability of large language models.
- Improved accuracy in downstream tasks.
- Better understanding of model predictions.
Commercial Applications: Potential commercial applications include AI-powered customer service chatbots, language translation services, and sentiment analysis tools for businesses.
Prior Art: Readers can explore prior research in the fields of natural language processing, machine learning, and large language model interpretability.
Frequently Updated Research: Stay updated on advancements in large language model interpretability, k-nearest neighbor algorithms, and downstream task optimization in natural language processing.
Questions about Language Model Fine-Tuning: 1. How does the debiased output probability distribution improve the interpretability of large language models? 2. What are the implications of using a k-nearest neighbor search in fine-tuning language model evaluations?
Original Abstract Submitted
techniques for fine-tuning free evaluation of large language models with enhanced interpretability using a debiased output probability distribution of a large language model and a probability distribution of a k-nearest neighbor search result are provided. in one aspect, a method for performing a downstream task with a language model includes: constructing a datastore by applying the language model to a training set; applying the language model to a prompt-applied sentence from a testing set to obtain a language model feature vector; performing a k-nearest neighbor search of the datastore using the language model feature vector as a query vector; and interpolating a probability distribution of results from the k-nearest neighbor search and an output probability distribution of the language model to obtain a prediction for the downstream task.
