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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Information based explanation methods for deep learning agents-with applications on large open-source chess models.

Patrik Hammersborg¹, Inga Strümke²

¹Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway. patrik.hammersborg@ntnu.no.

Scientific Reports

|August 30, 2024

Summary

This summary is machine-generated.

Researchers re-implemented concept detection for chess AI using open-source models. A novel explainable AI (XAI) method provides guaranteed visual explanations for discrete input domains like chess.

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Area of Science:

Artificial Intelligence
Computer Science
Computational Game Theory

Background:

Large neural network models like AlphaZero achieve state-of-the-art performance in computer chess.
Challenges include explaining the internal knowledge of these models and their lack of open availability.
Existing explainable AI (XAI) methods may not provide exhaustive or exclusive information guarantees.

Purpose of the Study:

To re-implement the concept detection methodology applied to AlphaZero using open-source chess models.
To develop a novel XAI method for explaining AI models in discrete input spaces.
To provide strict guarantees on the information used by AI models during inference.

Main Methods:

Re-implementation of a concept detection methodology on large, open-source chess models with comparable performance to AlphaZero.
Development of a novel XAI method that controls information flow between input and model.
Application and demonstration of the XAI method on standard chess using open-source models.

Main Results:

Achieved results comparable to those obtained when applying the methodology to AlphaZero, using only open-source resources.
The novel XAI method generates visual explanations guaranteed to highlight exhaustively and exclusively the information used by the model.
Demonstrated the viability of the XAI method for explaining AI models in discrete input domains like chess.

Conclusions:

The re-implementation validates the concept detection methodology using accessible, open-source chess AI.
The novel XAI method offers a robust approach to understanding AI decision-making in discrete domains.
This work contributes to more transparent and explainable AI in complex strategic games.