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Listen "#36 - KAG Framework - Builder, Solver & Model"
Episode Synopsis
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This podcast describes the Knowledge Augmented Generation (KAG) Framework.
There are 3 main components: KAG-Builder (for offline indexing), KAG-Solver (for hybrid reasoning), and KAG-Model (for optimisation).
The framework leverages Natural Language Understanding (NLU), Natural Language Inference (NLI), and Natural Language Generation (NLG) – core NLP processes – to enable the system to understand, reason with, and generate human-like text. NLU interprets input, NLI establishes logical connections, and NLG produces coherent outputs.
In essence, KAG integrates knowledge construction, reasoning, and model optimisation for advanced text processing.
This podcast describes the Knowledge Augmented Generation (KAG) Framework.
There are 3 main components: KAG-Builder (for offline indexing), KAG-Solver (for hybrid reasoning), and KAG-Model (for optimisation).
The framework leverages Natural Language Understanding (NLU), Natural Language Inference (NLI), and Natural Language Generation (NLG) – core NLP processes – to enable the system to understand, reason with, and generate human-like text. NLU interprets input, NLI establishes logical connections, and NLG produces coherent outputs.
In essence, KAG integrates knowledge construction, reasoning, and model optimisation for advanced text processing.
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