AI Innovations and Insights 23: KAG, AlphaMath, and Offloading

Author(s): Florian June

Originally published on Towards AI.

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This article is the 23rd in this compelling series. Today, we will explore three intriguing topics in AI, which are:

KAG: Brilliant Detective Who Masters Evidence and Connects the DotsAlphaMath: The Brilliance of AlphaGo’s Insights in LLM ReasoningLLM Inference: Offloading

Exquisite Video Imagery:

Open-source code: https://github.com/OpenSPG/KAG

KAG is like a smart detective who can both analyze evidence (knowledge graphs) and integrate clues (text retrieval).

When faced with a complex case, he follows multiple leads in his reasoning, eliminates irrelevant information, and ultimately pieces together the complete truth. He can quickly navigate through professional domains like healthcare, solving challenges and providing convincing conclusions.

Existing RAG systems face three major challenges: they rely too heavily on text or vector similarity for retrieval, lack sufficient logical reasoning or numerical capabilities, and require extensive professional knowledge.

KAG improves the effectiveness of LLMs in professional fields by combining the reasoning power of knowledge graphs with the generative capabilities of LLMs.

Figure 1: The KAG Framework. The left side shows KAG-Builder, while the right side displays KAG-Solver. The gray area at the bottom of the image represents KAG-Model. [Source].

As shown in Figure 1, the KAG framework consists of three components:

KAG-Builder focuses on building… Read the full blog for free on Medium.

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