Artificial intelligence has advanced tremendously in recent years, perhaps most visibly through the rise of large language models (LLMs) like GPT-3, PaLM, and Anthropic’s Claude. These models boast hundreds of billions of parameters trained on massive text datasets, imbuing them with impressive natural language capabilities.
We’ve seen LLMs chat about casual topics, summarize lengthy articles, translate text between languages, and even generate poems, stories, and computer code upon request. The sheer breadth of their skills is stunning.
However, peer deeper beneath the surface, and you’ll find these language models still falter on many real-world tasks requiring complex reasoning, consistency across documents, or integration of factual knowledge about the world. Their talents manifest mostly on surface-level language tasks based on pattern recognition within their training data.
True intelligence hinges on understanding the messy complexities of reality, not just statistical relationships in a finite dataset. This requires relevant context and background knowledge, the raw materials for reasoning about real-world situations.
LLMs may produce persuasive text given a simple prompt, but probe their knowledge and you hit clear limits. Their world knowledge remains narrow, shallow, and often outright incorrect. Even the most capable LLM stumbles when pressed for deeper reasoning grounded in accurate facts.
To realize the full potential of large language models and edge towards artificial general intelligence, providing the proper contextual knowledge is key. But this entails significant challenges:
- How can we supply diverse, high-quality contextual data at scale?
- What retrieval methods can uncover precisely relevant knowledge?
- How do we apply rigorous reasoning over retrieved information?
In this article, we’ll dive deep on these challenges, exploring different types of contextual knowledge, intelligent retrieval techniques, and reasoning modules to properly integrate retrieved information.
Unlocking the full capabilities of LLMs hinges on augmenting their knowledge and reasoning in the right way. Only with a grounding in reality can they fulfill their destiny — communicating naturally with humans and understanding the boundlessly complex real…