Hallucinations in highly regulated environments? That´s how we deal with it:
In the evolving digital landscape, businesses are integrating Large Language Models (LLMs) into their workflows. However, LLM hallucination - misunderstandings between prompt and training data - poses significant risks. 🚧
Contrary to popular belief, vector databases don't minimize hallucination. One root cause lies in the discrepancy between prompt output and training data, making LLM's output a game of chance. 🎲 Even with the answer, the LLM still relies on its training data, potentially leading to hallucination.
Lowering LLM inference temperature can reduce hallucination but increases plagiarism risk and unnatural speech. Embedding databases update models with new information, but this can increase hallucination due to conflict with learned token sequences in the foundation model. 🔄
The common solution? Calculate the embedding similarity distance between source text and possible summarizations, selecting the closest to the original. Not perfect, though! 🎯
Now, let's talk about pure numerical embeddings technology. 🚀
It accurately processes data and captures semantic relationships between words or phrases, even company-specific jargon. For instance, if a company uses "Blue Sky Thinking" for innovative thinking, numerical embeddings based on ontologies and a knowledge graph can process this effectively. 🌐
Numerical embeddings can be updated with new information, keeping the model current with evolving language use. 🔄 Updated ontologies enhance numerical embeddings, providing an up-to-date "map" for understanding company-specific jargon and very business specific vocabularies. We see that this improves model accuracy and relevance. 🎯
In my view, pure numerical embeddings technology and ontologies offer a powerful solution for dealing with company/business-specific jargon. It provides superior accuracy, efficiency, and adaptability, essential in the fast-paced AI world.
Considering an ontology? Maybe it's time to implement one…
What are your thoughts on the alternatives, like finetuning proprietary embedding models or few-shot strategies to handle embedding hallucinations?
Dominick Romano, Matthias Negri
