Generative AI has stormed on to the scene in 2023. ChatGPT and other models have opened the eyes of many on how far Large Language Models (LLMs) have come. The number of use-cases has been unbounded with many projects springing up around this new ecosystem.
Large language models (LLMs) are shaking up the field with their incredible abilities to generate text, analyze sentiment, translate languages, and much more. With access to massive amounts of data, LLMs have the potential to revolutionize the way we interact with language.
A couple problems have emerged.
Although LLMs are capable of performing various NLP tasks, they are considered generalists and not specialists. In order to train an LLM to become an expert in a particular domain, context management or fine-tuning is usually required. In many organizations, data is siloed across different departments or systems, which can make it difficult to leverage the full potential of Large Language Models (LLMs).
Also, in order to use a hosted service like ChatGPT, you have to share your data. In many cases, this isn’t a huge issue but if you’re working with sensitive or proprietary data it is.
The second issue is that most hosted models are designed to do well with the broadest scope of use cases, which makes sense. If you’re working in a domain with very specific language, like in medical, finance and engineering, being able to customize how a model responds is crucial.
The third issue is generating factual responses that tie back to a trusted data source. Model hallucinations are a term for when LLMs essentially make up confident sounding but wrong responses.
And the last issue is that many open instruction-tuning datasets such as Alpaca and open models such as LLaMA are licensed for non-commercial use.
Sia is a LLM Ops platform that utilizes your proprietary datasets to train ChatGPT-like models, creating intelligent applications capable of sophisticated interactions within your business ecosystem.
