Context is key: A deep dive into Domain-Specific LLMs
Large Language Models (LLMs) like ChatGPT have taken the world by storm, generating human-quality text, translating languages, and even writing different kinds of creative content. But there is always one question, is one language model trained on billions of data excel in everything? Look at some of the failures that include -
- Failure to answer pH of foods
- Unable to identify units of measures
- Wrong citations in law
This is due to lack of specialised knowledge and context dependence in a particular subject matter. This is where Domain-Specific LLMs (DS LLMs) step in, offering a focused approach to unlock the true potential of AI in various fields.
The limitations of General AI leading to the rise of DS LLMs:
While general-purpose LLMs are impressive, they often make blunders when faced with very specific question and complex concepts within a particular domain. This lack of domain expertise leads to several limitations:
- Inaccurate responses: General LLMs trained on massive, diverse datasets may not fully get trained in the depth of a specialized field, leading to misleading or incorrect information.
- Inefficient processing: Processing domain-specific information requires…
