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Large Language Models Are Getting Bloated. How Do We Fix It?
- Large Language Models (LLMs) are being used widely in society but have some baggage that is starting to affect their performance and our ability to use them.
- The massive amount of training and sheer knowledge required for LLM doesn’t equate to the performance you might expect. LLMs might seem smart while being inaccurate and are biased, verbose without being relevant, wasteful with resources and sometimes just wrong.
- These types of models are worth improving upon and there are indications that LLMs could find a more comfortable middle ground between over-generalized LLMs and agency-specific AI algorithms.
- The domain-specific foundational model has shown a lot of promise for solving the more serious problems that are too general for traditional AI but too in depth for LLMs.
- The Artificial Superintelligence (ASI) Alliance is currently the leader in this field and has developed an interesting twist to their model that transforms the ability to scale up training while maintaining validated answers.
- In ASI
, Web3 is incorporated to create an automated, distributed process that rewards contributors as their contributions are collected and verified. - Early progress shows that use cases like drug discovery or robot simulation based on the domain-specific foundational model could be revolutionary and give reliable information for a wide range of problems without having to train AI models specifically for each problem.
- LLMs will likely fade and give way to much better evolutions in AI, such as the Domain-specific Foundational Model.
- We will continue to see growth, improvements, and a tireless desire for improvement as AI has given us a glimpse of what it can do.
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