The two of you are using the word “generalist” differently. You don’t need your tool-using language model to be able to wax poetic about ancient egyptian burial practices. That’s why ChatGPT will become useless. It’s too large and expensive to continue running without subsidies, and it’s too useless for serious tasks. You can get away with a small local model that knows nothing about ancient egypt if all you need is to translate natural language into tool calls.
You’re absolutely right about that, and if we’re able to build a model that’s as capable as GPT and friends at parsing natural language, without simultaneously training it on everything from poetry to programming, that’s a major win. My current understanding of the field is that in order to build/train the models that are able to robustly parse natural language and “understand” the intent behind a series of instructions well enough to translate them to the correct tool calls, we need a very large and varied training set. I’m using “generalist” as a term to refer to the models that you can interact with in natural language across a wide variety of tasks. Those models are extremely powerful if you can also connect them to tools that solve problems deterministically, so that you get around the problem that they don’t really “understand” anything at all, while taking advantage of the fact that they’re extremely well suited for translating natural language to a selected set of pre-defined actions.
I think a major challenge going forward is that interpreting natural language requires a large set of training data. So training specialised models that can also interact with natural language is by nature difficult.
The two of you are using the word “generalist” differently. You don’t need your tool-using language model to be able to wax poetic about ancient egyptian burial practices. That’s why ChatGPT will become useless. It’s too large and expensive to continue running without subsidies, and it’s too useless for serious tasks. You can get away with a small local model that knows nothing about ancient egypt if all you need is to translate natural language into tool calls.
You’re absolutely right about that, and if we’re able to build a model that’s as capable as GPT and friends at parsing natural language, without simultaneously training it on everything from poetry to programming, that’s a major win. My current understanding of the field is that in order to build/train the models that are able to robustly parse natural language and “understand” the intent behind a series of instructions well enough to translate them to the correct tool calls, we need a very large and varied training set. I’m using “generalist” as a term to refer to the models that you can interact with in natural language across a wide variety of tasks. Those models are extremely powerful if you can also connect them to tools that solve problems deterministically, so that you get around the problem that they don’t really “understand” anything at all, while taking advantage of the fact that they’re extremely well suited for translating natural language to a selected set of pre-defined actions.
I think a major challenge going forward is that interpreting natural language requires a large set of training data. So training specialised models that can also interact with natural language is by nature difficult.