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Right but human beings dont have infinite wells of knowledge. They have to be provided new information to make new decisions. Google searches is RAG for humans.

Right but human beings dont have infinite wells of knowledge. They have to be provided new information to make new decisions. Google searches is RAG for humans. Sure, the model is unable to request the information, but thats the next step.

phillipcarter 3y ago
> Sure, the model is unable to request the information, but thats the next step. How does it decide which information to request? How does it know which is the right or wrong information for a given request? Maybe it's possible to wave this away with "something in the future will do that!" but that's literally the entire reason why all current AI is limited. It doesn't have the business context or reasoning capabilities to make the right decisions to decide what the right data to augment generation even is. I don't believe we're any closer to AGI.
- jamesbriggs 3y ago
  Not weighing on whether it is AGI-like or not, but my view on the search component is that it's like when we google info, we have a search term and we don't know whether that will return good results or not. So I suppose in order for RAG to become better the LLM needs to be able to review the information given, and decide whether to search more (which they can do with agents to be fair), and we also need to be building retrieval systems that do very well at returning the right information even with sub-optimal search queries. I think LLMs are fairly good at deciding what information is relevant to a query or not though, it is rare that I have found an LLM to be distracted by RAG results (even when they're not relevant)
janalsncm 3y ago
The problem is that facts are not differentiable. If I have a statement “The Declaration of Independence was signed in 1776” and the model replaces 1776 with 1775 that’s incorrect but in some sense just as incorrect as 1876 or 1976. So the loss at all but the correct output should be 1.0, making it pretty hard to learn. It’s like finding a hole in an infinite flat plane with trillions of dimensions.

phillipcarter 3y ago

> Sure, the model is unable to request the information, but thats the next step. How does it decide which information to request? How does it know which is the right or wrong information for a given request? Maybe it's possible to wave this away with "something in the future will do that!" but that's literally the entire reason why all current AI is limited. It doesn't have the business context or reasoning capabilities to make the right decisions to decide what the right data to augment generation even is. I don't believe we're any closer to AGI.

jamesbriggs 3y ago
Not weighing on whether it is AGI-like or not, but my view on the search component is that it's like when we google info, we have a search term and we don't know whether that will return good results or not. So I suppose in order for RAG to become better the LLM needs to be able to review the information given, and decide whether to search more (which they can do with agents to be fair), and we also need to be building retrieval systems that do very well at returning the right information even with sub-optimal search queries. I think LLMs are fairly good at deciding what information is relevant to a query or not though, it is rare that I have found an LLM to be distracted by RAG results (even when they're not relevant)

janalsncm 3y ago

The problem is that facts are not differentiable. If I have a statement “The Declaration of Independence was signed in 1776” and the model replaces 1776 with 1775 that’s incorrect but in some sense just as incorrect as 1876 or 1976. So the loss at all but the correct output should be 1.0, making it pretty hard to learn. It’s like finding a hole in an infinite flat plane with trillions of dimensions.