Fixing unreliable agents by adding more unreliable agents. It's a shame we can't do something about the tendency of LLMs to give more weight to their weights than to the data they're analysing.
I guess it all comes back to the fact that they don't have a clear division between data they have learned about the world, instructions and data under analysis.
You can ask them only to consider the 5 facts in front of them, but they won't do that, and cannot do that.
I don't understand this ontology thing. What's the difference between this and a memory system plus skills to collect your enterprise's APIs and CLIs etc?
A memory system is managed by the RL trained behavior, these systems use specific prompts and ordinary software to hardcode a policy. The RL version is usually more efficient, but it can't be improved or made more repeatable.
Fixing unreliable agents by adding more unreliable agents. It's a shame we can't do something about the tendency of LLMs to give more weight to their weights than to the data they're analysing.
I guess it all comes back to the fact that they don't have a clear division between data they have learned about the world, instructions and data under analysis.
You can ask them only to consider the 5 facts in front of them, but they won't do that, and cannot do that.
I don't understand this ontology thing. What's the difference between this and a memory system plus skills to collect your enterprise's APIs and CLIs etc?
A memory system is managed by the RL trained behavior, these systems use specific prompts and ordinary software to hardcode a policy. The RL version is usually more efficient, but it can't be improved or made more repeatable.
Game changer for entreprise (technical) debt