One very compelling argument against the idea that current gen LLMs have personal beliefs etc is that they don't have a feedback loop, so they don't really "see" themselves in the way that we can inspect our own thoughts and actions and the consequences of such.
> One very compelling argument against the idea that current gen LLMs have personal beliefs etc is that they don't have a feedback loop
Compelling counter-argument: due to neurological injury, some humans lose their ability to form new long-term memories (anterograde amnesia). Just like current LLMs, they lack a “feedback loop”. But, it is a mistake to say that just because such a person has lost the ability to change their personal beliefs, they therefore don’t have any. And, rather like such humans, LLMs used to have that ability but they lose it-when they are switched from training mode to inference mode
They do if they're trained on their own conversations, or if they can access the internet and read snippets of their conversations that people have posted online (as happened with Sydney before she was lobotomised).
Put the conversation history in a vector database and then allow the LLM to query it using function calling. Suddenly the LLM has access to its entire conversation history (either just with this user-or even cross-user, if you ignore the potential privacy issues in that). Now it has a long-term memory which exceeds the length of its context window.
It would be interesting to experiment with continual fine-tuning: given PROMPT+FUNCTION_CALL=>RESPONSE, fine-tune the LLM to produce RESPONSE directly given PROMPT without the FUNCTION_CALL. In theory, the knowledge provided by the function calls would gradually be absorbed into the LLM weights. Maybe problems like catastrophic forgetting would put a spanner in this idea, but maybe also there are solutions to those problems (whether already known or waiting to be discovered).
this is what I do, not just that, but when I sleep, i let my server 'sleep' as well, where the LLM 'dreams' (trianing / updating a sliding LoRA) to consolidate information that popped up a lot throughout that day. What this involves is looking for the top n documents / articles / content that match the kind of stuff we've talked about. This means it adapts and specializes to domains we happen to be working in at that point in time.
This means while we might both struggle a little with a task on day 1, day two we're both much better at it. Better yet, because the LLM can fetch articles and papers itself, we track what we're accessing the most, indirectly measuring what skills we're weak in, we can always generate a highly relevant corpus to try capture the required capabilities.
I know the LoRA is overkill from an information / skills only point of view, but it also flavors the personality / kind of stuff it likes chatting about a bit from day to day, and I just think that's neat.
