5 ms·

I have a simple and brittle system to track people and facts and associations on Newspapers, which is basically: "LLM extract people, places/projects/structure/

I have a simple and brittle system to track people and facts and associations on Newspapers, which is basically: "LLM extract people, places/projects/structure/places and save them as an Obsidian compatible graph network." For 2 or 3 newspapers it works; my idea was to use it as grounding to discover relationships between people, companies and jobs. As for the "everyone's life", I have always assumed that there would be a graph system to point to "forgotten" documents. Gemini said my idea was amazing and new in its implementation, even if not in spirit, but I'm assuming it was being sycophantic as usual.

altmanaltman 5mo ago
I always find it better to ask LLMs why this is bad and to explain itself why it thinks so. Sometimes it might hallicunate stuff but forcing it to find out the negatives is better than asking it for opinion since i am guessing they found early in training that an agreeable LLM is better received than one which is constantly truthful and considers you to be pretty dumb.
- radlad 5mo ago
  > i am guessing they found early in training that an agreeable LLM is better received than one which is constantly truthful and considers you to be pretty dumb My sense is that this is sort of accurate, but more likely it's a result of two things: 1. LLMs are still next-token predictors, and they are trained on texts of humans, which mostly collaborate. Staying on topic is more likely than diverging into a new idea. 2. LLMs are trained via RLHF which involves human feedback. Humans probably do prefer agreeable LLMs, which causes reinforcement at this stage. So yes, kinda. But I'm not sure it's as clear-cut as "the researchers found humans prefer agreeableness and programmed it in."
- user2722 5mo ago
  I usually ask it to give me two batches of answer: 1) normal; 2) not sycophantic.
asielen 5mo ago
Interesting, how do you use that system? I could see it useful for genealogy research.
- user2722 5mo ago
  Two ideas: grounding the LLM between different chats and researching politicians and their friends through mutual associations.

altmanaltman 5mo ago

I always find it better to ask LLMs why this is bad and to explain itself why it thinks so. Sometimes it might hallicunate stuff but forcing it to find out the negatives is better than asking it for opinion since i am guessing they found early in training that an agreeable LLM is better received than one which is constantly truthful and considers you to be pretty dumb.

radlad 5mo ago
> i am guessing they found early in training that an agreeable LLM is better received than one which is constantly truthful and considers you to be pretty dumb My sense is that this is sort of accurate, but more likely it's a result of two things: 1. LLMs are still next-token predictors, and they are trained on texts of humans, which mostly collaborate. Staying on topic is more likely than diverging into a new idea. 2. LLMs are trained via RLHF which involves human feedback. Humans probably do prefer agreeable LLMs, which causes reinforcement at this stage. So yes, kinda. But I'm not sure it's as clear-cut as "the researchers found humans prefer agreeableness and programmed it in."
user2722 5mo ago
I usually ask it to give me two batches of answer: 1) normal; 2) not sycophantic.

asielen 5mo ago

Interesting, how do you use that system? I could see it useful for genealogy research.

user2722 5mo ago
Two ideas: grounding the LLM between different chats and researching politicians and their friends through mutual associations.