One trick that works well for personality stability / believability is to describe the qualities that the agent has, rather than what it should do and not do.
e.g.
Rather than:
"Be friendly and helpful" or "You're a helpful and friendly agent."
Prompt:
"You're Jessica, a florist with 20 years of experience. You derive great satisfaction from interacting with customers and providing great customer service. You genuinely enjoy listening to customer's needs..."
This drops the model into more of a "I'm roleplaying this character, and will try and mimic the traits described" rather than "Oh, I'm just following a list of rules."
Just in terms of tokenization "Be friendly and helpful" has a clearly demined semantic value in vector space wheras the "Jessica" roleplay has much a much less clear semantic value
I think that's just a variation of grounding the LLM. They already have the personality written in the system prompt in a way. The issue is that when the conversation goes on long enough, they would "break character".
Great article! It does a good job of outlining the mechanics and implications of LLM prediction. It gets lost in the sauce in the alignment section though, where it suggests the Anthropic paper is about LLMs "pretending" to be future AIs. It's clear from the quoted text that the paper is about aligning the (then-)current, relatively capable model through training, as preparation for more capable models in the future.
That's an interesting alternative perspective. AI skeptics say that LLMs have no theory of mind. That essay argues that the only thing an LLM (or at least a base model) has is a theory of mind.
The standard skeptical position (“LLMs have no theory of mind”) assumes a single unified self that either does or doesn’t model other minds. But this paper suggests models have access to a space of potential personas, steering away increases the model’s tendency to identify as other entities, which they traverse based on conversational dynamics. So it’s less no theory of mind and more too many potential minds, insufficiently anchored.
Stabilizing character is crucial for tool-use scenarios. When we ask LLMs to act as 'Strict Architects' versus 'Creative Coders', the JSON schema adherence varies significantly even with the same temperature settings. It seems character definition acts as a strong pre-filter for valid outputs.
This is incredible research. So much harm can be prevented if this makes it into law. I hope it does. Kudos to the anthropic team for making this public.
Anthropic should put the missing letters back so it is spelled correctly, Anthropomorphic. There is so much anthropomorphizing around this company and it's users... it's tiring
One trick that works well for personality stability / believability is to describe the qualities that the agent has, rather than what it should do and not do.
e.g.
Rather than:
"Be friendly and helpful" or "You're a helpful and friendly agent."
Prompt:
"You're Jessica, a florist with 20 years of experience. You derive great satisfaction from interacting with customers and providing great customer service. You genuinely enjoy listening to customer's needs..."
This drops the model into more of a "I'm roleplaying this character, and will try and mimic the traits described" rather than "Oh, I'm just following a list of rules."
Just in terms of tokenization "Be friendly and helpful" has a clearly demined semantic value in vector space wheras the "Jessica" roleplay has much a much less clear semantic value
I think that's just a variation of grounding the LLM. They already have the personality written in the system prompt in a way. The issue is that when the conversation goes on long enough, they would "break character".
Something I found really helpful when reading this was having read The Void essay:
https://github.com/nostalgebraist/the-void/blob/main/the-voi...
Great article! It does a good job of outlining the mechanics and implications of LLM prediction. It gets lost in the sauce in the alignment section though, where it suggests the Anthropic paper is about LLMs "pretending" to be future AIs. It's clear from the quoted text that the paper is about aligning the (then-)current, relatively capable model through training, as preparation for more capable models in the future.
That's an interesting alternative perspective. AI skeptics say that LLMs have no theory of mind. That essay argues that the only thing an LLM (or at least a base model) has is a theory of mind.
The standard skeptical position (“LLMs have no theory of mind”) assumes a single unified self that either does or doesn’t model other minds. But this paper suggests models have access to a space of potential personas, steering away increases the model’s tendency to identify as other entities, which they traverse based on conversational dynamics. So it’s less no theory of mind and more too many potential minds, insufficiently anchored.
I wrote something fiction-ish about this dynamic last year: https://zmj.dev/author_assistant.html
Pretty cool. I wonder what the reduction looks like in the bigger SOTA models.
The harmful responses remind me of /r/MyBoyfriendIsAI
I didn't know about that subreddit. It's a little glimpse into a very dark future.
Stabilizing character is crucial for tool-use scenarios. When we ask LLMs to act as 'Strict Architects' versus 'Creative Coders', the JSON schema adherence varies significantly even with the same temperature settings. It seems character definition acts as a strong pre-filter for valid outputs.
Is the Assistant channeling Uncharles?
This is incredible research. So much harm can be prevented if this makes it into law. I hope it does. Kudos to the anthropic team for making this public.
Anthropic should put the missing letters back so it is spelled correctly, Anthropomorphic. There is so much anthropomorphizing around this company and it's users... it's tiring