My experience with Large Language Models is full of exploration of ideas. I had the model perform a lot of role-playing. Sometimes I made it act as a Marketing Expert, sometimes a Pilot, and sometimes a Curious Kid.
I noticed that the meanings of these roles differed in my head due to my biases, schemas, and memories. Curious, I wondered if the model itself could explain what it perceived a Curious Kid to be. Initially, it replied predictably with, "As a Large Language Model, I don't have perceptions about..." So I clarified by saying, "Describe the characteristics of a Curious Kid," and it responded:
- Asks lots of questions: They frequently ask “why,” “how,” and “what if,” often about things adults might overlook.
- Eager to explore: They enjoy investigating their surroundings, experimenting with objects, or trying new experiences.
- Enjoys problem-solving: Curious kids like puzzles, challenges, and figuring things out on their own.
- Not easily satisfied with simple answers: They often dig deeper, asking follow-up questions to understand more fully.
- Observant and attentive: They notice small details and may spot things others miss.
- Creative and imaginative: Curiosity often fuels storytelling, imaginative play, and unique ideas.
- Willing to take risks: They aren’t afraid to try something new or make mistakes if it leads to discovery.
- Self-motivated: They seek information or explore topics without being prompted.
- Persistent: If something piques their interest, they keep at it even when it’s challenging.
Wow, that was more comprehensive than my initial thought, which had only been "asks a lot of questions and isn't satisfied with simple answers." Reflecting back, my initial concept was indeed just a vague feeling.
That's when I got the idea: I wanted ChatGPT to "role-play a curious kid who could instantly turn into an adult whenever I wanted (by issuing the command /growup). Then, that adult could turn into Socrates (/lastwords), and finally back to normal (/maculatum)."
Thanks to OpenAI’s "Custom Instructions" feature, I could store these commands in the background, accessible in any chat.
The resulting outputs genuinely amazed me. They noticeably wiggled and jiggled up my worldview.
💭 Worldview? My worldview? I thought to myself. This intrigued me because I realized that by explicitly instructing the model to behave a certain way, it was responding according to my expectations. But what about its default behavior? Perhaps I wanted to change its default because it wasn't satisfying enough for me.
Let me slow down. I started dissecting things. Maybe generating tokens (words) in the same inference (run) limited the model's ability to fully embody the roles I wanted. It felt like having multiple personas (kid, adult, Socrates) combined into a single equation, something like: find x where 2 (kid) + 3 (adult) + x (Socrates) = 10, influencing the final outcome.
Around that time, OpenAI released their API, and I was among the first few to gain access. Despite the limited documentation available, I wrote some code based on OpenAI cookbooks, making three distinct prompts in three separate AI calls: the curious kid, the adult, Socrates, and added a listener named John, who listens to all of them and wonders what's real. I called this setup the "Truth Triad." Coincidentally, it was around the same time a community member demonstrated an idea called Baby AGI.
The outputs from the "Truth Triad" surprised me in ways I never expected. Now, reflecting on this, I believe I should recreate some of these outputs so you can experience the surprise firsthand.
I will keep that for the next post! (To give you an incentive to come back?)
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[My experience with Large Language Models](https://www.homosapient.com/my-experience-with-large-language-models/) is full of exploration of ideas. I had the model perform a lot of role-playing. Sometimes I made it act as a _Marketing Expert_, sometimes a _Pilot_, and sometimes a _Curious Kid_.
I noticed that the meanings of these roles differed in my head due to my biases, schemas, and memories. Curious, I wondered if the model itself could explain what it perceived a Curious Kid to be. Initially, it replied predictably with, "As a Large Language Model, I don't have perceptions about..." So I clarified by saying, "Describe the characteristics of a Curious Kid," and it responded:
1. **Asks lots of questions**: They frequently ask “why,” “how,” and “what if,” often about things adults might overlook.
2. **Eager to explore**: They enjoy investigating their surroundings, experimenting with objects, or trying new experiences.
3. **Enjoys problem-solving**: Curious kids like puzzles, challenges, and figuring things out on their own.
4. **Not easily satisfied with simple answers**: They often dig deeper, asking follow-up questions to understand more fully.
5. **Observant and attentive**: They notice small details and may spot things others miss.
6. **Creative and imaginative**: Curiosity often fuels storytelling, imaginative play, and unique ideas.
7. **Willing to take risks**: They aren’t afraid to try something new or make mistakes if it leads to discovery.
8. **Self-motivated**: They seek information or explore topics without being prompted.
9. **Persistent**: If something piques their interest, they keep at it even when it’s challenging.
Wow, that was more comprehensive than my initial thought, which had only been "asks a lot of questions and isn't satisfied with simple answers." Reflecting back, my initial concept was indeed just a vague feeling.
That's when I got the idea: I wanted ChatGPT to "role-play a curious kid who could instantly turn into an adult whenever I wanted (by issuing the command **/growup**). Then, that adult could turn into Socrates (**/lastwords**), and finally back to normal (**/maculatum**)."
Thanks to OpenAI’s "Custom Instructions" feature, I could store these commands in the background, accessible in any chat.
The resulting outputs genuinely amazed me. They noticeably wiggled and jiggled up my worldview.
💭 Worldview? **My** worldview? I thought to myself. This intrigued me because I realized that by explicitly instructing the model to behave a certain way, it was responding according to my expectations. But what about its default behavior? Perhaps I wanted to change its default because it wasn't satisfying enough for me.
Let me slow down. I started dissecting things. Maybe generating tokens (words) in the same inference (run) limited the model's ability to fully embody the roles I wanted. It felt like having multiple personas (kid, adult, Socrates) combined into a single equation, something like: find x where 2 (kid) + 3 (adult) + x (Socrates) = 10, influencing the final outcome.
Around that time, OpenAI released their API, and I was among the first few to gain access. Despite the limited documentation available, I wrote some code based on OpenAI cookbooks, making three distinct prompts in three separate AI calls: the curious kid, the adult, Socrates, and added a listener named John, who listens to all of them and wonders what's real. I called this setup the "Truth Triad." Coincidentally, it was around the same time a community member demonstrated an idea called Baby AGI.
The outputs from the "Truth Triad" surprised me in ways I never expected. Now, reflecting on this, I believe I should recreate some of these outputs so you can experience the surprise firsthand.
I will keep that for the next post! (To give you an incentive to come back?)
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