Wanted to thank you again. I am currently working on an improved version providing more context. Your sentence "Part of the deep satisfaction in solving ..." made it into the prompt's rule-set. At this very moment I am only using the dataset of r/dota2 to make the testing easier and I look at the very first result with the new prompt:
Generated words and clues:
heroes: Characters with unique abilities in Dota 2, tasked with defeating the enemy's Ancient.
ragers: Players who overly react to in-game frustrations, often ruining the fun for everyone.
rage: A common emotion experienced by players sometimes leading to poor decision-making.
tachyons: Hypothetical particles that travel faster than light, having no place in an Ancient's mechanics.
healing: Essential support function often provided by certain heroes like Treant Protector.
burn: Refers to a mechanism used to deplete an opponent's mana, crucial in trilane strategies.
matters: In Dota 2, every decision, including hero picks, can significantly change the outcome.
fault: What a player will often blame when losing, rather than acknowledging their own mistakes.
support: Role in Dota 2 focused on helping the team, often with abilities to aid and sustain.
team: Group of players working together to win, where synergy and composition are key to victory.
Note that the Words themselves were not picked by OpenAI but rather a per-selection from the BERT Embeddings ML Algorithm but this time with more than just a word as context.
This is definitely going in the right direction. It's only sample size of 1 but i had to share it with you!
Absolutely! Keep me updated - these types of projects are definitely fun to explore. It'll also be interesting to explore alternative LLMs, as well as providing zero shot examples within the system prompt itself (if you haven't already).
I forgot to mention but it might also be worth exploring more classic NLP techniques like named entity recognition to score clues higher and lower in terms of overall specificity.
Have not done zero shot yet. At the moment i am experimenting with this https://arxiv.org/abs/2210.05245. Looks promising hoping to push an updated version soon but i am still fighting with it at the moment
Generated words and clues:
heroes: Characters with unique abilities in Dota 2, tasked with defeating the enemy's Ancient.
ragers: Players who overly react to in-game frustrations, often ruining the fun for everyone.
rage: A common emotion experienced by players sometimes leading to poor decision-making.
tachyons: Hypothetical particles that travel faster than light, having no place in an Ancient's mechanics.
healing: Essential support function often provided by certain heroes like Treant Protector.
burn: Refers to a mechanism used to deplete an opponent's mana, crucial in trilane strategies.
matters: In Dota 2, every decision, including hero picks, can significantly change the outcome.
fault: What a player will often blame when losing, rather than acknowledging their own mistakes.
support: Role in Dota 2 focused on helping the team, often with abilities to aid and sustain.
team: Group of players working together to win, where synergy and composition are key to victory.
Note that the Words themselves were not picked by OpenAI but rather a per-selection from the BERT Embeddings ML Algorithm but this time with more than just a word as context.
This is definitely going in the right direction. It's only sample size of 1 but i had to share it with you!