| 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! |
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.