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Fun quirk about this game: If there aren't too many cards left and your teammate knows their powers of two, you have a winning strategy. You simply lay a mental bitmap over all remaining cards, setting 1 for cards that belong to your team and 0 for all others. You can then just say the number that is represented by this bitmap, e.g. "five" for 0101, and your teammate can decode it in their head. All numbers are, afte…
Is that really surprising? It’s basically the same brain playing with itself. Seems quite natural to link the code names to the same words. Let different LLMs play.
This is the take I thought I'd have, but in the last example, the guesser model reaches the correct conclusion using a different reasoning than the clue giver model. The clue giver justifies the link of Paper and Log as "written records", and between Paper and Line as "lines of text". But the guesser model connects Paper and Log because "paper is made from logs" (reaching the conclusion through a different meaning of…
Could this just be a case of Reddit being included in the training data? “ I read through codenames official rules to see if using "007" as a clue was allowed, and it turns out it is! To my surprise, I even came across a Reddit post where people were discussing and justifying why this clue fits perfectly within the rules.”
Is that really surprising? It’s basically the same brain playing with itself. Seems quite natural to link the code names to the same words. Let different LLMs play.
This is the take I thought I'd have, but in the last example, the guesser model reaches the correct conclusion using a different reasoning than the clue giver model. The clue giver justifies the link of Paper and Log as "written records", and between Paper and Line as "lines of text". But the guesser model connects Paper and Log because "paper is made from logs" (reaching the conclusion through a different meaning of…
No, it doesn't. It reaches the conclusion because of vector similarity (simplified explanation): these explanations are post-hoc.
Somehow I expected AI to give clues that combine 4-5-6 words at a time. It's not at all impressive to me. And I'm not a serious player at all
In that instance, the clues all matched 2-3 words, and the winning team got lucky twice (they guessed an unclued word using an unintended correlation, and their opponent guessed a different one of their unclued words.)
You also see a number of instances where the agents continue guessing words for a clue even though they've already gotten enough matches. For instance, in round 2, for the clue "Japan (2)", the blue team guesses sumo and cherry, then goes for a rather tenuous followup guess for round 1's 007 with "ring" (despite having gotten the two clued matches in the first round). A sillier example is in the final round, where the Red Team guesses 3 clues (thereby identifying all nine of their target words), then going ahead and guessing another word.
(For what it's worth, I think "shark" would have been a better guess for another 007 tie-in seeing as there are multiple Bond movies with sharks, but it's also not a match, and again, I wouldn't have gone for a third guess here when there were only two clued words.)
Somehow I expected AI to give clues that combine 4-5-6 words at a time. It's not at all impressive to me. And I'm not a serious player at all
I was wondering about the same. It is possible that the instructions didn’t try to make the gameplay as aggressive as possible. A good model could optimize the separator to make it easy to guess the most words possible. By having access to its own state, it should be possible to reach 5–6 words in most cases. There is an argument for keeping words around that would increase the difficulty of the opponents guessing la…
In case you're wondering, the prompts are available here: https://github.com/SuveenE/codenames-ai/blob/main/utils/prom...
Earlier quoted context omitted.
This is the take I thought I'd have, but in the last example, the guesser model reaches the correct conclusion using a different reasoning than the clue giver model. The clue giver justifies the link of Paper and Log as "written records", and between Paper and Line as "lines of text". But the guesser model connects Paper and Log because "paper is made from logs" (reaching the conclusion through a different meaning of…
> But the guesser model connects Paper and Log because "paper is made from logs" (reaching the conclusion through a different meaning of Log) No, it doesn't. It reaches the conclusion because of vector similarity (simplified explanation): these explanations are post-hoc .