Is excitedabout thepotential ofLLMs ineducationHas used agenerative AImodel for anon-academicpurposeHassuccessfullydebugged acomplexLLMHas traveledinternationallyto attend thisconferenceHas used anLLM tosummarizeresearchpapersHascontributedto an open-source AIprojectIs familiarwith theconcept ofpromptengineeringHasexperiencewith fine-tuning a pre-trained LLMIs optimisticabout thefuture ofhuman-AIcollaborationIs interestedin the ethicalimplicationsof generativeAIHaspublishedresearch onmultilingualLLMsHas experiencewith low-resourcelanguages inNLPCan explain thedifferencebetween causaland maskedlanguagemodelsHas used anLLM in alanguageother thanenglishCanrecommenda good AI ortech relatedpodcastHas learneda newlanguage inthe last yearCan namethreedifferent LLMarchitecturesHas collaboratedon a researchpaper withsomeone from adifferent continentHas presenteda paper onnaturallanguagegenerationKnows atleast threeprogramminglanguagesHasparticipated ina hackathonfocused on AIor LLMsHas apreferred AIresearch toolthey canrecommendHasattended anICMLconferencebeforeHas used agenerative AImodel tocreate art ormusicIs excitedabout thepotential ofLLMs ineducationHas used agenerative AImodel for anon-academicpurposeHassuccessfullydebugged acomplexLLMHas traveledinternationallyto attend thisconferenceHas used anLLM tosummarizeresearchpapersHascontributedto an open-source AIprojectIs familiarwith theconcept ofpromptengineeringHasexperiencewith fine-tuning a pre-trained LLMIs optimisticabout thefuture ofhuman-AIcollaborationIs interestedin the ethicalimplicationsof generativeAIHaspublishedresearch onmultilingualLLMsHas experiencewith low-resourcelanguages inNLPCan explain thedifferencebetween causaland maskedlanguagemodelsHas used anLLM in alanguageother thanenglishCanrecommenda good AI ortech relatedpodcastHas learneda newlanguage inthe last yearCan namethreedifferent LLMarchitecturesHas collaboratedon a researchpaper withsomeone from adifferent continentHas presenteda paper onnaturallanguagegenerationKnows atleast threeprogramminglanguagesHasparticipated ina hackathonfocused on AIor LLMsHas apreferred AIresearch toolthey canrecommendHasattended anICMLconferencebeforeHas used agenerative AImodel tocreate art ormusic

Human BINGO: Navigating Generative AI and LLMs Across Languages - Call List

(Print) Use this randomly generated list as your call list when playing the game. There is no need to say the BINGO column name. Place some kind of mark (like an X, a checkmark, a dot, tally mark, etc) on each cell as you announce it, to keep track. You can also cut out each item, place them in a bag and pull words from the bag.


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  1. Is excited about the potential of LLMs in education
  2. Has used a generative AI model for a non-academic purpose
  3. Has successfully debugged a complex LLM
  4. Has traveled internationally to attend this conference
  5. Has used an LLM to summarize research papers
  6. Has contributed to an open-source AI project
  7. Is familiar with the concept of prompt engineering
  8. Has experience with fine-tuning a pre-trained LLM
  9. Is optimistic about the future of human-AI collaboration
  10. Is interested in the ethical implications of generative AI
  11. Has published research on multilingual LLMs
  12. Has experience with low-resource languages in NLP
  13. Can explain the difference between causal and masked language models
  14. Has used an LLM in a language other than english
  15. Can recommend a good AI or tech related podcast
  16. Has learned a new language in the last year
  17. Can name three different LLM architectures
  18. Has collaborated on a research paper with someone from a different continent
  19. Has presented a paper on natural language generation
  20. Knows at least three programming languages
  21. Has participated in a hackathon focused on AI or LLMs
  22. Has a preferred AI research tool they can recommend
  23. Has attended an ICML conference before
  24. Has used a generative AI model to create art or music