Canrecommenda good AI ortech relatedpodcastCan namethreedifferent LLMarchitecturesHas collaboratedon a researchpaper withsomeone from adifferent continentHas learneda newlanguage inthe last yearHasexperiencewith fine-tuning a pre-trained LLMHaspublishedresearch onmultilingualLLMsIs excitedabout thepotential ofLLMs ineducationIs familiarwith theconcept ofpromptengineeringKnows atleast threeprogramminglanguagesHasparticipated ina hackathonfocused on AIor LLMsHas presenteda paper onnaturallanguagegenerationHas used agenerative AImodel for anon-academicpurposeHas traveledinternationallyto attend thisconferenceHas used anLLM in alanguageother thanenglishHas used agenerative AImodel tocreate art ormusicHascontributedto an open-source AIprojectIs interestedin the ethicalimplicationsof generativeAIHassuccessfullydebugged acomplexLLMHasattended anICMLconferencebeforeHas apreferred AIresearch toolthey canrecommendCan explain thedifferencebetween causaland maskedlanguagemodelsHas experiencewith low-resourcelanguages inNLPHas used anLLM tosummarizeresearchpapersIs optimisticabout thefuture ofhuman-AIcollaborationCanrecommenda good AI ortech relatedpodcastCan namethreedifferent LLMarchitecturesHas collaboratedon a researchpaper withsomeone from adifferent continentHas learneda newlanguage inthe last yearHasexperiencewith fine-tuning a pre-trained LLMHaspublishedresearch onmultilingualLLMsIs excitedabout thepotential ofLLMs ineducationIs familiarwith theconcept ofpromptengineeringKnows atleast threeprogramminglanguagesHasparticipated ina hackathonfocused on AIor LLMsHas presenteda paper onnaturallanguagegenerationHas used agenerative AImodel for anon-academicpurposeHas traveledinternationallyto attend thisconferenceHas used anLLM in alanguageother thanenglishHas used agenerative AImodel tocreate art ormusicHascontributedto an open-source AIprojectIs interestedin the ethicalimplicationsof generativeAIHassuccessfullydebugged acomplexLLMHasattended anICMLconferencebeforeHas apreferred AIresearch toolthey canrecommendCan explain thedifferencebetween causaland maskedlanguagemodelsHas experiencewith low-resourcelanguages inNLPHas used anLLM tosummarizeresearchpapersIs optimisticabout thefuture ofhuman-AIcollaboration

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