Has used agenerative AImodel tocreate art ormusicIs interestedin the ethicalimplicationsof generativeAIHas apreferred AIresearch toolthey canrecommendHas traveledinternationallyto attend thisconferenceHaspublishedresearch onmultilingualLLMsCanrecommenda good AI ortech relatedpodcastIs optimisticabout thefuture ofhuman-AIcollaborationIs excitedabout thepotential ofLLMs ineducationCan namethreedifferent LLMarchitecturesIs currentlyworking on aprojectinvolving cross-lingual transferlearningHas experiencewith low-resourcelanguages inNLPCan explain thedifferencebetween causaland maskedlanguagemodelsHas used anLLM tosummarizeresearchpapersHas presenteda paper onnaturallanguagegenerationHas learneda newlanguage inthe last yearHassuccessfullydebugged acomplexLLMHas used agenerative AImodel for anon-academicpurposeHasattended anICMLconferencebeforeHasexperiencewith fine-tuning a pre-trained LLMIs familiarwith theconcept ofpromptengineeringHasparticipated ina hackathonfocused on AIor LLMsHas collaboratedon a researchpaper withsomeone from adifferent continentKnows atleast threeprogramminglanguagesHascontributedto an open-source AIprojectHas used agenerative AImodel tocreate art ormusicIs interestedin the ethicalimplicationsof generativeAIHas apreferred AIresearch toolthey canrecommendHas traveledinternationallyto attend thisconferenceHaspublishedresearch onmultilingualLLMsCanrecommenda good AI ortech relatedpodcastIs optimisticabout thefuture ofhuman-AIcollaborationIs excitedabout thepotential ofLLMs ineducationCan namethreedifferent LLMarchitecturesIs currentlyworking on aprojectinvolving cross-lingual transferlearningHas experiencewith low-resourcelanguages inNLPCan explain thedifferencebetween causaland maskedlanguagemodelsHas used anLLM tosummarizeresearchpapersHas presenteda paper onnaturallanguagegenerationHas learneda newlanguage inthe last yearHassuccessfullydebugged acomplexLLMHas used agenerative AImodel for anon-academicpurposeHasattended anICMLconferencebeforeHasexperiencewith fine-tuning a pre-trained LLMIs familiarwith theconcept ofpromptengineeringHasparticipated ina hackathonfocused on AIor LLMsHas collaboratedon a researchpaper withsomeone from adifferent continentKnows atleast threeprogramminglanguagesHascontributedto an open-source AIproject

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