Is optimisticabout thefuture ofhuman-AIcollaborationHas used anLLM in alanguageother thanenglishHas used anLLM tosummarizeresearchpapersHas used agenerative AImodel for anon-academicpurposeHas presenteda paper onnaturallanguagegenerationIs excitedabout thepotential ofLLMs ineducationCan explain thedifferencebetween causaland maskedlanguagemodelsHaspublishedresearch onmultilingualLLMsHascontributedto an open-source AIprojectHasexperiencewith fine-tuning a pre-trained LLMKnows atleast threeprogramminglanguagesHas traveledinternationallyto attend thisconferenceHas apreferred AIresearch toolthey canrecommendHas learneda newlanguage inthe last yearCan namethreedifferent LLMarchitecturesHassuccessfullydebugged acomplexLLMIs interestedin the ethicalimplicationsof generativeAICanrecommenda good AI ortech relatedpodcastHas collaboratedon a researchpaper withsomeone from adifferent continentHasattended anICMLconferencebeforeHas experiencewith low-resourcelanguages inNLPHasparticipated ina hackathonfocused on AIor LLMsIs familiarwith theconcept ofpromptengineeringHas used agenerative AImodel tocreate art ormusicIs optimisticabout thefuture ofhuman-AIcollaborationHas used anLLM in alanguageother thanenglishHas used anLLM tosummarizeresearchpapersHas used agenerative AImodel for anon-academicpurposeHas presenteda paper onnaturallanguagegenerationIs excitedabout thepotential ofLLMs ineducationCan explain thedifferencebetween causaland maskedlanguagemodelsHaspublishedresearch onmultilingualLLMsHascontributedto an open-source AIprojectHasexperiencewith fine-tuning a pre-trained LLMKnows atleast threeprogramminglanguagesHas traveledinternationallyto attend thisconferenceHas apreferred AIresearch toolthey canrecommendHas learneda newlanguage inthe last yearCan namethreedifferent LLMarchitecturesHassuccessfullydebugged acomplexLLMIs interestedin the ethicalimplicationsof generativeAICanrecommenda good AI ortech relatedpodcastHas collaboratedon a researchpaper withsomeone from adifferent continentHasattended anICMLconferencebeforeHas experiencewith low-resourcelanguages inNLPHasparticipated ina hackathonfocused on AIor LLMsIs familiarwith theconcept ofpromptengineeringHas 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 optimistic about the future of human-AI collaboration
  2. Has used an LLM in a language other than english
  3. Has used an LLM to summarize research papers
  4. Has used a generative AI model for a non-academic purpose
  5. Has presented a paper on natural language generation
  6. Is excited about the potential of LLMs in education
  7. Can explain the difference between causal and masked language models
  8. Has published research on multilingual LLMs
  9. Has contributed to an open-source AI project
  10. Has experience with fine-tuning a pre-trained LLM
  11. Knows at least three programming languages
  12. Has traveled internationally to attend this conference
  13. Has a preferred AI research tool they can recommend
  14. Has learned a new language in the last year
  15. Can name three different LLM architectures
  16. Has successfully debugged a complex LLM
  17. Is interested in the ethical implications of generative AI
  18. Can recommend a good AI or tech related podcast
  19. Has collaborated on a research paper with someone from a different continent
  20. Has attended an ICML conference before
  21. Has experience with low-resource languages in NLP
  22. Has participated in a hackathon focused on AI or LLMs
  23. Is familiar with the concept of prompt engineering
  24. Has used a generative AI model to create art or music