Has used agenerative AImodel for anon-academicpurposeHas learneda newlanguage inthe last yearIs excitedabout thepotential ofLLMs ineducationCan explain thedifferencebetween causaland maskedlanguagemodelsIs familiarwith theconcept ofpromptengineeringHaspublishedresearch onmultilingualLLMsCan namethreedifferent LLMarchitecturesHas experiencewith low-resourcelanguages inNLPIs optimisticabout thefuture ofhuman-AIcollaborationHas collaboratedon a researchpaper withsomeone from adifferent continentHassuccessfullydebugged acomplexLLMHasparticipated ina hackathonfocused on AIor LLMsHas traveledinternationallyto attend thisconferenceIs interestedin the ethicalimplicationsof generativeAIHas used anLLM in alanguageother thanenglishHas used anLLM tosummarizeresearchpapersHas presenteda paper onnaturallanguagegenerationHas used agenerative AImodel tocreate art ormusicCanrecommenda good AI ortech relatedpodcastHasexperiencewith fine-tuning a pre-trained LLMHas apreferred AIresearch toolthey canrecommendHascontributedto an open-source AIprojectHasattended anICMLconferencebeforeKnows atleast threeprogramminglanguagesHas used agenerative AImodel for anon-academicpurposeHas learneda newlanguage inthe last yearIs excitedabout thepotential ofLLMs ineducationCan explain thedifferencebetween causaland maskedlanguagemodelsIs familiarwith theconcept ofpromptengineeringHaspublishedresearch onmultilingualLLMsCan namethreedifferent LLMarchitecturesHas experiencewith low-resourcelanguages inNLPIs optimisticabout thefuture ofhuman-AIcollaborationHas collaboratedon a researchpaper withsomeone from adifferent continentHassuccessfullydebugged acomplexLLMHasparticipated ina hackathonfocused on AIor LLMsHas traveledinternationallyto attend thisconferenceIs interestedin the ethicalimplicationsof generativeAIHas used anLLM in alanguageother thanenglishHas used anLLM tosummarizeresearchpapersHas presenteda paper onnaturallanguagegenerationHas used agenerative AImodel tocreate art ormusicCanrecommenda good AI ortech relatedpodcastHasexperiencewith fine-tuning a pre-trained LLMHas apreferred AIresearch toolthey canrecommendHascontributedto an open-source AIprojectHasattended anICMLconferencebeforeKnows atleast threeprogramminglanguages

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