Is excitedabout thepotential ofLLMs ineducationHascontributedto an open-source AIprojectHas used anLLM tosummarizeresearchpapersCan namethreedifferent LLMarchitecturesHas used agenerative AImodel tocreate art ormusicKnows atleast threeprogramminglanguagesHas used agenerative AImodel for anon-academicpurposeHasattended anICMLconferencebeforeHas apreferred AIresearch toolthey canrecommendHassuccessfullydebugged acomplexLLMCan explain thedifferencebetween causaland maskedlanguagemodelsCanrecommenda good AI ortech relatedpodcastHas presenteda paper onnaturallanguagegenerationIs familiarwith theconcept ofpromptengineeringHas traveledinternationallyto attend thisconferenceHasexperiencewith fine-tuning a pre-trained LLMIs optimisticabout thefuture ofhuman-AIcollaborationIs interestedin the ethicalimplicationsof generativeAIIs currentlyworking on aprojectinvolving cross-lingual transferlearningHaspublishedresearch onmultilingualLLMsHas collaboratedon a researchpaper withsomeone from adifferent continentHas experiencewith low-resourcelanguages inNLPHasparticipated ina hackathonfocused on AIor LLMsHas learneda newlanguage inthe last yearIs excitedabout thepotential ofLLMs ineducationHascontributedto an open-source AIprojectHas used anLLM tosummarizeresearchpapersCan namethreedifferent LLMarchitecturesHas used agenerative AImodel tocreate art ormusicKnows atleast threeprogramminglanguagesHas used agenerative AImodel for anon-academicpurposeHasattended anICMLconferencebeforeHas apreferred AIresearch toolthey canrecommendHassuccessfullydebugged acomplexLLMCan explain thedifferencebetween causaland maskedlanguagemodelsCanrecommenda good AI ortech relatedpodcastHas presenteda paper onnaturallanguagegenerationIs familiarwith theconcept ofpromptengineeringHas traveledinternationallyto attend thisconferenceHasexperiencewith fine-tuning a pre-trained LLMIs optimisticabout thefuture ofhuman-AIcollaborationIs interestedin the ethicalimplicationsof generativeAIIs currentlyworking on aprojectinvolving cross-lingual transferlearningHaspublishedresearch onmultilingualLLMsHas collaboratedon a researchpaper withsomeone from adifferent continentHas experiencewith low-resourcelanguages inNLPHasparticipated ina hackathonfocused on AIor LLMsHas learneda newlanguage inthe last year

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