Knows atleast threeprogramminglanguagesHas experiencewith low-resourcelanguages inNLPIs familiarwith theconcept ofpromptengineeringHas used agenerative AImodel for anon-academicpurposeHas learneda newlanguage inthe last yearHasattended anICMLconferencebeforeIs interestedin the ethicalimplicationsof generativeAIHas apreferred AIresearch toolthey canrecommendCanrecommenda good AI ortech relatedpodcastHasparticipated ina hackathonfocused on AIor LLMsHas used anLLM tosummarizeresearchpapersHasexperiencewith fine-tuning a pre-trained LLMHas presenteda paper onnaturallanguagegenerationHaspublishedresearch onmultilingualLLMsCan explain thedifferencebetween causaland maskedlanguagemodelsCan namethreedifferent LLMarchitecturesIs excitedabout thepotential ofLLMs ineducationHassuccessfullydebugged acomplexLLMHascontributedto an open-source AIprojectIs optimisticabout thefuture ofhuman-AIcollaborationHas collaboratedon a researchpaper withsomeone from adifferent continentHas traveledinternationallyto attend thisconferenceIs currentlyworking on aprojectinvolving cross-lingual transferlearningHas used agenerative AImodel tocreate art ormusicKnows atleast threeprogramminglanguagesHas experiencewith low-resourcelanguages inNLPIs familiarwith theconcept ofpromptengineeringHas used agenerative AImodel for anon-academicpurposeHas learneda newlanguage inthe last yearHasattended anICMLconferencebeforeIs interestedin the ethicalimplicationsof generativeAIHas apreferred AIresearch toolthey canrecommendCanrecommenda good AI ortech relatedpodcastHasparticipated ina hackathonfocused on AIor LLMsHas used anLLM tosummarizeresearchpapersHasexperiencewith fine-tuning a pre-trained LLMHas presenteda paper onnaturallanguagegenerationHaspublishedresearch onmultilingualLLMsCan explain thedifferencebetween causaland maskedlanguagemodelsCan namethreedifferent LLMarchitecturesIs excitedabout thepotential ofLLMs ineducationHassuccessfullydebugged acomplexLLMHascontributedto an open-source AIprojectIs optimisticabout thefuture ofhuman-AIcollaborationHas collaboratedon a researchpaper withsomeone from adifferent continentHas traveledinternationallyto attend thisconferenceIs currentlyworking on aprojectinvolving cross-lingual transferlearningHas 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. Knows at least three programming languages
  2. Has experience with low-resource languages in NLP
  3. Is familiar with the concept of prompt engineering
  4. Has used a generative AI model for a non-academic purpose
  5. Has learned a new language in the last year
  6. Has attended an ICML conference before
  7. Is interested in the ethical implications of generative AI
  8. Has a preferred AI research tool they can recommend
  9. Can recommend a good AI or tech related podcast
  10. Has participated in a hackathon focused on AI or LLMs
  11. Has used an LLM to summarize research papers
  12. Has experience with fine-tuning a pre-trained LLM
  13. Has presented a paper on natural language generation
  14. Has published research on multilingual LLMs
  15. Can explain the difference between causal and masked language models
  16. Can name three different LLM architectures
  17. Is excited about the potential of LLMs in education
  18. Has successfully debugged a complex LLM
  19. Has contributed to an open-source AI project
  20. Is optimistic about the future of human-AI collaboration
  21. Has collaborated on a research paper with someone from a different continent
  22. Has traveled internationally to attend this conference
  23. Is currently working on a project involving cross-lingual transfer learning
  24. Has used a generative AI model to create art or music