Hassuccessfullydebugged acomplexLLMHas apreferred AIresearch toolthey canrecommendIs excitedabout thepotential ofLLMs ineducationIs optimisticabout thefuture ofhuman-AIcollaborationHasattended anICMLconferencebeforeHaspublishedresearch onmultilingualLLMsHas used agenerative AImodel for anon-academicpurposeHas presenteda paper onnaturallanguagegenerationHas used anLLM in alanguageother thanenglishHas used agenerative AImodel tocreate art ormusicIs interestedin the ethicalimplicationsof generativeAICan explain thedifferencebetween causaland maskedlanguagemodelsHasexperiencewith fine-tuning a pre-trained LLMHascontributedto an open-source AIprojectCanrecommenda good AI ortech relatedpodcastHas traveledinternationallyto attend thisconferenceHasparticipated ina hackathonfocused on AIor LLMsCan namethreedifferent LLMarchitecturesIs familiarwith theconcept ofpromptengineeringHas learneda newlanguage inthe last yearHas collaboratedon a researchpaper withsomeone from adifferent continentHas used anLLM tosummarizeresearchpapersKnows atleast threeprogramminglanguagesHas experiencewith low-resourcelanguages inNLPHassuccessfullydebugged acomplexLLMHas apreferred AIresearch toolthey canrecommendIs excitedabout thepotential ofLLMs ineducationIs optimisticabout thefuture ofhuman-AIcollaborationHasattended anICMLconferencebeforeHaspublishedresearch onmultilingualLLMsHas used agenerative AImodel for anon-academicpurposeHas presenteda paper onnaturallanguagegenerationHas used anLLM in alanguageother thanenglishHas used agenerative AImodel tocreate art ormusicIs interestedin the ethicalimplicationsof generativeAICan explain thedifferencebetween causaland maskedlanguagemodelsHasexperiencewith fine-tuning a pre-trained LLMHascontributedto an open-source AIprojectCanrecommenda good AI ortech relatedpodcastHas traveledinternationallyto attend thisconferenceHasparticipated ina hackathonfocused on AIor LLMsCan namethreedifferent LLMarchitecturesIs familiarwith theconcept ofpromptengineeringHas learneda newlanguage inthe last yearHas collaboratedon a researchpaper withsomeone from adifferent continentHas used anLLM tosummarizeresearchpapersKnows atleast threeprogramminglanguagesHas experiencewith low-resourcelanguages inNLP

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