Hasattended anICMLconferencebeforeIs familiarwith theconcept ofpromptengineeringHas learneda newlanguage inthe last yearIs optimisticabout thefuture ofhuman-AIcollaborationHas traveledinternationallyto attend thisconferenceHassuccessfullydebugged acomplexLLMCan namethreedifferent LLMarchitecturesHasparticipated ina hackathonfocused on AIor LLMsHasexperiencewith fine-tuning a pre-trained LLMCanrecommenda good AI ortech relatedpodcastIs interestedin the ethicalimplicationsof generativeAIIs excitedabout thepotential ofLLMs ineducationHaspublishedresearch onmultilingualLLMsHas presenteda paper onnaturallanguagegenerationHas used anLLM tosummarizeresearchpapersIs currentlyworking on aprojectinvolving cross-lingual transferlearningHas apreferred AIresearch toolthey canrecommendCan explain thedifferencebetween causaland maskedlanguagemodelsHascontributedto an open-source AIprojectHas collaboratedon a researchpaper withsomeone from adifferent continentHas used agenerative AImodel tocreate art ormusicKnows atleast threeprogramminglanguagesHas experiencewith low-resourcelanguages inNLPHas used agenerative AImodel for anon-academicpurposeHasattended anICMLconferencebeforeIs familiarwith theconcept ofpromptengineeringHas learneda newlanguage inthe last yearIs optimisticabout thefuture ofhuman-AIcollaborationHas traveledinternationallyto attend thisconferenceHassuccessfullydebugged acomplexLLMCan namethreedifferent LLMarchitecturesHasparticipated ina hackathonfocused on AIor LLMsHasexperiencewith fine-tuning a pre-trained LLMCanrecommenda good AI ortech relatedpodcastIs interestedin the ethicalimplicationsof generativeAIIs excitedabout thepotential ofLLMs ineducationHaspublishedresearch onmultilingualLLMsHas presenteda paper onnaturallanguagegenerationHas used anLLM tosummarizeresearchpapersIs currentlyworking on aprojectinvolving cross-lingual transferlearningHas apreferred AIresearch toolthey canrecommendCan explain thedifferencebetween causaland maskedlanguagemodelsHascontributedto an open-source AIprojectHas collaboratedon a researchpaper withsomeone from adifferent continentHas used agenerative AImodel tocreate art ormusicKnows atleast threeprogramminglanguagesHas experiencewith low-resourcelanguages inNLPHas used agenerative AImodel for anon-academicpurpose

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