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