Can explain thedifferencebetween causaland maskedlanguagemodelsCanrecommenda good AI ortech relatedpodcastIs currentlyworking on aprojectinvolving cross-lingual transferlearningHasattended anICMLconferencebeforeHas used agenerative AImodel for anon-academicpurposeHas collaboratedon a researchpaper withsomeone from adifferent continentHas traveledinternationallyto attend thisconferenceHasparticipated ina hackathonfocused on AIor LLMsIs interestedin the ethicalimplicationsof generativeAIHas apreferred AIresearch toolthey canrecommendHasexperiencewith fine-tuning a pre-trained LLMHas experiencewith low-resourcelanguages inNLPHascontributedto an open-source AIprojectHas used anLLM tosummarizeresearchpapersIs familiarwith theconcept ofpromptengineeringIs optimisticabout thefuture ofhuman-AIcollaborationHas used agenerative AImodel tocreate art ormusicIs excitedabout thepotential ofLLMs ineducationHaspublishedresearch onmultilingualLLMsCan namethreedifferent LLMarchitecturesKnows atleast threeprogramminglanguagesHassuccessfullydebugged acomplexLLMHas learneda newlanguage inthe last yearHas presenteda paper onnaturallanguagegenerationCan explain thedifferencebetween causaland maskedlanguagemodelsCanrecommenda good AI ortech relatedpodcastIs currentlyworking on aprojectinvolving cross-lingual transferlearningHasattended anICMLconferencebeforeHas used agenerative AImodel for anon-academicpurposeHas collaboratedon a researchpaper withsomeone from adifferent continentHas traveledinternationallyto attend thisconferenceHasparticipated ina hackathonfocused on AIor LLMsIs interestedin the ethicalimplicationsof generativeAIHas apreferred AIresearch toolthey canrecommendHasexperiencewith fine-tuning a pre-trained LLMHas experiencewith low-resourcelanguages inNLPHascontributedto an open-source AIprojectHas used anLLM tosummarizeresearchpapersIs familiarwith theconcept ofpromptengineeringIs optimisticabout thefuture ofhuman-AIcollaborationHas used agenerative AImodel tocreate art ormusicIs excitedabout thepotential ofLLMs ineducationHaspublishedresearch onmultilingualLLMsCan namethreedifferent LLMarchitecturesKnows atleast threeprogramminglanguagesHassuccessfullydebugged acomplexLLMHas learneda newlanguage inthe last yearHas presenteda paper onnaturallanguagegeneration

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