Hascontributedto an open-source AIprojectHas used anLLM tosummarizeresearchpapersHas used agenerative AImodel for anon-academicpurposeIs excitedabout thepotential ofLLMs ineducationHas presenteda paper onnaturallanguagegenerationHasexperiencewith fine-tuning a pre-trained LLMCanrecommenda good AI ortech relatedpodcastHasparticipated ina hackathonfocused on AIor LLMsHas learneda newlanguage inthe last yearHassuccessfullydebugged acomplexLLMCan namethreedifferent LLMarchitecturesCan explain thedifferencebetween causaland maskedlanguagemodelsHas apreferred AIresearch toolthey canrecommendHas used agenerative AImodel tocreate art ormusicHas experiencewith low-resourcelanguages inNLPIs familiarwith theconcept ofpromptengineeringIs currentlyworking on aprojectinvolving cross-lingual transferlearningHas traveledinternationallyto attend thisconferenceHaspublishedresearch onmultilingualLLMsHasattended anICMLconferencebeforeKnows atleast threeprogramminglanguagesHas collaboratedon a researchpaper withsomeone from adifferent continentIs optimisticabout thefuture ofhuman-AIcollaborationIs interestedin the ethicalimplicationsof generativeAIHascontributedto an open-source AIprojectHas used anLLM tosummarizeresearchpapersHas used agenerative AImodel for anon-academicpurposeIs excitedabout thepotential ofLLMs ineducationHas presenteda paper onnaturallanguagegenerationHasexperiencewith fine-tuning a pre-trained LLMCanrecommenda good AI ortech relatedpodcastHasparticipated ina hackathonfocused on AIor LLMsHas learneda newlanguage inthe last yearHassuccessfullydebugged acomplexLLMCan namethreedifferent LLMarchitecturesCan explain thedifferencebetween causaland maskedlanguagemodelsHas apreferred AIresearch toolthey canrecommendHas used agenerative AImodel tocreate art ormusicHas experiencewith low-resourcelanguages inNLPIs familiarwith theconcept ofpromptengineeringIs currentlyworking on aprojectinvolving cross-lingual transferlearningHas traveledinternationallyto attend thisconferenceHaspublishedresearch onmultilingualLLMsHasattended anICMLconferencebeforeKnows atleast threeprogramminglanguagesHas collaboratedon a researchpaper withsomeone from adifferent continentIs optimisticabout thefuture ofhuman-AIcollaborationIs interestedin the ethicalimplicationsof generativeAI

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