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