Can explain thedifferencebetween causaland maskedlanguagemodelsHaspublishedresearch onmultilingualLLMsIs optimisticabout thefuture ofhuman-AIcollaborationHas used anLLM tosummarizeresearchpapersKnows atleast threeprogramminglanguagesHas collaboratedon a researchpaper withsomeone from adifferent continentHasattended anICMLconferencebeforeCan namethreedifferent LLMarchitecturesHasexperiencewith fine-tuning a pre-trained LLMHassuccessfullydebugged acomplexLLMHas presenteda paper onnaturallanguagegenerationHas used anLLM in alanguageother thanenglishHas apreferred AIresearch toolthey canrecommendCanrecommenda good AI ortech relatedpodcastIs familiarwith theconcept ofpromptengineeringIs excitedabout thepotential ofLLMs ineducationHasparticipated ina hackathonfocused on AIor LLMsHas traveledinternationallyto attend thisconferenceHascontributedto an open-source AIprojectHas learneda newlanguage inthe last yearHas experiencewith low-resourcelanguages inNLPIs interestedin the ethicalimplicationsof generativeAIHas used agenerative AImodel for anon-academicpurposeHas used agenerative AImodel tocreate art ormusicCan explain thedifferencebetween causaland maskedlanguagemodelsHaspublishedresearch onmultilingualLLMsIs optimisticabout thefuture ofhuman-AIcollaborationHas used anLLM tosummarizeresearchpapersKnows atleast threeprogramminglanguagesHas collaboratedon a researchpaper withsomeone from adifferent continentHasattended anICMLconferencebeforeCan namethreedifferent LLMarchitecturesHasexperiencewith fine-tuning a pre-trained LLMHassuccessfullydebugged acomplexLLMHas presenteda paper onnaturallanguagegenerationHas used anLLM in alanguageother thanenglishHas apreferred AIresearch toolthey canrecommendCanrecommenda good AI ortech relatedpodcastIs familiarwith theconcept ofpromptengineeringIs excitedabout thepotential ofLLMs ineducationHasparticipated ina hackathonfocused on AIor LLMsHas traveledinternationallyto attend thisconferenceHascontributedto an open-source AIprojectHas learneda newlanguage inthe last yearHas experiencewith low-resourcelanguages inNLPIs interestedin the ethicalimplicationsof generativeAIHas used agenerative AImodel for anon-academicpurposeHas used agenerative AImodel tocreate art ormusic

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.


1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
  1. Can explain the difference between causal and masked language models
  2. Has published research on multilingual LLMs
  3. Is optimistic about the future of human-AI collaboration
  4. Has used an LLM to summarize research papers
  5. Knows at least three programming languages
  6. Has collaborated on a research paper with someone from a different continent
  7. Has attended an ICML conference before
  8. Can name three different LLM architectures
  9. Has experience with fine-tuning a pre-trained LLM
  10. Has successfully debugged a complex LLM
  11. Has presented a paper on natural language generation
  12. Has used an LLM in a language other than english
  13. Has a preferred AI research tool they can recommend
  14. Can recommend a good AI or tech related podcast
  15. Is familiar with the concept of prompt engineering
  16. Is excited about the potential of LLMs in education
  17. Has participated in a hackathon focused on AI or LLMs
  18. Has traveled internationally to attend this conference
  19. Has contributed to an open-source AI project
  20. Has learned a new language in the last year
  21. Has experience with low-resource languages in NLP
  22. Is interested in the ethical implications of generative AI
  23. Has used a generative AI model for a non-academic purpose
  24. Has used a generative AI model to create art or music