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[Keynote #2] Architectures of Ambiguity Mapping the Technical Hurdles...

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Claims that LLMs read words but not the world, failing Filipino sarcasm about nine times out of ten, drive two concrete projects: a 10,000‑message benchmark called PhilSarcasm with triple annotations and cultural scaffolding to teach meaning flips, and LIGTAS, a toxicity benchmark that labels eight harm categories to probe off‑the‑shelf safety filters. Results show cultural distortion from western training data and safety rules misfiring; recommended actions are localized evaluation, honest benchmark scope, and dataset sharing and tooling for regional languages.

AI-written summary · about

  1. 0:00Nuance gap in LLMs
  2. 3:18Cultural context in AI responses
  3. 10:10Python developers and ambiguity
  4. 12:22Four architectures of ambiguity
  5. 16:19Phil Sarcasm and LIGTAS projects
  6. 23:58Improving inclusivity in Python
  7. 28:17Culturally grounded AI systems
  8. 30:45Q&A
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