Got an invite to be on the IUI 2024 program committee. I think I have to accept.

Order batteries!

From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models

• Language models (LMs) are pretrained on diverse data sources, including news, discussion forums, books, and online encyclopedias. A significant portion of this data includes opinions and perspectives which, on one hand, celebrate democracy and diversity of ideas, and on the other hand are inherently socially biased. Our work develops new methods to (1) measure political biases in LMs trained on such corpora, along social and economic axes, and (2) measure the fairness of downstream NLP models trained on top of politically biased LMs. We focus on hate speech and misinformation detection, aiming to empirically quantify the effects of political (social, economic) biases in pretraining data on the fairness of high-stakes social-oriented tasks. Our findings reveal that pretrained LMs do have political leanings that reinforce the polarization present in pretraining corpora, propagating social biases into hate speech predictions and misinformation detectors. We discuss the implications of our findings for NLP research and propose future directions to mitigate unfairness.

SBIRs

• 2:00 BMD status
• Sent a bunch of papers over to the interns for the background section
• Started on the Q6 report

GPT Agents

• 8:30 – 9:30 more app development. And have the email domains rippled out yet?
  • Great progress!
• 3:00 – 4:00 more app development. Need to get the public version running before the meeting.
• 2:30 Alden meeting?
• 4:00 LLM meeting