Interpretable Forecasting with Transformers

I am working with Nuño Sempere on a project to extract latent probabilities from GPT-3.

Primary outcomes:

improve on the state of the art in anti-hallucination and truthful question answering using LLMs.
measure information retrieval + architecture tweaks vs crowd performance on prediction markets.
elicit explanations for the reasoning behind the model's decisions, both directly and indirectly

Our team is currently #7/#61 on the Autocast Competition (forecasting.mlsafety.org). We're prioritizing understandable, legible, and safe behavior above optimizing for capabilities.
Nuño is an expert on forecasting at the Quantified Research Uncertainty Institute. He is the author of forecasting.substack.com, was a summer fellow at FHI, created the "Estimated Value" sequence, made metaforecast.org, and is a founding member of samotsvety.org.
I've been at Microsoft for ~3 years, have a bit of experience with LLMs, did 5 internships, won multiple awards in international competitions (including a $35k prize in the HITB AI Challenge), was invited to speak at an IEEE conference, got into Stanford, and met Geoff Hinton once.

Paying rent for experimentation and testing on cloud GPUs. Only so much you can do with APIs.
We'll apply for a second round of funding to scale up our approach if initial results are promising.