Project description

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

What is your track record on similar projects?

• 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.

How will you spend your funding?

• 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.