Paper: Forecasting world events with neural nets
Paper authors: Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, Dan Hendrycks This post contains the paper’s abstract and excerpts from the paper (with slight modifications). Paper Abstract Forecasts of climate, geopolitical conflict, pandemics, and economic indicators help shape policy and decision making. In these domains, the judgment of expert humans contributes to the best forecasts. Can these forecasts be automated? To this end, we introduce Autocast, a dataset containing thousands of forecasting questions and an accompanying 200GB news corpus. Questions are taken from forecasting tournaments (including Metaculus), ensuring high quality, real-world importance, and diversity. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts (avoiding leakage from the future). Motivated by the difficulty of forecasting numbers across orders of magnitude (e.g. global cases of COVID-19 in 2022), we also curate IntervalQA, a dataset of numerical questions and metrics for calibration. We test language models on our forecasting task and find that performance is far below a human expert baseline. However, performance improves with increased model size and incorporation of relevant information from the news corpus. An example question from the Autocast dataset (taken from Good Judgment Open) The dataset simulates the task faced by human forecasters. The model must generate a forecast each day for a series of days (here 01-14-22 to 03-23-22) given access to the news on that day (but not after that day). So the model makes retrodictions without "cheating" (i.e. having access to future information) Paper Introduction Forecasting plays a crucial role in the modern world. Climate forecasts shape the policies of governments and companies. Economic forecasts influence investment and employment. In 2020, forecasts about the spread of COVID-
Hi Roger, thanks for this comment. You're pointing at a real imprecision in my framing there that should be corrected!
TL;DR I agree the original phrasing was imprecise in describing "learning". The core claim that instilling a generalizing goal-directed disposition through data poisoning is harder than instilling a trigger-behavior link — I still think holds, even granting that the model's world knowledge does most of the representational heavy lifting. The open empirical question is how much harder, and whether clever attack designs can close that gap. I hope to post more on this soon!
Regarding that excerpt, I wasn't precise enough about what I meant by "learn". You're right that a frontier model already... (read 432 more words →)