Approaching Human-Level Forecasting with Language Models

• URL: http://arxiv.org/abs/2402.18563v1
• Date: Wed, 28 Feb 2024 18:54:18 GMT
• Title: Approaching Human-Level Forecasting with Language Models
• Authors: Danny Halawi, Fred Zhang, Chen Yueh-Han, Jacob Steinhardt
• Abstract summary: We study whether language models (LMs) can forecast at the level of competitive human forecasters. We develop a retrieval-augmented LM system designed to automatically search for relevant information, generate forecasts, and aggregate predictions.
• Score: 34.202996056121
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Forecasting future events is important for policy and decision making. In this work, we study whether language models (LMs) can forecast at the level of competitive human forecasters. Towards this goal, we develop a retrieval-augmented LM system designed to automatically search for relevant information, generate forecasts, and aggregate predictions. To facilitate our study, we collect a large dataset of questions from competitive forecasting platforms. Under a test set published after the knowledge cut-offs of our LMs, we evaluate the end-to-end performance of our system against the aggregates of human forecasts. On average, the system nears the crowd aggregate of competitive forecasters, and in some settings surpasses it. Our work suggests that using LMs to forecast the future could provide accurate predictions at scale and help to inform institutional decision making.

Related papers

• Performative Prediction on Games and Mechanism Design [69.7933059664256]
  We study a collective risk dilemma where agents decide whether to trust predictions based on past accuracy. As predictions shape collective outcomes, social welfare arises naturally as a metric of concern. We show how to achieve better trade-offs and use them for mechanism design.
  arXiv  Detail & Related papers  (2024-08-09T16:03:44Z)
• Deep learning for precipitation nowcasting: A survey from the perspective of time series forecasting [4.5424061912112474]
  This paper reviews recent progress in time series precipitation forecasting models using deep learning. We categorize forecasting models into textitrecursive and textitmultiple strategies based on their approaches to predict future frames. We evaluate current deep learning-based models for precipitation forecasting on a public benchmark, discuss their limitations and challenges, and present some promising research directions.
  arXiv  Detail & Related papers  (2024-06-07T12:07:09Z)
• Can Language Models Use Forecasting Strategies? [14.332379032371612]
  We describe experiments using a novel dataset of real world events and associated human predictions. We find that models still struggle to make accurate predictions about the future.
  arXiv  Detail & Related papers  (2024-06-06T19:01:42Z)
• Wisdom of the Silicon Crowd: LLM Ensemble Prediction Capabilities Rival Human Crowd Accuracy [1.999925939110439]
  We use an ensemble approach consisting of a crowd of twelve large language models (LLMs) We compare the aggregated LLM predictions on 31 binary questions to that of a crowd of human forecasters from a three-month forecasting tournament. We find that both models' forecasting accuracy benefits from exposure to the median human prediction as information.
  arXiv  Detail & Related papers  (2024-02-29T17:27:59Z)
• Prediction-Oriented Bayesian Active Learning [51.426960808684655]
  Expected predictive information gain (EPIG) is an acquisition function that measures information gain in the space of predictions rather than parameters. EPIG leads to stronger predictive performance compared with BALD across a range of datasets and models.
  arXiv  Detail & Related papers  (2023-04-17T10:59:57Z)
• Forecasting Future World Events with Neural Networks [68.43460909545063]
  Autocast is a dataset containing thousands of forecasting questions and an accompanying news corpus. The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts. We test language models on our forecasting task and find that performance is far below a human expert baseline.
  arXiv  Detail & Related papers  (2022-06-30T17:59:14Z)
• What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience [63.75363908696257]
  computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations. An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making. We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
  arXiv  Detail & Related papers  (2022-06-13T21:31:06Z)
• Test-time Collective Prediction [73.74982509510961]
  Multiple parties in machine learning want to jointly make predictions on future test points. Agents wish to benefit from the collective expertise of the full set of agents, but may not be willing to release their data or model parameters. We explore a decentralized mechanism to make collective predictions at test time, leveraging each agent's pre-trained model.
  arXiv  Detail & Related papers  (2021-06-22T18:29:58Z)
• When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making [68.19284302320146]
  We carry out user studies to assess how people with differing levels of expertise respond to different types of predictive uncertainty. We found that showing posterior predictive distributions led to smaller disagreements with the ML model's predictions. This suggests that posterior predictive distributions can potentially serve as useful decision aids which should be used with caution and take into account the type of distribution and the expertise of the human.
  arXiv  Detail & Related papers  (2020-11-12T02:23:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.