Pitfalls in Evaluating Language Model Forecasters
Authors: name: Daniel Paleka, name: Shashwat Goel, name: Jonas Geiping, name: Florian Tramèr

Large language models (LLMs) have recently been applied to forecasting tasks,
with some works claiming these systems match or exceed human performance. In
this paper, we argue that, as a community, we should be careful about such
conclusions as evaluating LLM forecasters presents unique challenges. We
identify two broad categories of issues: (1) difficulty in trusting evaluation
results due to many forms of temporal leakage, and (2) difficulty in
extrapolating from evaluation performance to real-world forecasting. Through
systematic analysis and concrete examples from prior work, we demonstrate how
evaluation flaws can raise concerns about current and future performance
claims. We argue that more rigorous evaluation methodologies are needed to
confidently assess the forecasting abilities of LLMs.