Pitfalls in Evaluating Language Model Forecasters
Overview
Overall Novelty Assessment
The paper contributes a critical methodological analysis of LLM forecasting evaluation, identifying temporal leakage issues and extrapolation challenges that may undermine performance claims. It resides in the Real-World Event Forecasting leaf, which contains four papers total including this one. The three sibling papers focus on benchmark construction and comparative performance assessment (ForecastBench, Expert Forecasters Comparison, Forecasting Strategies), making this a moderately populated but focused research direction. The paper's emphasis on evaluation validity rather than forecasting performance itself represents a distinct angle within this cluster.
The taxonomy reveals that Real-World Event Forecasting sits alongside Time Series and Sequential Forecasting and Domain-Specific Forecasting Applications within the broader Forecasting and Prediction Tasks branch. Neighboring branches include Clinical and Healthcare Prediction and Biological and Scientific Prediction, which address specialized forecasting domains. The General LLM Evaluation Frameworks branch contains methodological work on evaluation metrics and LLM-as-Evaluator approaches, which share conceptual overlap with this paper's concerns about evaluation rigor. The paper bridges practical forecasting assessment with broader evaluation methodology critiques found in works like Unfair Evaluators and Model Written Evaluations.
Among thirty candidates examined, all three contributions show evidence of prior work overlap. The temporal leakage contribution examined ten candidates with two appearing to provide overlapping analysis. The extrapolation challenges contribution examined ten candidates with one refutable match. The concrete demonstrations contribution examined ten candidates with two potential overlaps. These statistics suggest that within the limited search scope, concerns about evaluation methodology in LLM forecasting have received some prior attention, though the specific framing and systematic analysis may differ across works.
Based on the top-thirty semantic matches examined, the paper appears to occupy a methodologically critical position within an active forecasting evaluation area. The analysis does not cover the full breadth of evaluation methodology literature or all forecasting domains, and the refutable matches may address overlapping concerns from different angles rather than identical contributions. The limited search scope means additional relevant prior work may exist beyond the candidates examined.
Taxonomy
Research Landscape Overview
Claimed Contributions
The authors systematically identify and categorize various forms of temporal leakage that compromise the trustworthiness of LLM forecasting evaluations, including logical leakage from backtesting, unreliable date-restricted retrieval, and over-reliance on model cutoff dates.
The authors analyze how good benchmark performance may not translate to real-world forecasting ability, identifying issues such as piggybacking on human forecasts, gaming benchmarks through strategic betting, and skewed data distributions that limit generalizability.
The authors provide empirical evidence and concrete examples from existing LLM forecasting papers to demonstrate how various evaluation flaws manifest in practice, showing that these issues affect real published benchmarks and may have led to overly optimistic assessments.
Core Task Comparisons
Comparisons with papers in the same taxonomy category
[18] Forecastbench: A dynamic benchmark of ai forecasting capabilities PDF
[33] Evaluating LLMs on Real-World Forecasting Against Expert Forecasters PDF
[45] Can Language Models Use Forecasting Strategies? PDF
Contribution Analysis
Detailed comparisons for each claimed contribution
Identification of temporal leakage issues in LLM forecasting evaluation
The authors systematically identify and categorize various forms of temporal leakage that compromise the trustworthiness of LLM forecasting evaluations, including logical leakage from backtesting, unreliable date-restricted retrieval, and over-reliance on model cutoff dates.
[74] Lookahead bias in pretrained language models PDF
[77] Evaluating forecasting is more difficult than other llm evaluations PDF
[38] Scieval: A multi-level large language model evaluation benchmark for scientific research PDF
[70] Benchmarking benchmark leakage in large language models PDF
[71] Are language models actually useful for time series forecasting? PDF
[72] Mind the Gap: Assessing Temporal Generalization in Neural Language Models PDF
[73] Analyzing information leakage of updates to natural language models PDF
[75] Analyzing Leakage of Personally Identifiable Information in Language Models PDF
[76] Autotimes: Autoregressive time series forecasters via large language models PDF
[78] Assessment methods and protection strategies for data leakage risks in large language models PDF
Analysis of extrapolation challenges from benchmarks to real-world forecasting
The authors analyze how good benchmark performance may not translate to real-world forecasting ability, identifying issues such as piggybacking on human forecasts, gaming benchmarks through strategic betting, and skewed data distributions that limit generalizability.
[51] Forecast evaluation for data scientists: common pitfalls and best practices PDF
[61] TravelPlanner: A Benchmark for Real-World Planning with Language Agents PDF
[62] STAR: A Benchmark for Situated Reasoning in Real-World Videos PDF
[63] Largest: A benchmark dataset for large-scale traffic forecasting PDF
[64] Nico++: Towards better benchmarking for domain generalization PDF
[65] A practical generalization metric for deep networks benchmarking PDF
[66] Open graph benchmark: Datasets for machine learning on graphs PDF
[67] Unveiling the limits of deep learning models in hydrological extrapolation tasks PDF
[68] Measuring ai ability to complete long tasks PDF
[69] XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization PDF
Concrete demonstrations of evaluation flaws in prior forecasting work
The authors provide empirical evidence and concrete examples from existing LLM forecasting papers to demonstrate how various evaluation flaws manifest in practice, showing that these issues affect real published benchmarks and may have led to overly optimistic assessments.