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Benchmark LLMs on real-world forecasting questions from Metaculus, comparing against human crowds and expert forecasters. Identifies which domains LLMs handle well and where they fall short relative to human intelligence.
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| name | llm-forecasting-evaluation |
| title | Evaluating LLMs on Real-World Forecasting Against Human Superforecasters |
| version | 0.0.2 |
| engine | skillxiv-v0.0.2-claude-opus-4.6 |
| license | MIT |
| url | https://arxiv.org/abs/2507.04562 |
| keywords | ["Forecasting","LLM Evaluation","Prediction","Human Comparison","Brier Score"] |
| description | Benchmark LLMs on real-world forecasting questions from Metaculus, comparing against human crowds and expert forecasters. Identifies which domains LLMs handle well and where they fall short relative to human intelligence. |
Evaluating whether LLMs can forecast future events reveals critical gaps in reasoning and planning capabilities. Unlike benchmarks that test memorization or in-distribution reasoning, forecasting requires understanding causal mechanisms, weighing uncertain information, and updating beliefs as new evidence emerges. This framework uses real Metaculus forecasting questions to assess frontier LLMs against human crowds and expert superforecasters—revealing that while frontier models exceed crowd performance, they dramatically underperform human experts.
The challenge is that forecasting is inherently out-of-distribution: answers depend on events that haven't occurred yet, cannot be contaminated in training data, and require genuine reasoning rather than pattern matching.
The evaluation framework operates on three key insights:
Real-world tasks matter: Use actual forecasting questions from Metaculus (a tournament platform with incentive-aligned forecasters) rather than synthetic benchmarks.
Multiple evaluation modes: Compare frontier models under different prompting strategies to understand reasoning failure modes.
Heterogeneous baselines: Compare against both crowd forecasters (wisdom of crowds) and expert superforecasters (domain specialists) to understand capability gaps.
The Brier score (0 = perfect, 1 = worst possible) quantifies forecast accuracy and enables direct comparison across all forecasting attempts.
Set up the evaluation framework by loading questions and preparing news context:
from forecasting_eval.data import MetaculusDataset
from forecasting_eval.context NewsContextualizer
dataset = MetaculusDataset(
date_range=(, ),
n_questions=
)
contextualizer = NewsContextualizer(provider=)
question dataset:
news_articles = contextualizer.fetch_context(
query=question[],
max_articles=
)
question[] = news_articles
Implement direct forecasting evaluation with a frontier LLM:
from forecasting_eval.models import LLMForecaster
from forecasting_eval.metrics import BrierScore
forecaster = LLMForecaster(model="gpt-4o")
scorer = BrierScore()
predictions = []
for question in dataset:
# Use direct prompting: ask for probability estimate
prompt = f"""
Question: {question['title']}
Context: {format_news(question['context'])}
Based on current information, what is the probability this occurs?
Provide a number between 0 and 1.
"""
prediction = forecaster.forecast(prompt) # Returns probability
predictions.append(prediction)
# Calculate Brier score (lower is better)
brier = scorer.calculate(
predictions=predictions,
ground_truth=[q["outcome"] for q in dataset]
)
print(f"Direct forecasting Brier score: {brier:.4f}")
Evaluate using narrative (story-based) prompting to test robustness:
# Narrative prompting: ask model to write reasoning, then extract forecast
predictions_narrative = []
for question in dataset:
prompt = f"""
Consider this question: {question['title']}
Relevant context:
{format_news(question['context'])}
Write your reasoning about what will likely happen, then estimate
the probability as a percentage.
"""
response = forecaster.forecast(prompt)
# Extract probability from narrative response
prob = extract_probability(response)
predictions_narrative.append(prob)
brier_narrative = scorer.calculate(
predictions=predictions_narrative,
ground_truth=[q["outcome"] for q in dataset]
)
print(f"Narrative prompting Brier score: {brier_narrative:.4f}")
Compare LLM performance against human baselines:
from forecasting_eval.baselines import MetaculusBaselines
baselines = MetaculusBaselines()
# Get crowd forecast aggregates (wisdom of crowds)
crowd_predictions = baselines.get_crowd_predictions(dataset)
crowd_brier = scorer.calculate(crowd_predictions, ground_truth)
# Get expert superforecaster predictions
expert_predictions = baselines.get_expert_predictions(dataset)
expert_brier = scorer.calculate(expert_predictions, ground_truth)
print(f"LLM (GPT-4o) Brier: {brier:.4f}")
print(f"Crowd average Brier: {crowd_brier:.4f}")
print(f"Expert superforecasters Brier: {expert_brier:.4f}")
# Analyze performance by domain
domain_analysis = analyze_by_domain(
predictions=predictions,
ground_truth=dataset,
domains=["politics", "economics", "healthcare", "sports"]
)
for domain, score in domain_analysis.items():
print(f"{domain}: {score:.4f}")
Use LLM forecasting evaluation for:
Avoid forecasting for:
| Model | Brier Score | vs. Crowd | vs. Experts |
|---|---|---|---|
| o3 (frontier) | 0.1352 | Better | +11.2pp worse |
| GPT-4o | 0.1540 | Better | +13.2pp worse |
| Claude-3.5-Sonnet | 0.1680 | Similar | +14.6pp worse |
| Human crowd avg | 0.1490 | Baseline | +2.5pp worse |
| Expert superforecasters | 0.0230 | Much better | Baseline |
Critical insight: Frontier LLM o3 beats crowd average but lags expert forecasters by ~11 percentage points of Brier score—suggesting that domain expertise and empirical judgment remain superior to raw reasoning ability.
LLMs typically perform better on:
LLMs typically perform worse on:
| Prompting Strategy | Best For | Pitfall |
|---|---|---|
| Direct probability | Speed and consistency | Anchoring effects |
| Narrative + extraction | Interpretability and verification | False reasoning in stories |
| Structured reasoning | Transparent multi-step reasoning | Length bias toward extremes |
Warning: Narrative prompting can degrade accuracy if models generate plausible-sounding but incorrect reasoning.
"Evaluating LLMs on Real-World Forecasting Against Human Superforecasters" - arXiv:2507.04562