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New methods probe LLM forecasting accuracy and calibration

By PulseAugur Editorial · [2 sources] ·

Researchers have developed new methods to evaluate the forecasting abilities of large language models (LLMs) by addressing issues of data leakage and calibration. One approach, Hindcast, replays prediction markets from a specific past date, preventing models from accessing post-event information or training data that includes future outcomes. Another study probes LLMs' internal representations to assess their calibration and the faithfulness of their reasoning. This research found that internal activations provide a more reliable signal for calibration and can act as lie detectors, revealing that forecasts are often determined before the reasoning process begins. AI

IMPACT These methods could lead to more reliable LLM evaluations and improved AI forecasting capabilities.

RANK_REASON The cluster contains two academic papers detailing novel methods for evaluating LLM forecasting capabilities.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New methods probe LLM forecasting accuracy and calibration

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The cluster contains two academic papers detailing novel methods for evaluating LLM forecasting capabilities.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Ben Zhou ·

    Hindcast: Replaying Prediction Markets to Evaluate LLM Forecasters

    Forecasters are evaluated by backtesting, which replays resolved questions and grades the probability the system would have assigned before the outcome was known. For LLMs, two channels leak the answer into this test. A model that retrieves can surface reports written after the e…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness

    Large language models fine-tuned for forecasting can be accurate yet poorly calibrated, and their chain-of-thought (CoT) reasoning may not faithfully reflect the evidence behind a forecast. We ask whether internal representations offer a more direct window into both. Working with…

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