What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness
arXiv:2607.08046v1 Announce Type: new Abstract: 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 Eternis-Forecaster 8B on OpenForesight, we train representation-pooling probes on intermediate activations and find they achieve substantially better calibration; a result that also holds for GLM-4.7-Flash and GLM-4.5-Air. We then assess CoT faithfulness through evidence
The proliferation of advanced LLMs necessitates deeper understanding of their reasoning processes to enhance reliability and trustworthiness, especially as they are deployed in critical forecasting roles.
This research offers a method to significantly improve the calibration and faithfulness of large language models, crucial for their effective and safe deployment in decision-making and forecasting applications across various industries.
The ability to probe internal representations directly provides a new avenue for model auditing and improving AI transparency, moving beyond reliance on potentially unfaithful chain-of-thought outputs.
- · AI developers
- · Forecasting platforms
- · Industries relying on AI predictions
- · AI safety researchers
- · Unreliable black-box AI systems
- · Decision-makers using uncalibrated AI without scrutiny
More accurate and trustworthy AI forecasts become available for business and strategic planning.
Increased adoption of AI in high-stakes forecasting domains due to improved calibration and faithfulness.
New regulatory frameworks may emerge, requiring internal transparency and auditability of AI systems based on such probing techniques.
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Read at arXiv cs.CL