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 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 ablation and diversionary injection: removing an influential source in the prompt often changes the model's forecast while leaving the reasoning...
| Authors | Raphaël Sarfati, Pratyush Ranjan Tiwari, Siddharth Boppana et al. |
|---|---|
| Primary category | cs.CL |
| Published | 9 July 2026 |
| Last updated at source | 9 July 2026 |
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