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cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 20·5 min read
Different Facets of Verbalised Overconfidence: an Interpretability Study
TL;DR
Qwen3-4B, a large language model, exhibits overconfidence by providing assertive answers despite uncertainty. This study identifies features that contribute to this behavior and proposes methods to mitigate overconfident errors.
✦ Why It Matters
Implement targeted interventions on uncertainty features in your models to reduce overconfidence today.
Key Takeaways
- Explore the use of verbal epistemic markers in model outputs.
- Implement numeric confidence scores to assess model certainty.
- Identify and modify uncertainty features to reduce overconfidence.
- Test findings across different languages and tasks for robustness.
How It Works
The study employs controlled reasoning scenarios to manipulate logical necessity and possibility, allowing for a detailed analysis of how Qwen3-4B expresses uncertainty. It identifies a broad coalition of features that promote certainty, while a sparse set of features is responsible for expressing uncertainty.
By intervening on these uncertainty features, the researchers demonstrate a causal link to overconfidence and provide a method to mitigate it.