Probing and Steering Uncertainty in Biomedical Language Models: Representational Structure and Behavioral Limits

2026-06-25 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Health & Medical Research · Depth: Expert, quick

Summary

Biomedical language models often produce overly confident clinical statements despite ambiguous evidence. A study investigated whether linguistic uncertainty, defined as hedged epistemic stance (e.g., "consistent with," "cannot exclude"), is encoded in model representations and can be controlled without retraining. Across six biomedical language models, including causal decoders and bidirectional encoders, the research found that uncertainty is captured by a robust low-dimensional linear structure within hidden states. By applying activation steering, this representation was directly manipulated, leading to increased hedged generation in decoder models and targeted uncertainty shifts in encoder representations. These findings indicate that epistemic stance is an interpretable and controllable feature of biomedical language model representations, with implications for generating safer and more calibrated clinical text.

Key takeaway

For AI Scientists developing biomedical language models, you should recognize that linguistic uncertainty is an interpretable and controllable feature within model representations. Implement activation steering techniques to directly manipulate hidden states, ensuring your models generate safer, more calibrated clinical text by precisely controlling their epistemic stance. This approach can mitigate the risk of overly confident statements from ambiguous evidence.

Key insights

Linguistic uncertainty is an interpretable, controllable feature within biomedical language model representations, enabling safer clinical text.

Principles

• Uncertainty forms a robust low-dimensional linear structure.
• Epistemic stance is controllable via activation steering.

Method

Activation steering directly manipulates hidden state representations to control linguistic uncertainty, increasing hedged generation in decoders and shifting encoder representations.

In practice

• Use activation steering for calibrated clinical text.
• Manipulate representations to control epistemic stance.

Topics

• Biomedical Language Models
• Linguistic Uncertainty
• Activation Steering
• Clinical Text Generation
• Model Representations
• Epistemic Stance

Best for: AI Scientist, NLP Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.