U-Space: Uncovering When and Why Uncertainty Arises in Language Models [R][D]
If you build on open-weight reasoning models, you have probably wanted a "how sure is the model about this answer?" signal that does not cost 5x sampling, a judge model, or a labeled dataset.
Proof1 community thread
Key points
- score = (how strongly the hidden state points toward different "doubt" directions) x (mean token entropy over the reasoning tokens)
- Label-free: No correctness labels; no training and no no fine-tuning.
- It tells you what kind of uncertainty: (i) ambiguity, (ii) missing information, (iii) conflicting evidence, or (iv) general uncertainty. (not necessary for many applications but potentially useful)
- Fit one with Anthropic's reference code (anthropics/jacobian-lens, jlens.fit): about 100 unlabeled prompts, no parameters trained, cost dominated by backward passes.
Sources (1)
- [1]U-Space: Uncovering When and Why Uncertainty Arises in Language Models [R][D]r/MachineLearning (top, daily) · Oct 12, 03:10 PM
If you build on open-weight reasoning models, you have probably wanted a "how sure is the model about this answer?" signal that does not cost 5x sampling, a judge model, or a labeled dataset.
score = (how strongly the hidden state points toward different "doubt" directions) x (mean token entropy over the reasoning tokens)
Extractive summary: sentences quoted from the sources.
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