U-Space: Uncovering When and Why Uncertainty Arises in Language Models

Key points

• Yet recognizing when to defer remains difficult because language models can present incorrect conclusions with fluent explanations and an authoritative tone.1
• Uncertainty quantification seeks to address this disconnect by estimating the reliability of individual predictions.1
• Building on this capability, we introduce the U-Space, a low-dimensional subspace that makes a model's evolving uncertainty measurable and interpretable.1
• We identify semantic anchors for doubt and certainty, map their unembedding directions back into the residual space, and combine their contrasts into an orthogonal basis.1

Sources (2)

• [1]U-Space: Uncovering When and Why Uncertainty Arises in Language Models
  Hugging Face Daily Papers · Oct 6, 12:00 AM

  Large language models are informing decisions with ever-higher stakes.

  Yet recognizing when to defer remains difficult because language models can present incorrect conclusions with fluent explanations and an authoritative tone.

• [2]U-Space: Uncovering When and Why Uncertainty Arises in Language Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 08:40 PM · same content

Extractive summary: sentences quoted from the sources.