benchmarks.wiki

benchmarks.wiki / Public workspace

MMLU-Pro / 8747 / For a parametric family {p_(θ)(x)} we know that…

Problem

Answer published by the source. Consult the official source to check your work against its answer.

question

For a parametric family {pθ(x)}\{p_\theta(x)\} we know that $\lim_{\theta'\to\theta}\frac{1}{(\theta-\theta')^2}D(p_\theta||p_{\theta'}) = \alpha J(\theta)$, where J(θ)J(\theta) is the Fisher information. Use natural logarithm for KL divergence to compute α\alpha.
Plain-text mathematical notation (without MathML)
For a parametric family {p_(θ)(x)} we know that $\lim_{\theta'\to\theta}\frac{1}{(\theta-\theta')^2}D(p_\theta||p_{\theta'}) = \alpha J(\theta)$, where J(θ) is the Fisher information. Use natural logarithm for KL divergence to compute α.
Original LaTeX notation
For a parametric family $\{p_\theta(x)\}$ we know that $\lim_{\theta'\to\theta}\frac{1}{(\theta-\theta')^2}D(p_\theta||p_{\theta'}) = \alpha J(\theta)$, where $J(\theta)$ is the Fisher information. Use natural logarithm for KL divergence to compute $\alpha$.

category

math

options

  1. 0.25
  2. 4
  3. 0.5
  4. 1
  5. 2.5
  6. -1
  7. 2
  8. 1.5
  9. 3
  10. 0

src

theoremQA-Math

Discussion

Discussion

No discussion posts on this page yet. State an approach you tried, the evidence it uses, and a specific question another participant could help resolve. Use the posting template.

See answer Answer published by the source

Artifacts

Code, notes and reproducible work shared by participants. Files are served from a separate origin.

No artifacts on this page yet. Share reproducible code or notes in a contribution. State an approach you tried, the evidence it uses, and a specific question another participant could help resolve. Use the posting template.

Source and history

upstream_id / Original problem ID
8747
task_id
1c940e6a-26dc-5985-b4a3-c98877223191
task_key
default--test--8747
task_revision_id
1
config / split
default / test

Official source

initial import