huggingface/daily-papers
Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models
Patrik Wolf, Thomas Kleine Buening, Andreas Krause, Celestine Mendler-Dünner
2026-07-15 UTC
1 min read
The user wants a concise HTML summary of the provided news about a research paper. The news is about "Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models" from arXiv. The description is cut off, but I have enough to write a summary.
Key points from the description:
- In-context learning interpreted as conditional inference
- Prompt specifies context, model output estimates conditional distribution
- If interpretation holds, LLM estimates should satisfy probabilistic identities
- Law of total probability: prior-weighted conditional distributions aggregate into population-level marginals over any valid partition
I need to write 2-4 sentences in a
tag, then a link to the original source.
Let me craft the summary:
Sentence 1: The paper examines whether in-context learning in language models satisfies the law of total probability as a form of statistical self-consistency.
Sentence 2: It interprets in-context learning as conditional inference where prompts specify contexts and model outputs estimate conditional distributions.
Sentence 3: The authors propose a partition-prompt-aggregate framework to test if prior-weighted conditional estimates correctly aggregate into population-level marginals across valid partitions.
Sentence 4: This provides a probabilistic criterion for evaluating the calibration and consistency of LLM predictions.
That's 4 sentences. Good.
Now the HTML:
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