A Formal Framework for Uncertainty Analysis of Text Generation with Large Language Models

A Formal Framework for Uncertainty Analysis of Text Generation with Large Language Models | Signal Canvas | ScienceToStartup

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Proof freshness: stale
Proof status: unverified
Display score: 4/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 7
Source count: 3
Coverage: 50%

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Evidence Receipt

Route status: building

Claims: 8

References: 7

Proof: Verification pending

Freshness state: computing

Source paper: A Formal Framework for Uncertainty Analysis of Text Generation with Large Language Models

PDF: https://arxiv.org/pdf/2603.26363v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T21:58:29.094Z

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Not build-ready: A Formal Framework for Uncertainty Analysis of Text Generation with Large Language Models

/buildability/a-formal-framework-for-uncertainty-analysis-of-text-generation-with-large-language-models

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Subject: A Formal Framework for Uncertainty Analysis of Text Generation with Large Language Models

Verdict

Ignore

Verdict is Ignore because current viability and proof state do not clear the buildability gate.

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Claim map

Strong 8Mixed 0Weak 0

Evidencepartial
We introduce filters and objective functions to describe how different aspects of uncertainty can be expressed over the sampling tree and demonstrate how to express existing approaches towards uncertainty through these functions.
Implicationpartial
The abstract details the components of the framework, including filters and objective functions for uncertainty expression.
Verificationpartial
partial
Evidencepartial
With our framework we show not only how different methods are formally related and can be reduced to a common core, but also point out additional aspects of uncertainty that have not yet been studied.
Implicationpartial
The abstract states that the framework shows how different methods are related and can be reduced to a common core, indicating a result of the framework's application.
Verificationpartial
partial
Evidencepartial
With our framework we show not only how different methods are formally related and can be reduced to a common core, but also point out additional aspects of uncertainty that have not yet been studied.
Implicationpartial
The abstract explicitly mentions that the framework points out new, unstudied aspects of uncertainty.
Verificationpartial
partial
Evidencepartial
Within the sampling tree, each edge is annotated with the probability to extend the direct prefix to the sequence represented by the target node, e.g., the edge betweenv1:i−1andv i is labeled withp(v i|v1:i−1).
Implicationpartial
This is a specific technical detail about the sampling tree notation, directly supported by the text.
Verificationpartial
partial
Evidencepartial
To enable this, we define a filter function to specify to which token sequences this objective is applied as f:V→N 0 (4) mapping to the natural numbers including zero.
Implicationpartial
The abstract and parsed text describe the purpose and definition of the filter function for working with subtrees.
Verificationpartial
partial
Evidencepartial
The generation of texts using Large Language Models (LLMs) is inherently uncertain, with sources of uncertainty being not only the generation of texts, but also the prompt used and the downstream interpretation.
Implicationpartial
This is a core statement directly from the abstract, setting the premise for the entire paper.
Verificationpartial
partial
Evidencepartial
Within this work, we provide a formal framework for the measurement of uncertainty that takes these different aspects into account.
Implicationpartial
The abstract explicitly states the contribution of the paper as providing a formal framework for uncertainty measurement.
Verificationpartial
partial
Evidencepartial
Our framework models prompting, generation, and interpretation as interconnected autoregressive processes that can be combined into a single sampling tree.
Implicationpartial
The abstract clearly describes the modeling approach used in the proposed framework.
Verificationpartial
partial

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A Formal Framework for Uncertainty Analysis of Text Generation with Large Language Models

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