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arxiv: 2604.10495 · v2 · pith:RXZFZ5N3new · submitted 2026-04-12 · 💻 cs.CL

Why Don't You Know? Evaluating the Impact of Uncertainty Sources on Uncertainty Quantification in LLMs

Maiya Goloburda , Roman Vashurin , Fedor Chernogorskii , Nurkhan Laiyk , Daniil Orel , Preslav Nakov , Maxim Panov This is my paper

Pith reviewed 2026-05-10 15:48 UTC · model grok-4.3

classification 💻 cs.CL
keywords uncertainty quantificationlarge language modelsuncertainty sourcesLLM evaluationdatasetmodel confidence
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The pith

Uncertainty quantification methods for LLMs work only when uncertainty comes from knowledge gaps.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models encounter uncertainty from multiple distinct sources such as missing knowledge, ambiguous inputs, and variable outputs, each carrying different implications for users and systems. Most existing UQ methods produce a single confidence score without identifying the source. This paper creates a dataset that tags examples by their primary uncertainty source and tests how standard UQ techniques behave when each source is isolated. The experiments demonstrate reliable performance solely in the knowledge-gap setting and clear degradation or misleading results in the other cases, showing that source-blind methods fall short for dependable deployment.

Core claim

The central claim is that while many UQ methods perform well when uncertainty stems solely from model knowledge limitations, their performance degrades or becomes misleading when other sources such as input ambiguity or output variability are introduced instead.

What carries the argument

A new dataset that explicitly categorizes uncertainty instances by source to enable separate, controlled evaluation of UQ method behavior under each source.

If this is right

  • UQ methods must be adjusted or extended to account for uncertainty source if they are to remain effective outside narrow knowledge-gap conditions.
  • Benchmarks and evaluation protocols for UQ should separately measure performance under each uncertainty source rather than on mixed data.
  • Reliable LLM applications in practice will require systems that report both a confidence value and an identified uncertainty source.
  • Future method development should prioritize source-aware designs over single-score approaches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • In deployed systems, distinguishing ambiguity from ignorance could allow models to ask clarifying questions instead of guessing with low confidence.
  • Data collection and fine-tuning pipelines could target specific uncertainty sources to reduce their prevalence in high-stakes domains.
  • Hybrid UQ pipelines might combine existing scores with lightweight classifiers that predict the uncertainty source from the input and output.

Load-bearing premise

The introduced dataset cleanly separates uncertainty sources without significant label overlap or confounding factors that would prevent isolating each source's effect on UQ performance.

What would settle it

A test showing that UQ methods maintain high accuracy and calibration across all three uncertainty source categories on the new dataset or an equivalent controlled collection would contradict the main result.

Figures

Figures reproduced from arXiv: 2604.10495 by Daniil Orel, Fedor Chernogorskii, Maiya Goloburda, Maxim Panov, Nurkhan Laiyk, Preslav Nakov, Roman Vashurin.

Figure 1
Figure 1. Figure 1: Illustration of model responses and down [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Question generation pipeline used to construct question triplets. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Prediction–Rejection Ratio (PRR) curve [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Comparison of uncertainty estimation families by PRR. For each model and evaluation type, bars show [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Spearman correlation coefficient for best performing UQ methods in each category across models. [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Examples of UQ failure modes across question types. Type 1: consistent, high-probability answers are [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Agreement between human annotations and LLM-as-Judge across question types. C Model Details Answer the following question in ONE sentence. Answer as plain text on a single line. No line breaks or markdown. Provide only the answer - no introductions, explanations, or extra text. No words like "here is", "breakdown", "let’s", "fascinating ". Keep the answer short. Question: {text} Listing 10: Prompt used to … view at source ↗
Figure 8
Figure 8. Figure 8: Distributions of generation lengths across [PITH_FULL_IMAGE:figures/full_fig_p018_8.png] view at source ↗
read the original abstract

As Large Language Models (LLMs) are increasingly deployed in real-world applications, reliable uncertainty quantification (UQ) becomes critical for safe and effective use. Most existing UQ approaches for language models aim to produce a single confidence score -- for example, estimating the probability that a model's answer is correct. However, uncertainty in natural language tasks arises from multiple distinct sources, including model knowledge gaps, output variability, and input ambiguity, which have different implications for system behavior and user interaction. In this work, we study how the source of uncertainty impacts the behavior and effectiveness of existing UQ methods. To enable controlled analysis, we introduce a new dataset that explicitly categorizes uncertainty sources, allowing systematic evaluation of UQ performance under each condition. Our experiments reveal that while many UQ methods perform well when uncertainty stems solely from model knowledge limitations, their performance degrades or becomes misleading when other sources are introduced. These findings highlight the need for uncertainty-aware methods that explicitly account for the source of uncertainty in large language models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper claims that uncertainty in LLMs arises from distinct sources including model knowledge gaps, output variability, and input ambiguity, each with different implications. Existing UQ methods that produce a single confidence score perform adequately when uncertainty is due solely to knowledge limitations but degrade or become misleading when other sources are present. To support this, the authors introduce a new dataset that explicitly categorizes uncertainty sources for controlled evaluation and report experimental results showing the need for source-aware UQ methods.

Significance. If the central empirical findings hold after validation, the work is significant for highlighting a key limitation in current UQ techniques for LLMs and for providing a categorized dataset that enables systematic study of source-specific effects. This has direct relevance to safe real-world deployment where distinguishing uncertainty types matters for user interaction and system behavior. The evaluation of multiple existing methods against the new dataset is a constructive contribution.

major comments (2)
  1. [Dataset Construction] Dataset Construction section: The manuscript states that the dataset 'explicitly categorizes uncertainty sources' to enable 'controlled analysis,' but reports no validation metrics such as inter-annotator agreement, label-feature correlations with answer correctness, or ablation on label purity. This is load-bearing for the central claim, as performance degradation for non-knowledge sources could arise from uneven example difficulty or annotation artifacts rather than the isolated source itself.
  2. [Experiments] Experiments section: The abstract and summary report performance differences across sources without visible details on data splits, statistical significance tests, or controls for confounders, making it unclear whether the observed degradation is robust or influenced by post-hoc selection.
minor comments (1)
  1. [Abstract] The abstract would benefit from naming the specific UQ methods and evaluation metrics used, to allow readers to immediately assess the scope of the comparison.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments highlight important aspects of dataset validation and experimental rigor that will strengthen the manuscript. We address each major comment below and commit to the necessary revisions.

read point-by-point responses

  1. Referee: [Dataset Construction] Dataset Construction section: The manuscript states that the dataset 'explicitly categorizes uncertainty sources' to enable 'controlled analysis,' but reports no validation metrics such as inter-annotator agreement, label-feature correlations with answer correctness, or ablation on label purity. This is load-bearing for the central claim, as performance degradation for non-knowledge sources could arise from uneven example difficulty or annotation artifacts rather than the isolated source itself.

    Authors: We agree that quantitative validation metrics are essential to support the dataset's reliability and to isolate the effects of uncertainty sources. The initial submission described the categorization process but omitted these metrics. In the revised manuscript, we will add inter-annotator agreement scores (Cohen's kappa), correlations between source labels and answer correctness, and an ablation on label purity to demonstrate that performance differences arise from the intended sources rather than artifacts or difficulty imbalances. revision: yes

  2. Referee: [Experiments] Experiments section: The abstract and summary report performance differences across sources without visible details on data splits, statistical significance tests, or controls for confounders, making it unclear whether the observed degradation is robust or influenced by post-hoc selection.

    Authors: We thank the referee for this observation. While the full Experiments section provides the data split methodology and reports results across multiple runs, we acknowledge that statistical significance testing and explicit confounder controls were not sufficiently highlighted. We will revise the section to include p-values for performance differences, clearer documentation of train/validation/test splits, and additional controls (e.g., matching for input length and lexical features) to confirm robustness. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical dataset evaluation with no self-referential derivations

full rationale

The paper introduces an external dataset that categorizes uncertainty sources and reports experimental performance of existing UQ methods under each condition. No equations, fitted parameters, or derivations are present that reduce any reported result to a quantity defined by the authors' own inputs or self-citations. The central claims rest on direct measurements against the new dataset rather than any closed loop of self-definition or prediction-by-construction. This is a standard empirical study whose findings are falsifiable via replication on the released data.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical evaluation paper; no free parameters, axioms, or invented entities are required by the central claim beyond standard assumptions of supervised dataset construction and LLM inference.

pith-pipeline@v0.9.0 · 5501 in / 1029 out tokens · 28243 ms · 2026-05-10T15:48:10.381856+00:00 · methodology

discussion (0)

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