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arxiv: 2601.02627 · v2 · submitted 2026-01-06 · 💻 cs.CL · cs.AI

Improved Evidence Extraction and Metrics for Document Inconsistency Detection with LLMs

Nelvin Tan , Yaowen Zhang , James Asikin Cheung , Fusheng Liu , Yu-Ching Shih , Dong Yang This is my paper

Pith reviewed 2026-05-16 18:05 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords evidence extractiondocument inconsistency detectionlarge language modelsprompting methodsredact-and-retryconstrained filteringsemi-synthetic dataset
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The pith

A redact-and-retry framework with constrained filtering improves LLM evidence extraction for detecting document inconsistencies.

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

The paper investigates how large language models can be used to extract evidence when checking documents for inconsistencies. It proposes new metrics to evaluate the quality of that evidence extraction and introduces a redact-and-retry prompting method with constrained filtering. Experiments show this approach outperforms standard prompting techniques. The work also includes a new semi-synthetic dataset to test these capabilities. This matters because better evidence extraction could make LLM-based document analysis more reliable in practice.

Core claim

The authors establish that their redact-and-retry framework with constrained filtering substantially improves evidence extraction performance over other prompting methods for document inconsistency detection, supported by new comprehensive evidence-extraction metrics and validated on a released semi-synthetic dataset.

What carries the argument

The redact-and-retry framework with constrained filtering, which iteratively removes inconsistent parts and retries with constraints to refine evidence extraction.

Load-bearing premise

The new evidence-extraction metrics accurately reflect true extraction quality and the gains seen on the semi-synthetic dataset will hold for actual real-world documents.

What would settle it

A direct comparison on a collection of real-world inconsistent documents where human experts rate the extracted evidence quality, checking if the framework still outperforms baselines and if metric scores match the ratings.

Figures

Figures reproduced from arXiv: 2601.02627 by Dong Yang, Fusheng Liu, James Asikin Cheung, Nelvin Tan, Yaowen Zhang, Yu-Ching Shih.

Figure 1
Figure 1. Figure 1: Plots of EHR vs. EPR and EHRC vs. EPRC. Evidence hit and precision [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Algorithm 1 visualized [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Algorithm 1 with LLM filter call visualized. G4o L3.2 L3 RUF(− → +|flip) 0.472 0.380 0 RUF(+ → −|flip) 0.528 0.619 0 RCF(− → +|flip) 0 0 0 RCF(+ → −|flip) 0 0 0 RUF(E kept|E found) 0.861 0.847 0.976 RUF(E disc.|E found) 0.139 0.153 0.024 RCF(E kept|E found) 0.908 0.884 0.988 RCF(E disc.|E found) 0.092 0.116 0.012 [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Boxplot for number of retries for RnR. ‘Pos’ [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Boxplot for number of sentences. ‘pos’ refers to datapoints [PITH_FULL_IMAGE:figures/full_fig_p009_6.png] view at source ↗
read the original abstract

Large language models (LLMs) are becoming useful in many domains due to their impressive abilities that arise from large training datasets and large model sizes. However, research on LLM-based approaches to document inconsistency detection is relatively limited. We address this gap by investigating evidence extraction capabilties of LLMs for document inconsistency detection. To this end, we introduce new comprehensive evidence-extraction metrics and a redact-and-retry framework with constrained filtering that substantially improves evidence extraction performance over other prompting methods. We support our approach with strong experimental results and release a new semi-synthetic dataset for evaluating evidence extraction.

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

3 major / 2 minor

Summary. The paper introduces new evidence-extraction metrics for LLM-based document inconsistency detection and proposes a redact-and-retry framework with constrained filtering. It claims this framework substantially improves evidence extraction performance over standard prompting methods, supported by experimental results on a new semi-synthetic dataset that is released with the work.

Significance. If the central improvements hold under proper validation, the work could provide practical prompting techniques and evaluation tools for inconsistency detection tasks. However, the absence of human correlation studies for the new metrics and reliance on semi-synthetic data limit the assessed significance, as gains may not transfer to real documents.

major comments (3)
  1. [Metrics definition and validation section] The new evidence-extraction metrics are introduced without any reported correlation to human judgments or established extraction measures (e.g., no inter-annotator agreement or comparison to ROUGE/BLEU variants on held-out annotations). This directly undermines the claim that reported improvements reflect genuine extraction quality rather than metric artifacts.
  2. [Dataset and experimental setup] The semi-synthetic dataset construction is not shown to approximate real-world inconsistency distributions; no ablation or transfer experiments on authentic documents are reported. This makes the generalization claim in the abstract load-bearing but unsupported.
  3. [Experiments and results] Experimental results lack statistical details such as variance across runs, significance tests, or full baseline comparisons (including parameter counts and prompt lengths). The abstract's assertion of 'strong experimental results' cannot be assessed without these.
minor comments (2)
  1. [Framework description] Notation for the constrained filtering step is introduced without a formal definition or pseudocode, making the framework hard to reproduce from the text alone.
  2. [Abstract and results] The abstract claims 'substantial improvements' but the results section should include effect sizes or relative gains with confidence intervals for clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major point below and describe the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Metrics definition and validation section] The new evidence-extraction metrics are introduced without any reported correlation to human judgments or established extraction measures (e.g., no inter-annotator agreement or comparison to ROUGE/BLEU variants on held-out annotations). This directly undermines the claim that reported improvements reflect genuine extraction quality rather than metric artifacts.

    Authors: We agree that direct validation against human judgments would strengthen the metrics. In the revised manuscript we will add a human annotation study on a held-out subset of the dataset, report inter-annotator agreement, and compute correlations (Pearson and Spearman) between our metrics and human scores. We will also include comparisons against ROUGE and BLEU variants. These results will be presented in an expanded Metrics section. revision: yes

  2. Referee: [Dataset and experimental setup] The semi-synthetic dataset construction is not shown to approximate real-world inconsistency distributions; no ablation or transfer experiments on authentic documents are reported. This makes the generalization claim in the abstract load-bearing but unsupported.

    Authors: We acknowledge that explicit validation against real-world distributions is desirable. In revision we will expand the dataset section with a detailed mapping of our synthetic inconsistency types to patterns documented in prior literature on real documents. We will also add a small-scale transfer experiment on publicly available authentic documents where feasible and will moderate the generalization language in the abstract. revision: partial

  3. Referee: [Experiments and results] Experimental results lack statistical details such as variance across runs, significance tests, or full baseline comparisons (including parameter counts and prompt lengths). The abstract's assertion of 'strong experimental results' cannot be assessed without these.

    Authors: We agree that additional statistical rigor is needed. The revised version will report standard deviations across five independent runs, include statistical significance tests (paired t-tests and Wilcoxon signed-rank), and provide complete baseline specifications including model parameter counts and prompt token lengths. These details will appear in the Experiments and Results section. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical prompting study with no derivations or fitted predictions

full rationale

The paper presents an empirical investigation of LLM prompting strategies for evidence extraction in document inconsistency detection. It introduces new metrics and a redact-and-retry framework, evaluates them on a semi-synthetic dataset, and reports experimental improvements. No equations, mathematical derivations, or parameter-fitting steps are described that could reduce a claimed prediction to its own inputs by construction. The work contains no self-citation chains used to justify uniqueness theorems or ansatzes, and the central claims rest on experimental results rather than definitional equivalence. This is a standard empirical contribution whose validity can be assessed against external benchmarks or human judgments without internal circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No mathematical axioms, free parameters, or invented entities; the contribution is empirical with new metrics and a prompting framework.

pith-pipeline@v0.9.0 · 5403 in / 936 out tokens · 34376 ms · 2026-05-16T18:05:32.404032+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Language Bias under Conflicting Information in Multilingual LLMs

    cs.CL 2026-04 unverdicted novelty 6.0

    Multilingual LLMs ignore conflicts in information presented across languages and exhibit consistent biases favoring certain languages like Chinese over Russian.

Reference graph

Works this paper leans on

13 extracted references · 13 canonical work pages · cited by 1 Pith paper · 2 internal anchors

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    Example: ‘Zully donated her kidney.’ vs

    Negation.There exists one sentence which is a negation of another sentence. Example: ‘Zully donated her kidney.’ vs. ‘Zully never donated her kidney.’

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    Example: ‘All the donors are between 20 to 45 years old.’ vs

    Numeric.There exists a numerical mismatch between sentences. Example: ‘All the donors are between 20 to 45 years old.’ vs. ‘Lisa, who donates her kidney, she is 70 years old.’

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    Example: ‘Zully Broussard donated her kidney to a stranger.’ vs

    Content.There exists one sentence chang- ing one or multiple attributes of an event or entity that was previously stated in another sentence. Example: ‘Zully Broussard donated her kidney to a stranger.’ vs. ‘Zully Broussard donated her kidney to her close friend.’

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    Ex- ample: ‘The rescue team searched for the boy worriedly.’ vs

    Emotion/Mood/Feeling.Inconsistency in emotion/mood/feeling between sentences. Ex- ample: ‘The rescue team searched for the boy worriedly.’ vs. ‘The rescue team searched for the boy happily.’

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    Example: ‘Jane and Tom are a married couple.’ vs

    Relation.Presence of two mutually exclusive relations between entities. Example: ‘Jane and Tom are a married couple.’ vs. ‘Jane is Tom’s sister.’ 8 Figure 6: Boxplot for number of sentences. ‘pos’ refers to datapointsi such that yi =Yes , ‘neg’ refers to datapoints isuch thaty i =No, and ‘all’ refers to all datapoints

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    Example: ‘The road T51 was located in New York.’ vs

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