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arxiv: 2606.32029 · v1 · pith:NHDWCWBZnew · submitted 2026-06-30 · 💻 cs.CL · cs.AI

When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors

Yuqing Yang , Qi Zhu , Zhen Han , Boran Han , Zhengyuan Shen , Shuai Wang , Vassilis N. Ioannidis , Huzefa Rangwala This is my paper

Pith reviewed 2026-07-01 05:24 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords data referencing errorstable reasoningLLM evaluationerror detectioncritic modelsrejection samplingtabular data processing
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The pith

LLMs make data referencing errors on tables across all sizes, but a trained critic detects them and improves accuracy up to 12%.

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

Large language models struggle with correctly referencing specific data points from tables during reasoning, even when they understand the table layout. This paper conducts the first large-scale study of these data referencing errors across multiple model sizes and tasks. It finds that such errors are widespread and shows that a critic focused on data referencing can filter or reject faulty reasoning paths to raise final answer accuracy by as much as 12 percent. The work also produces a compact 4 billion parameter critic model that identifies these errors reliably on both familiar and new data.

Core claim

Data referencing errors occur in every tested LLM from 1.7B to 20B parameters; training a critic to spot incorrect table value citations or omissions allows critic-based filtering and rejection sampling to raise answer accuracy by up to 12 percent, and a 4B-parameter critic reaches 78.2 percent average F1 on in- and out-of-distribution cases while aiding larger models at inference time.

What carries the argument

The data referencing critic, a model that evaluates whether reasoning steps correctly cite or omit table values.

If this is right

  • DREs appear in all models tested, independent of parameter count.
  • Critic-based filtering and rejection sampling raise accuracy by as much as 12%.
  • A 4B critic achieves 78.2% F1 on both in-distribution and out-of-distribution DREs.
  • The critic assists inference for larger models.
  • The approach works through detection of specific referencing mistakes rather than general reasoning critique.

Where Pith is reading between the lines

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

  • Similar critics could be trained for other structured inputs such as code or knowledge graphs.
  • Lightweight critics might enable cheaper auditing of large model outputs in production.
  • Error-specific critics could be combined to cover multiple failure modes in LLM reasoning.
  • Future work might test whether the same critic training data works across different base models without retraining.

Load-bearing premise

The defined data referencing errors can be isolated and measured independently from other reasoning mistakes in the evaluation tasks.

What would settle it

A controlled test where models are forced to use only correct table references and accuracy does not improve beyond the critic method, or where the critic fails to improve accuracy on a new table task with different error patterns.

Figures

Figures reproduced from arXiv: 2606.32029 by Boran Han, Huzefa Rangwala, Qi Zhu, Shuai Wang, Vassilis N. Ioannidis, Yuqing Yang, Zhengyuan Shen, Zhen Han.

Figure 1
Figure 1. Figure 1: Illustration of Tabular DREs. 2025), yet they still commit surprisingly basic mis￾takes even when the table format is correctly parsed, by referencing table content incorrectly, as illus￾trated in [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: F1 scores across different model-dataset pairs for critic evaluation. randomly sample 400 response segment with a bal￾anced number of positive and negative examples, resulting in a total of 3,600 samples. We report the standard F1 score for this binary classification task, which balances precision (how often predicted DREs are correct) and recall (how many true DREs are identified). Results We compare the … view at source ↗
Figure 3
Figure 3. Figure 3: Judge prompt for Sonnet-3.7+gt. Both “Failed Copied Values Consistency Check” and “Failed Omission [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: This is an example of Qwen3-8B on the WTQ test set. The table contains 20 rows in total, but Qwen3-8B [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: This is an example of Qwen3-8B on the SciTab test set. Qwen3-8B misquoted 0.714 as 0.704, but this [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Critic Prompt for small-scale LLMs. We do not provide ground-truth answers in this prompt. [PITH_FULL_IMAGE:figures/full_fig_p017_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: An input example for the judge prompt. In this case, the generation model correctly cites the “Silver” [PITH_FULL_IMAGE:figures/full_fig_p018_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The judge output of Sonnet-3.7 without and with ground truth. Without a ground-truth answer, Sonnet-3.7 [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
read the original abstract

While large language models (LLMs) perform well on table tasks, they still make data referencing errors (DREs), i.e., incorrectly citing or omitting table values, despite understanding the table structure. Beyond final-answer accuracy, DREs directly compromise the correctness and reliability of intermediate reasoning steps. Yet prior studies have only offered limited, small-scale analyses. In this work, we present the first systematic evaluation of tabular data referencing errors across different models and tasks. Our results show that DREs occur across all tested models (1.7B to 20B parameters). Furthermore, we demonstrate that incorporating data referencing as a critic significantly improves answer accuracy up to 12.0%, through critic-based filtering and rejection sampling. Finally, we trained a lightweight 4B-parameter critic model that achieves an average F1 score of 78.2% in detecting both in-distribution and out-of-distribution DREs, and effectively assists inference for larger 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

0 major / 3 minor

Summary. The paper introduces data referencing errors (DREs) as a distinct failure mode in LLMs on table tasks, where models incorrectly cite or omit specific table values despite grasping structure. It reports the first systematic evaluation showing DREs occur across models from 1.7B to 20B parameters, demonstrates that a data-referencing critic improves final answer accuracy by up to 12% via filtering and rejection sampling, and presents a trained 4B-parameter critic achieving 78.2% average F1 on both in-distribution and out-of-distribution DRE detection while aiding inference on larger models.

Significance. If the isolation of DREs as a measurable category and the critic's effectiveness hold under rigorous evaluation, the work provides a targeted, practical approach to improving LLM reliability on structured data beyond aggregate accuracy metrics. The scale of the evaluation across model sizes and the lightweight critic are useful contributions for deployment. The empirical focus on a specific error type complements broader reasoning studies.

minor comments (3)
  1. The abstract and introduction would benefit from an explicit definition and annotation protocol for DREs (including examples of what counts as a referencing error versus other reasoning failures) to allow readers to assess the claimed separation from general errors.
  2. Provide details on the datasets, task formulations, and statistical significance tests used for the 12.0% accuracy improvement and the 78.2% F1 scores, including baseline comparisons and inter-annotator agreement if human labels are involved.
  3. Clarify how out-of-distribution DREs are constructed and whether the critic training data distribution matches the error patterns observed in the main evaluation.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive summary, recognition of the practical value of isolating DREs, and the recommendation for minor revision. No major comments requiring point-by-point rebuttal were provided in the report.

Circularity Check

0 steps flagged

No significant circularity; empirical measurement study

full rationale

This is an empirical evaluation and model-training paper with no mathematical derivation chain, equations, or first-principles predictions. Claims rest on direct measurements of DREs across models, critic-based filtering experiments, and separate training/evaluation of a 4B critic model on in- and out-of-distribution cases. No steps reduce by construction to fitted inputs, self-definitions, or self-citation chains; results are externally falsifiable via the described annotation and accuracy metrics.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 1 invented entities

Based solely on the abstract, the paper introduces the DRE category as a new error type but lists no free parameters, mathematical axioms, or invented physical entities. All claims rest on empirical measurement and model training.

invented entities (1)
  • Data Referencing Errors (DREs) no independent evidence
    purpose: To name and isolate the specific failure mode of incorrectly citing or omitting table values in LLM outputs
    Defined in the abstract as the central phenomenon under study; no independent evidence outside the paper is provided

pith-pipeline@v0.9.1-grok · 5720 in / 1353 out tokens · 41121 ms · 2026-07-01T05:24:04.497912+00:00 · methodology

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Reference graph

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