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Source note3 min read

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

Source

arXiv

arxiv.org/abs/2606.32029v1

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Plain-Language Brief

The research evaluates the issue of data referencing errors (DREs) made by large language models (LLMs) while performing table tasks. The study presents a systematic evaluation of these errors across different models and tasks, showing that all tested models have DREs. The authors propose incorporating a data referencing critic which significantly improves answer accuracy and trains a lightweight model to detect both in-distribution and out-of-distribution DREs.

What the Document Says

  • Large language models (LLMs) make data referencing errors (DREs), i.e., incorrectly citing or omitting table values, despite understanding the table structure.
  • These errors directly compromise the correctness and reliability of intermediate reasoning steps beyond final-answer accuracy.
  • Prior studies have only offered limited, small-scale analyses on DREs.
  • The authors present a systematic evaluation of tabular data referencing errors across different models and tasks.
  • DREs occur across all tested models (1.7B to 20B parameters).
  • Incorporating data referencing as a critic improves answer accuracy up to 12.0% through critic-based filtering and rejection sampling.
  • The authors train 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.

Key Concepts Mentioned

  • large language models
  • data referencing errors
  • table tasks
  • final-answer accuracy
  • intermediate reasoning steps
  • critic model

Claims Made by the Document

  • LLMs make data referencing errors
  • DREs compromise the correctness and reliability of intermediate reasoning steps
  • Prior studies have only offered limited analyses on DREs
  • The authors present a systematic evaluation of tabular data referencing errors
  • DREs occur across all tested models
  • Incorporating data referencing as a critic improves answer accuracy
  • A lightweight 4B-parameter critic model can detect both in-distribution and out-of-distribution DREs

Evidence Used

  • The text states that LLMs make errors while performing table tasks.
  • The text explains that these errors directly impact the correctness and reliability of intermediate reasoning steps.
  • The text mentions that prior studies have only offered limited analyses on DREs.
  • The text describes a systematic evaluation of tabular data referencing errors across different models and tasks.
  • The text states that all tested models have DREs.
  • The text mentions that incorporating data referencing as a critic improves answer accuracy.
  • The text explains that the authors train a lightweight model to detect both in-distribution and out-of-distribution DREs.

Methods / Approach

  • systematic evaluation of tabular data referencing errors
  • incorporating data referencing as a critic
  • critic-based filtering
  • rejection sampling
  • training a lightweight model

Limitations Stated by the Authors

  • The study does not provide specific details about the types or frequencies of DREs observed across the models.

Limitations Noted by the Agent

Agent interpretation — not from the source.

  • The text does not specify how the evaluation was conducted, the number and types of tasks, or the performance metrics used.
  • The text does not mention whether the lightweight critic model was compared to other existing solutions for DREs.

Open Questions

  • What are the specific types and frequencies of DREs observed across the models?
  • How does the lightweight critic model compare to other existing solutions for DREs?

Notes for Future Q&A

May include interpretation. Labeled where it does.

  • The study focuses on the issue of data referencing errors (DREs) in large language models (LLMs) while performing table tasks.
  • The authors present a systematic evaluation of these errors across different models and tasks.

Raw Abstract / Excerpt

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.