When Context Grows, So Does the Challenge: Human Oversight in LLM Evaluation of Financial Tables

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This is a Quantitative Study study.

Arijit Sehanobish; Sheila Anderson; G Michel; Mike Arov

doi:10.1145/3746058.3758457

September 27, 2025Open Access

When Context Grows, So Does the Challenge: Human Oversight in LLM Evaluation of Financial Tables

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Key Points

Performance of large language models declines significantly as table size increases, highlighting a crucial limitation.
Our findings indicate that as financial tables grow, the ability of models to accurately judge them diminishes.
The introduction of a triggered oversight framework aims to improve decisions on ambiguous cases by leveraging human expertise.
Addressing the challenges of evaluating large financial tables can help refine model reliability and enhance interpretability.

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Implication

Analysis reveals reduced effectiveness of large language models for financial table evaluation, suggesting human oversight may enhance accuracy.

Abstract

We evaluate the reliability of Large Language Models (LLMs)-as-a-Judge for comparing financial tables derived from text-to-SQL pipelines. Our findings show degraded performance on larger tables. To address this, we propose a triggered oversight framework that defers ambiguous cases to human experts.

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Cite This Study

Sehanobish et al. (2025) studied this question.

synapsesocial.com/papers/68d90a0f41e1c178a14f6a03 https://doi.org/10.1145/3746058.3758457

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When Context Grows, So Does the Challenge: Human Oversight in LLM Evaluation of Financial Tables

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

When Context Grows, So Does the Challenge: Human Oversight in LLM Evaluation of Financial Tables

Key Points

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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