Improving the Completeness and Comparability of Segment Disclosures: A Large Language Model Approach
This study develops a large language model-based framework to extract segment disclosures directly from Form 10-K filings, preserving both reportable and nested segment information. A retrieval augmented system is designed to incorporate information across multiple filings to support comparability. The framework is demonstrated in longitudinal analysis within firms and cross-firm alignment of geographic segments, showing accurate extraction and effective handling of cross-period queries.
Article intelligence
Key points
- Segment disclosures are central to financial reporting but face completeness and comparability issues due to dispersed formats in 10-K filings.
- Proposes an LLM-based framework to extract segment info, including nested segments, directly from 10-Ks.
- Designs a retrieval augmented system for cross-filing comparability.
- Validated in two settings: within-firm longitudinal analysis and cross-firm geographic segment alignment.
Why it matters
This matters because segment disclosures are central to financial reporting but face completeness and comparability issues due to dispersed formats in 10-K filings.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.23924] Improving the Completeness and Comparability of Segment Disclosures: A Large Language Model Approach
[Submitted on 20 Apr 2026]
Title:Improving the Completeness and Comparability of Segment Disclosures: A Large Language Model Approach
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Abstract:Segment-level disclosures are a central component of financial reporting, providing insight into firms' internal organization and the allocation of economic activities across operating units. However, segment information is often presented in both qualitative and quantitative forms, dispersed across tables and narrative sections of Form 10-K filings. Empirical research relying on structured databases faces both completeness and comparability challenges, as some firm-year observations may be missing, nested segment disclosures are not captured, and support for longitudinal and cross-firm comparability is limited. This study develops a large language model-based framework to extract segment disclosures directly from Form 10-K filings and to preserve both reportable and nested segment information. We further design a retrieval augmented system that incorporates information across multiple filings to support comparability. We use two representative settings to demonstrate its application: longitudinal analysis within a firm to interpret segment changes over time, and cross firm alignment of geographic segments across firms with different reporting structures. The results indicate that the artifact accurately extracts segment-level information and effectively addresses questions that require cross-period knowledge, demonstrating the potential of LLM-based approaches to enhance the measurement and interpretation of segment disclosures.
Comments: 39 pages, 4 figures, submitted to Accounting Horizons
Subjects:
Computation and Language (cs.CL); Information Retrieval (cs.IR); General Finance (q-fin.GN)
Cite as: arXiv:2605.23924 [cs.CL]
(or arXiv:2605.23924v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.23924
arXiv-issued DOI via DataCite
Submission history
From: Zhiyuan Cheng [view email] [v1] Mon, 20 Apr 2026 03:04:08 UTC (462 KB)
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