---
title: "ARQA — paper by Wang and Balloccu (2026)"
description: "Existing QA benchmarks usually test only one ability, such as table arithmetic, hybrid retrieval, or text-only citation. 24 references."
authors: ["Ruilong Wang", "Simone Balloccu"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)"
source: "https://aclanthology.org/2026.eacl-industry.63/"
doi: "10.18653/v1/2026.eacl-industry.63"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/wang-2026-arqa-benchmark-grounded-table-text/"
---
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# ARQA: A Benchmark for Grounded Table–Text QA in Enterprise Annual Reports

**Authors:** Ruilong Wang and [Simone Balloccu](https://md.research.elamur.ai/free/en/authors/simone-balloccu/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-industry.63/) · [PDF](https://aclanthology.org/2026.eacl-industry.63.pdf) · [DOI 10.18653/v1/2026.eacl-industry.63](https://doi.org/10.18653/v1/2026.eacl-industry.63) · [OpenAlex](https://openalex.org/W7140107318)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Enterprise annual reports pair audited KPI tables with narrative explaining why figures changed. Existing QA benchmarks usually test only one ability, such as table arithmetic, hybrid retrieval, or text-only citation. ARQA (Annual Report QA) combines numeric recomputation, joint table–text grounding, and causal explanation in a single benchmark. It contains about 2.5K QA pairs built from ten years of Volkswagen Group annual reports. Each item rests on a "pack", meaning one table plus its related paragraphs, and is annotated with cell- and span-level evidence. Items fall into three families: LOOKUP, ARITHMETIC, and INSIGHT. GPT-4o planned, generated, and self-verified the items. Deterministic checks then filtered them, and domain experts audited the results. The authors tested five instruction-tuned LLMs. The models retrieved facts well but did worse on recomputation, on cross-modal causal explanation, and especially on citing exact text spans. Better prompting helped with procedure but left these gaps open. ARQA therefore works as a diagnostic testbed for auditable, evidence-first reasoning over corporate documents.

## Key points
- **Data source:** Volkswagen Group annual reports for fiscal years 2015–2024, covering the Production and Management domains. GPT-4o pre-filtered candidate paragraphs (relatedness score ≥ 0.5, pages p±1).
- **Pipeline (plan → generate → verify → audit):** GPT-4o was guided by expert-curated focus points. Deterministic checks covered schema validity, cell bounds and values, and exact character spans. ARITHMETIC programs were re-executed at 28-digit precision, with a tolerance of ±1 ULP or a relative error of 10⁻⁶.
- **Expert audit:** 35 internal automotive experts each reviewed up to 20 packs. They rated ARITHMETIC and INSIGHT items for Grounded Correctness (pass/fail) and Insight Quality (1–3). Agreement was measured with percent agreement and Gwet's AC1.
- **Models and setups:** Llama-3.1 8B Instruct, Llama-3.3 70B Instruct, Qwen-2.5 32B Instruct, DeepSeek-R1-Distill-Qwen 32B, and GPT-4o were each run under two setups:
  - S1: single-pass inference with a schema-explicit prompt.
  - S2: type-aware prompts routed by question family.
  - Both setups were also tested with Few-shot + CoT prompting.
- **Findings:**
  - GPT-4o performed best overall, and Qwen-2.5 32B was the strongest open-weight model.
  - S2 raised cell-evidence F1 but lowered value–unit exact match and INSIGHT claim F1.
  - Span evidence F1 stayed very low for every model.
  - The benchmark and toolkit are released on GitHub (RuilongWang/ARQA-Benchmark).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Ruilong Wang and Simone Balloccu. 2026. ARQA: A Benchmark for Grounded Table–Text QA in Enterprise Annual Reports. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{wang2026arqa,
  title = {{ARQA: A Benchmark for Grounded Table–Text QA in Enterprise Annual Reports}},
  author = {Ruilong Wang and Simone Balloccu},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-industry.63},
  url = {https://doi.org/10.18653/v1/2026.eacl-industry.63}
}
```

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*Updated 1 November 2026*