GRI - QA : a Comprehensive Benchmark for Table Question Answering over Environmental Data (2025)

Abstract: Assessing corporate environmental sustainability with Table Question Answering systems is challenging due to complex tables, specialized terminology, and the variety of questions they must handle. In this paper, we introduce GRI-QA, a test benchmark designed to evaluate Table QA approaches in the environmental domain. Using GRI standards, we extract and annotate tables from non-financial corporate reports, generating question-answer pairs through a hybrid LLM-human approach. The benchmark includes eight datasets, categorized by the types of operations required, including operations on multiple tables from multiple documents. Our evaluation reveals a significant gap between human and model performance, particularly in multi-step reasoning, highlighting the relevance of the benchmark and the need for further research in domain-specific Table QA. Code and benchmark datasets are available at https://github.com/softlab-unimore/gri_qa.

## introduction sustainability accounting is crucial to global regulatory efforts for corporate environmental transparency. initiatives like the european green deal @xcite and the united nations' sustainability agenda (sdgs, 2024) require publicly listed companies to disclose environmental data through non-financial reports adhering to consolidated standards, such as the global reporting initiative (gri) framework @xcite . these reports provide crucial information, in particular within many and large tables, to determine whether companies are adopting responsible environmental practices or engaging in greenwashing, i.e., overstating their sustainability achievements to appear more environmentally responsible than they truly are @xcite @xcite . however, automatically analyzing non-financial reports introduces many challenges due to (1) the format and structure of the tables, which lack standardization across companies and often feature hierarchical layouts combining top and side headers, (2) specialized terminologies, as environmental tables frequently include industry-specific terms which, combined with performance metrics with varying measurement conventions complicates their interpretation, and (3) the nature and variety of the questions posed by analysts, which can range from simple value extraction to complex calculations involving multiple elements within the same table, across tables within the same document, or even across tables from different documents. these challenges highlight the need for models capable of understanding complex table structures, applying context-aware reasoning, and combining general semantic knowledge with domain expertise. while similar datasets exist in other sectors, as highlighted in section 2, an environmental domainspecific benchmark is still missing and could represent a significant step forward. such a benchmark would be valuable for both domain specialization in table question answering (qa) models and for its technical features, requiring reasoning across varying numbers of tables, an uncommon aspect in other datasets. to address this gap, we introduce gri-qa, a new table qa test benchmark on environmental tables extracted from corporate reports, with questions categorized according to the gri standard. gri-qa was designed through a methodology that requires input from domain experts (section 3) to reflect the specific information needs in the sector. figure 1 shows the question types present in gri-qa and provides an example of question-answer using two tables containing bmw and allianz co 2 emissions statistics from the 2023 non-financial report. these question types feature extractive questions that require straightforward data retrieval, and hierarchical questions which require disambiguating terms based on the table's hierarchical structure. gri-qa contains calculated questions that can be relational, focusing on understanding numerical relationships between table entries, such as comparisons (blue box in the figure ), superlatives (yellow box), or rankings (purple box). it also includes quantitative questions, which demand precise computations (i.e., sums, averages, or percentage variations in pink boxes in the figure ) using numerical data. moreover, gri-qa proposes multistep questions which require to process multiple operations on (multiple) tables from (multiple) documents (fuchsia boxes). some of these questions may require a textual value as an answer, as in the example for the multi-step superlative question. to assess the benchmark's complexity, we evaluated several state-of-the-art tabular questionanswering systems and gpt models. the experimental results in section 4 highlight a significant performance gap between humans and models, particularly in multi-step and multi-table reasoning. while gpt-based models with cot prompting achieve strong results on simpler tasks, they still struggle with more complex ones, especially when dealing with multi-table scenarios. financial models show promise but exhibit greater variability depending on the dataset. these findings underscore the benchmark's relevance and suggest the need for further research, particularly in addressing complex, multi-step and multi-table questions. in summary, our contributions are threefold: (1) we introduce gri-qa, a publicly available bench- ## the gri-qa benchmark the gri-qa benchmark consists of a total of 4089 questions spanning 204 tables extracted from corporate english reports 2 published in 2023 from companies in germany (19), france (7), and italy (4). the questions are logically organized into eight datasets based on their types, as shown in finally, the questions are annotated with their gri topic. gri-qa focuses specifically on envi-2 www.annualreports.com ronmental topics (see appendix b for a detailed description of the gri 300 series), with their distribution across the datasets shown in the construction of gri-qa consists of two phases: retrieving the tables related to specified gri topics in corporate reports (section 3.1) and generating questions based on the extracted tables (section 3.2). ## experimental evaluation we assess the quality of gri-qa using table qa models trained on both general, and financialdomain datasets. in the follow, we present the baselines (section 4.1), metrics (section 4.2), and discuss the results obtained (section 4.3).