Duplicate from G4KMU/t2-ragbench

Co-authored-by: Jan Strich <strich@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-4.0
+pretty_name: T2-RAGBench
+task_categories:
+- table-question-answering
+- question-answering
+language:
+- en
+tags:
+- pdf
+- finance
+- rag
+arxiv: 2506.12071
+configs:
+- config_name: FinQA
+  data_files:
+  - split: train
+    path: data/FinQA/train/*
+  - split: dev
+    path: data/FinQA/dev/*
+  - split: test
+    path: data/FinQA/test/*
+- config_name: ConvFinQA
+  data_files:
+  - split: turn_0
+    path: data/ConvFinQA/*
+- config_name: TAT-DQA
+  data_files:
+  - split: train
+    path: data/TAT-DQA/train/*
+  - split: dev
+    path: data/TAT-DQA/dev/*
+  - split: test
+    path: data/TAT-DQA/test/*
+---
+
+# Dataset Card for T2-RAGBench
+
+[**Project Page**](https://t2ragbench.demo.hcds.uni-hamburg.de) | [**Paper**](https://huggingface.co/papers/2506.12071) | [**Code**](https://github.com/uhh-hcds/g4kmu-paper)
+
+## Table of Contents
+
+- [Dataset Card for T2-RAGBench](#dataset-card-for-t2-ragbench)
+  - [Table of Contents](#table-of-contents)
+  - [Dataset Description](#dataset-description)
+    - [Dataset Summary](#dataset-summary)
+    - [Supported Tasks](#supported-tasks)
+    - [Leaderboards](#leaderboards)
+    - [PDF Files](#pdf-files)
+    - [Languages](#languages)
+  - [Dataset Structure](#dataset-structure)
+    - [Data Instances](#data-instances)
+    - [Data Fields](#data-fields)
+      - [FinQA and ConvFinQA Only](#finqa-and-convfinqa-only)
+      - [TAT-DQA Only](#tat-dqa-only)
+    - [Data Splits](#data-splits)
+  - [Dataset Creation](#dataset-creation)
+    - [Curation Rationale](#curation-rationale)
+    - [Source Data](#source-data)
+    - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+  - [Considerations for Using the Data](#considerations-for-using-the-data)
+    - [Social Impact of Dataset](#social-impact-of-dataset)
+    - [Discussion of Biases](#discussion-of-biases)
+    - [Other Known Limitations](#other-known-limitations)
+  - [Additional Information](#additional-information)
+    - [Licensing Information](#licensing-information)
+    - [Citation Information](#citation-information)
+    - [Contributions](#contributions)
+
+---
+
+## IMPORTANT NOTICE:
+We deleted VQAonBD from the dataset due to low quality of the question reformulations. If you still want to use it you will find the data in the previous commit history.
+
+## Dataset Description
+
+### Dataset Summary
+
+T2-RAGBench is a benchmark dataset designed to evaluate Retrieval-Augmented Generation (RAG) on financial documents containing both text and tables. It consists of **23,088** context-independent question-answer pairs and over **7300** documents derived from three curated datasets: FinQA, ConvFinQA, and TAT-DQA. Each instance includes a reformulated question, a verified answer, and its supporting context composed of textual and tabular information. It is also possible to use the pdfs directly, as the dataset includes the original PDF files.
+
+### Supported Tasks
+
+- Question Answering (QA)
+- Table-based Question Answering (TableQA)
+- Retrieval-Augmented Generation (RAG)
+
+### Leaderboards
+
+You can submit your results to the [T2-RAGBench leaderboard](https://t2ragbench.demo.hcds.uni-hamburg.de) to compare your model's performance against others.
+The submission guidelines are available on the leaderboard page.
+
+### PDF Files
+
+The dataset includes original PDF files from which the text and tables were extracted. These files can be used for direct document-based tasks or to verify the context of the questions. To download the PDF files clone the this repository and all files will be available in the `data` directory. The files are organized by dataset and split, matching the structure of the dataset.
+
+### Languages
+
+- English
+
+---
+
+## Dataset Structure
+
+### Data Instances
+
+Each instance contains a unique identifier, a question, a context (text and table), and a verified answer.
+
+### Data Fields
+
+For each subset, each sample contains the following fields:
+
+- `id`: Unique identifier for the sample  
+- `context_id`: Identifier for the context document  
+- `split`: Dataset split (`train`, `dev`, `test`, `turn_0`, or `validation_5`)  
+- `question`: Context-independent QA query  
+- `program_answer`: Reformulated numeric answer used for evaluation  
+- `original_answer`: Original answer from the source dataset  
+- `context`: Extracted document text including both textual and tabular information  
+- `file_name`: Name of the source PDF file  
+
+#### FinQA and ConvFinQA Only
+
+- `table`: Table content extracted from the PDF in Markdown format  
+- `pre_text`: Document text located before the table  
+- `post_text`: Document text located after the table  
+- `company_name`: Name of the company from the financial report  
+- `company_symbol`: Stock ticker symbol of the company  
+- `report_year`: Year of the financial report  
+- `page_number`: Page number in the PDF where the table was found  
+- `company_sector`: Sector classification of the company (e.g., Financials, Energy)  
+- `company_industry`: Industry classification of the company  
+- `company_headquarters`: Location of the company's headquarters  
+- `company_date_added`: Date the company was added to the reference index (e.g., S&P 500)  
+- `company_cik`: Central Index Key used by the SEC for company identification  
+- `company_founded`: Year the company was founded  
+
+
+#### TAT-DQA Only
+
+- `company_name`: Name of the company from the financial report  
+- `report_year`: Year of the financial report  
+- `company_sector`: Sector classification of the company  
+
+### Data Splits
+
+| Subset     | Domain  | # Documents | # QA Pairs | Avg. Tokens/Doc | Avg. Tokens/Question |
+|------------|---------|-------------|------------|------------------|-----------------------|
+| FinQA      | Finance | 2,789       | 8,281      | 950.4            | 39.2                  |
+| ConvFinQA  | Finance | 1,806       | 3,458      | 890.9            | 30.9                  |
+| TAT-DQA    | Finance | 2,723       | 11,349     | 915.3            | 31.7                  |
+| **Total**  |         | **8,095**   | **32,908** | **803.2**        | **36.3**              |
+
+---
+
+## Dataset Creation
+
+### Curation Rationale
+
+Most existing QA datasets rely on oracle-contexts, which limit their ability to evaluate retrieval quality. T2-RAGBench transforms questions into a context-independent form to evaluate both retrieval and reasoning.
+
+### Source Data
+
+Selected from existing QA datasets: FinQA, ConvFinQA, TAT-DQA. FinQA and ConvFinQA are based on FinTabNet. TAT-DQA is a diverse QA set with a focus on numerical answers.
+
+### Annotations
+
+Questions were reformulated with LLaMA 3.3-70B to ensure context-independence. Human annotators verified a random subset of examples. Reformulated questions showed >80% context-independence compared to <10% in the originals.
+
+---
+
+## Personal and Sensitive Information
+
+Documents originate from public financial filings. No sensitive or personal user data is included. Entity names are real company names extracted from SEC filings.
+
+---
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+T2-RAGBench encourages the development of RAG systems capable of reasoning over complex, real-world documents, such as those found in finance.
+
+### Discussion of Biases
+
+The dataset focuses on financial documents, and domain-specific biases such as consistent formats or terminology may limit generalizability.
+
+### Other Known Limitations
+
+- Reformulated questions are LLM-generated  
+- Performance evaluations may be influenced by prompt templates  
+- Dataset focused on finance domain only  
+
+---
+
+## Additional Information
+
+We also have converted other SOTA benchmark in the same format.
+They can be found in our collection:
+https://huggingface.co/collections/G4KMU/rag-datasets
+
+### Licensing Information
+
+CC-BY-4.0
+
+### Citation Information
+
+```bibtex
+@inproceedings{strich-etal-2026-t2,
+    title = "T$^2$-{RAGB}ench: Text-and-Table Benchmark for Evaluating Retrieval-Augmented Generation",
+    author = "Strich, Jan  and
+      Isgorur, Enes Kutay  and
+      Trescher, Maximilian  and
+      Biemann, Chris  and
+      Semmann, Martin",
+    editor = "Demberg, Vera  and
+      Inui, Kentaro  and
+      Marquez, Llu{\'i}s",
+    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
+    month = mar,
+    year = "2026",
+    address = "Rabat, Morocco",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2026.eacl-long.8/",
+    doi = "10.18653/v1/2026.eacl-long.8",
+    pages = "165--191",
+    ISBN = "979-8-89176-380-7"
+}
+```
+
+### Contributions
+This benchmark builds upon the following datasets:
+
+- [FinQA](https://github.com/czyssrs/FinQA): Numerical reasoning over financial documents  
+- [ConvFinQA](https://github.com/czyssrs/ConvFinQA): Conversational QA extension of FinQA  
+- [TAT-DQA](https://nextplusplus.github.io/TAT-DQA/): Hybrid document QA with tables and text

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