metadata

dataset_info:
  features:
    - name: query
      dtype: string
    - name: answer
      dtype: string
    - name: text
      dtype: string
    - name: choices
      sequence: string
    - name: gold
      dtype: int64
  splits:
    - name: train
      num_bytes: 474769
      num_examples: 267
    - name: validation
      num_bytes: 74374
      num_examples: 48
    - name: test
      num_bytes: 387629
      num_examples: 225
  download_size: 358300
  dataset_size: 936772
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
license: apache-2.0
task_categories:
  - question-answering
language:
  - gr
tags:
  - finance
  - qa
pretty_name: Plutus QA
size_categories:
  - n<1K

Dataset Card for Plutus QA

Table of Contents
Dataset Description
Dataset Structure
Dataset Creation
Considerations for Using the Data
Additional Information

Dataset Description

Homepage: https://huggingface.co/collections/TheFinAI/plutus-benchmarking-greek-financial-llms-67bc718fb8d897c65f1e87db
Repository: https://huggingface.co/datasets/TheFinAI/plutus-QA
Paper: Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance
Leaderboard: https://huggingface.co/spaces/TheFinAI/Open-Greek-Financial-LLM-Leaderboard#/
Model: https://huggingface.co/spaces/TheFinAI/plutus-8B-instruct

Dataset Summary

Plutus QA is a question-answering dataset designed for financial applications in the Greek language. This resource contains a diverse set of queries, each paired with an answer, additional context text, a set of multiple-choice options, and a gold label index indicating the correct choice. By integrating textual context with a multiple-choice format, the dataset is aimed at benchmarking the capabilities of large language models in resolving financial questions in low-resource settings.

Supported Tasks

Task: Question Answering
Evaluation Metrics: Accuracy

Languages

Greek

Dataset Structure

Data Instances

Each instance in the dataset consists of the following five fields:

query: A question or prompt regarding financial matters.
answer: The corresponding answer text paired with the query.
text: Additional context or background information to support the query.
choices: A sequence field containing multiple-choice answer options.
gold: An integer field representing the index of the correct answer in the choices.

Data Fields

query: String – Represents the financial question or prompt.
answer: String – Contains the answer aligned with the given query.
text: String – Provides extra contextual information related to the query.
choices: Sequence of strings – Lists all available answer options.
gold: Int64 – Denotes the index of the correct answer from the choices.

Data Splits

The dataset is organized into three splits:

Train: 267 instances (474,769 bytes)
Validation: 48 instances (74,374 bytes)
Test: 225 instances (387,629 bytes)

Dataset Creation

Curation Rationale

The Plutus QA dataset was created to enable the evaluation of large language models on question-answering tasks within the financial domain for Greek language texts. Its design—including multiple-choice answers with additional context—aims to reflect complex financial decision-making and reasoning processes in a low-resource language environment.

Source Data

Initial Data Collection and Normalization

The source data is derived from Greek university financial exams. Standardization and normalization procedures were applied to ensure consistency across queries, choices, and textual context.

Who are the Source Language Producers?

Greek university financial exams.

Annotations

Annotation Process

Annotations were performed by domain experts proficient in both finance and linguistics. The process included verifying the correct answer for each query and marking the corresponding correct index among provided choices.

Who are the Annotators?

A team of financial analysts and linguists collaborated to ensure the annotations are accurate and reflective of real-world financial reasoning.

Personal and Sensitive Information

This dataset is curated to exclude any personally identifiable information (PII) and only contains public financial text data necessary for question-answering tasks.

Considerations for Using the Data

Social Impact of Dataset

The Plutus QA dataset is instrumental in enhancing automated question-answering systems in the financial sector, particularly for Greek language applications. Improved QA systems can support better financial decision-making, increase the efficiency of financial services, and contribute to academic research in financial natural language processing.

Discussion of Biases

The domain-specific financial language may limit generalizability to non-financial question-answering tasks.
Annotation subjectivity could introduce biases in determining the correct answer among multiple choices.
The dataset's focus on Greek financial documents may not fully represent other financial or multilingual contexts.

Other Known Limitations

Pre-processing may be required to handle variations in question and answer formats.
The dataset is specialized for the financial domain and may need adaptation for different QA tasks or domains.

Additional Information

Dataset Curators

The Fin AI Team

Licensing Information

License: Apache License 2.0

Citation Information

If you use this dataset in your research, please consider citing it as follows:

[Insert appropriate citation details and DOI when available.]

Contributions

Contributions, corrections, or improvements are welcome via the dataset’s repository on Hugging Face.

This data card provides a detailed overview of the "Plutus QA" dataset, including its structure, usage, and key attributes along with the shared paper, leaderboard, and model details. Adjust any sections as needed to suit your evolving project documentation.