Arabic RAG benchmark - SILMA RAGQA

Dec 18, 2024

Unlocking the Power of Arabic Extractive Question Answering

As AI-powered tools continue to evolve, extractive question answering (QA) stands as a critical domain to unlock the potential of large language models. Particularly in Arabic, where natural language processing (NLP) resources often lag behind, innovative benchmarks are necessary to drive advancements. Introducing SILMA RAGQA Benchmark V1.0—an ambitious, bilingual benchmark designed to evaluate the efficacy of Arabic and English language models for Extractive Question Answering, with a special focus on Retrieval-Augmented Generation (RAG) applications.

This dataset not only fills a gap but sets a new standard for rigorous, bilingual QA model evaluation across diverse domains and complex scenarios. Here’s an overview of SILMA RAGQA’s features, capabilities, and its role in advancing language technologies.

What is SILMA RAGQA?

The SILMA RAGQA Benchmark is a curated dataset that evaluates Arabic and English language models across extractive QA tasks. The benchmark consists of 17 bilingual datasets spanning domains such as medical, financial, and general knowledge. Designed to test critical features of language models, it serves as a standard for identifying strengths and limitations in models, particularly for those aiming to handle bilingual and domain-specific QA challenges.

SILMA RAGQA has already been employed to assess the upcoming SILMA Kashif Model, slated for release in January 2025. Results from this model evaluation have provided key insights into the robustness and versatility of state-of-the-art QA systems.

What Does SILMA RAGQA Test?

The SILMA RAGQA benchmark isn’t just a static evaluation tool; it’s designed to comprehensively test nuanced capabilities of language models. Some of the most critical aspects it evaluates include:

  1. General QA Capabilities

    • Tests the ability of a model to understand and respond to both Arabic and English queries accurately.

  2. Short and Long Context Handling

    • Assesses how well the model handles varying input lengths, from brief paragraphs to extensive texts.

  3. Answer Types: Short vs. Long

    • Measures the ability to generate concise answers as well as elaborate, context-rich responses.

  4. Complex Numerical Reasoning

    • Evaluates the model’s capacity to handle complex calculations and numerical data embedded in text.

  5. Tabular Data Understanding

    • Tests performance in extracting insights and answering questions based on structured tabular data.

  6. Multi-hop Question Answering

    • Requires the model to synthesize information from multiple paragraphs to form a coherent response.

  7. Negative Rejection

    • A vital test for practical applications: the model’s ability to identify when no valid answer can be provided and confidently state that.

  8. Multi-domain Performance

    • Tests the model's ability to adapt to texts from different industries, such as healthcare and finance.

  9. Noise Robustness

    • Assesses resilience to noisy and ambiguous input contexts, ensuring performance in real-world, less-than-ideal conditions.

Dataset Highlights

The dataset includes a robust selection of sources, with each one specifically tailored to challenge different aspects of model performance. Here are some standout datasets within SILMA RAGQA:

  • XQuAD-R (Arabic & English): A foundational dataset that tests bilingual comprehension.

  • COVID-QA (Arabic & English): Focuses on health-related questions

  • FinQA & TabQA (Arabic & English): Assess financial data comprehension and tabular reasoning.

  • BoolQ & SciQ (Arabic): Evaluate logical reasoning and science-based question answering.

A notable aspect is the inclusion of translated datasets to ensure comprehensive Arabic support, enabling robust comparisons between Arabic and English performance.

How to Use SILMA RAGQA for Model Evaluation

For developers and researchers eager to benchmark their models, SILMA RAGQA offers a straightforward evaluation process. Simply follow these steps:

  1. Visit the benchmark page on HuggingFace
    https://huggingface.co/datasets/silma-ai/silma-rag-qa-benchmark-v1.0#silma-rag-qa-benchmark

  2. Change the Model Name: Update the provided script with the name of your model.

  3. Install Dependencies: Install required libraries such as transformers, datasets, and scikit-learn.

  4. Run the Benchmarking Script: Execute the benchmarking script using the accelerate launch command for scalable performance.

This simplicity makes it accessible for researchers at all levels to evaluate and iterate on their models effectively.

SILMA Kashif Model: The Next Chapter

SILMA RAGQA’s relevance will only grow as the SILMA Kashif Model debuts in January 2025. Designed with insights gleaned from this benchmark, Kashif is expected to push the boundaries of extractive QA in Arabic and bilingual contexts. Researchers and industry professionals alike are encouraged to stay tuned for its release.

Final Thoughts

SILMA RAGQA Benchmark V1.0 is a transformative step forward for NLP in Arabic. By rigorously testing models across diverse, challenging, and bilingual datasets, it provides invaluable insights for the development of robust, accurate, and versatile QA systems.



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