Benchmarking Arabic and English LLMs for Retrieval-Augmented Question Answering: Introducing SILMA RAGQA V10

2024-12-18

Large Language Models (LLMs) have revolutionized how we interact with information. However, evaluating their effectiveness in tasks like Retrieval-Augmented Generation (RAG) Question Answering (QA) across languages can be challenging. This is where SILMA RAGQA V1.0 comes in!

SILMA RAGQA, a benchmark curated by silma.ai, provides a comprehensive assessment tool for gauging the capabilities of Arabic and English LLMs in tackling complex QA scenarios within the RAG framework.

A Diverse Testing Ground for LLM Prowess

The benchmark offers a rich tapestry of 17 bilingual datasets, encompassing various domains, to rigorously test LLM performance across several key areas:

General Arabic and English QA: How well can the LLM handle basic question-answering tasks in both languages?
Contextual Flexibility: Can the LLM effectively process short or lengthy contextual passages while answering questions?
Answer Length Versatility: Does the LLM excel at generating both concise and elaborate answers as needed?
Numerical Expertise: Can the LLM tackle questions involving complex numerical calculations?
Tabular Data Mastery: How efficiently can the LLM extract answers from tabular data formats?
Multi-Hop Reasoning: Can the LLM integrate information from multiple paragraphs to answer a single question?
Negative Rejection Accuracy: Is the LLM capable of identifying inaccurate responses and offering corrective statements like “answer not found in provided context”?
Domain Adaptability: Can the LLM seamlessly navigate questions across diverse domains like finance and medicine?
Noise Resistance: How effectively can the LLM handle noisy or ambiguous information within the context?

What Undercode Says:

SILMA RAGQA V1.0 is a significant contribution to the field of LLM evaluation. Here’s why it stands out:

Multilinguality: By incorporating both Arabic and English datasets, the benchmark caters to a broader audience and facilitates cross-lingual LLM comparisons.
Comprehensive Testing: The diverse range of datasets ensures a thorough evaluation of LLM capabilities across various facets of RAG QA.
Real-World Relevance: The focus on multi-domain scenarios reflects the practical applications of LLMs in handling different types of information.

Negative Rejection: This crucial functionality assesses an

Beyond the Benchmark: A Look at Leaderboard Insights

The provided leaderboard, featuring various LLM models and their corresponding scores on the benchmark, offers valuable insights:

The Performance Landscape: The leaderboard allows for comparisons between different models, highlighting their strengths and weaknesses in RAG QA tasks.
Emerging Trends: The presence of models like SILMA-Kashif (to be released in January 2025) indicates the ongoing development of more effective LLM solutions.
The Road Ahead: The leaderboard serves as a springboard for further research and development in LLM architectures specifically tailored for RAG QA.

SILMA RAGQA V1.0 is a valuable tool for researchers and developers working on Arabic and English LLMs. By utilizing this benchmark, they can objectively assess their models’ effectiveness and identify areas for improvement. This ultimately leads to the advancement of LLMs capable of handling complex QA tasks with greater accuracy and adaptability.