Advancements in Large Language Models for Arabic and Structured Knowledge

Introduction

The field of natural language processing is witnessing significant advancements in the development of Large Language Models (LLMs) for Arabic and structured knowledge. Recent studies have focused on improving the performance of LLMs in these areas, addressing the limitations of existing models and datasets.

General Direction

The field is moving towards creating more comprehensive and rigorous benchmarks to evaluate the capabilities of LLMs in understanding structured knowledge and Arabic language. Researchers are working on developing new datasets and evaluation frameworks to address the gaps in existing resources, particularly in areas such as STEM, code generation, and tabular data.

Innovative Work

Noteworthy papers in this area include:

  • 3LM, which introduces a suite of benchmarks designed specifically for Arabic, focusing on STEM-related question-answer pairs and code generation.
  • AraTable, which presents a novel benchmark for evaluating the reasoning and understanding capabilities of LLMs when applied to Arabic tabular data.
  • SKA-Bench, which proposes a comprehensive and rigorous structured knowledge understanding benchmark to diagnose the shortcomings of LLMs.

Sources

Mind the Gap: A Review of Arabic Post-Training Datasets and Their Limitations

3LM: Bridging Arabic, STEM, and Code through Benchmarking

Beyond Isolated Dots: Benchmarking Structured Table Construction as Deep Knowledge Extraction

SKA-Bench: A Fine-Grained Benchmark for Evaluating Structured Knowledge Understanding of LLMs

TyDi QA-WANA: A Benchmark for Information-Seeking Question Answering in Languages of West Asia and North Africa

AraTable: Benchmarking LLMs' Reasoning and Understanding of Arabic Tabular Data

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