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Morphemes Without Borders: Evaluating Root-Pattern Morphology in Arabic Tokenizers and LLMs

arXiv:2603.15773v1 Announce Type: new Abstract: This work investigates how effectively large language models (LLMs) and their tokenization schemes represent and generate Arabic root-pattern morphology, probing whether they capture genuine morphological structure or rely on surface memorization. Arabic morphological system provides a rich testbed for analyzing how LLMs handle complex, non-concatenative forms and how tokenization choices influence this process. Our study begins with an evaluation of morphological fidelity across Arabic and multilingual tokenizers against gold-standard segmentation, followed by an analysis of LLM performance in productive root-pattern generation using a newly developed test set. Our findings across seven Arabic-centric and multilingual LLMs and their respective tokenizers reveal that tokenizer morphological alignment is not necessary nor sufficient for morphological generation, which questions the role of morphological tokenization in downstream performa

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Yara Alakeel, Chatrine Qwaider, Hanan Aldarmaki, Sawsan Alqahtani · · 1 min read · 13 views
#cs.CL #cs.AI

arXiv:2603.15773v1 Announce Type: new Abstract: This work investigates how effectively large language models (LLMs) and their tokenization schemes represent and generate Arabic root-pattern morphology, probing whether they capture genuine morphological structure or rely on surface memorization. Arabic morphological system provides a rich testbed for analyzing how LLMs handle complex, non-concatenative forms and how tokenization choices influence this process. Our study begins with an evaluation of morphological fidelity across Arabic and multilingual tokenizers against gold-standard segmentation, followed by an analysis of LLM performance in productive root-pattern generation using a newly developed test set. Our findings across seven Arabic-centric and multilingual LLMs and their respective tokenizers reveal that tokenizer morphological alignment is not necessary nor sufficient for morphological generation, which questions the role of morphological tokenization in downstream performance.

Executive Summary

This article critically examines the representation and generation of Arabic root-pattern morphology in large language models (LLMs) and their tokenization schemes. The study evaluates the morphological fidelity of various Arabic and multilingual tokenizers against gold-standard segmentation and analyzes LLM performance in productive root-pattern generation using a newly developed test set. The findings reveal that tokenizer morphological alignment is not necessary nor sufficient for morphological generation, challenging the role of morphological tokenization in downstream performance. This research has significant implications for the development of more effective Arabic language processing tools and highlights the need for a deeper understanding of the complex relationships between tokenization, morphology, and LLM performance.

Key Points

  • ▸ The study evaluates the morphological fidelity of Arabic and multilingual tokenizers against gold-standard segmentation.
  • ▸ LLMs' performance in productive root-pattern generation is analyzed using a newly developed test set.
  • ▸ Tokenizer morphological alignment is not necessary nor sufficient for morphological generation.

Merits

Contributions to the field of Arabic language processing

The study provides a comprehensive evaluation of the representation and generation of Arabic root-pattern morphology in LLMs, shedding light on the complex relationships between tokenization, morphology, and LLM performance.

Development of a new test set for evaluating LLMs

The newly developed test set for productive root-pattern generation will enable more accurate evaluation and comparison of LLMs' performance in handling Arabic morphology.

Demerits

Limited scope to generalizability

The study primarily focuses on Arabic language processing and may not be directly applicable to other languages or linguistic contexts.

Tokenization schemes' influence on LLM performance

The study highlights the need for further research on the impact of tokenization schemes on LLM performance and the potential trade-offs between tokenization and morphological representation.

Expert Commentary

The study's contribution to the field of Arabic language processing is substantial, as it sheds light on the complex relationships between tokenization, morphology, and LLM performance. However, the study's limited scope to generalizability and the need for further research on tokenization schemes' influence on LLM performance are notable limitations. The development of more effective Arabic language processing tools and the prioritization of morphological representation and generation in LLM architectures will have significant implications for the field of natural language processing.

Recommendations

  • ✓ Future research should focus on developing more accurate tokenization schemes and morphological representation mechanisms for Arabic and other languages with complex morphology.
  • ✓ LLM architectures should prioritize morphological representation and generation, potentially leading to improved performance in languages with complex morphology.

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