Large Language Models and the Future of Computational Linguistics
Keywords:
Large Language Models (LLMs), Computational Linguistics, Natural Language Processing (NLP), Artificial Intelligence (AI), Language Technology.Abstract
The emergence of Large Language Models (LLMs) has marked a significant milestone in the development of Artificial Intelligence (AI) and Natural Language Processing (NLP), profoundly influencing the field of computational linguistics and transforming the way language is analyzed, processed, and generated. This study aims to examine the impact of LLMs on the future development of computational linguistics by exploring their contributions, limitations, and broader implications for language research and technology. The study employs a qualitative literature review approach, incorporating elements of systematic literature review and conceptual analysis to synthesize findings from scholarly publications, conference proceedings, and industry reports related to LLMs and computational linguistics. The results indicate that LLMs have substantially enhanced language modeling, machine translation, text generation, discourse analysis, language documentation, and multilingual processing, while also expanding opportunities for linguistic research and AI-driven language applications. Furthermore, the findings reveal that LLMs outperform many traditional NLP approaches through their ability to capture complex syntactic, semantic, and contextual relationships. However, challenges such as hallucination, bias, limited explainability, privacy concerns, and ethical issues continue to affect their reliability and responsible use. In conclusion, LLMs represent a transformative force in computational linguistics, offering unprecedented opportunities for innovation and language technology development while simultaneously introducing important technical, ethical, and societal challenges. The future of computational linguistics is likely to involve deeper integration of AI-driven methodologies supported by continued human expertise, interdisciplinary collaboration, and the development of more transparent, fair, and linguistically informed language models.
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