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Assessing the Linguistic Mastery of Artificial Intelligence: A Deep Dive into ChatGPT’s Morphological Skills Across Languages

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Assessing the Linguistic Mastery of Artificial Intelligence: A Deep Dive into ChatGPT’s Morphological Skills Across Languages

Researchers rigorously examine ChatGPT’s morphological abilities across 4 languages (English, German, Tamil, and Turkish). ChatGPT falls short in comparison with specialized systems, especially in English. The evaluation underscores ChatGPT’s limitations in morphological skills, difficult assertions of human-like language proficiency.

Recent investigations into large language models (LLMs) have predominantly focused on syntax and semantics, overlooking morphology. The present LLM literature must often pay more attention to the total range of linguistic phenomena. While past studies have explored the English past tense, a comprehensive evaluation of morphological abilities in LLMs is required. The tactic employs the Wug test to evaluate ChatGPT’s morphological skills within the 4 mentioned languages. Findings challenge claims of human-like language proficiency in ChatGPT, indicating its limitations in comparison with specialized systems.

While recent large language models like GPT-4, LLaMA, and PaLM have shown promise in linguistic abilities, there’s been a notable gap in assessing their morphological capabilities – the skill to generate words systematically. Previous studies have predominantly focused on syntax and semantics, overlooking morphology. The approach addresses the deficiency by systematically analyzing ChatGPT’s morphological skills using the wug test across 4 mentioned languages and comparing its performance with specialized systems. 

The proposed method assesses ChatGPT’s morphological abilities through the Wug test, comparing its outputs with supervised baselines and human annotations using accuracy because the metric. Unique datasets of nonce words are created to make sure no prior exposure to ChatGPT. Three prompting styles, zero-shot, one-shot, and few-shot, are used, with multiple runs for every style. The evaluation accounts for inter-speaker morphological variation and spans 4 languages: English, German, Tamil, and Turkish while comparing results with purpose-built systems for performance assessment.

The study revealed that ChatGPT needs more purpose-built systems with morphological capabilities, particularly in English. Performance varied across languages, with German achieving near-human performance levels. The worth of k (variety of top-ranked responses considered) had an impact, widening the gap between baselines and ChatGPT as k increased. ChatGPT tended to generate implausible inflexions, potentially influenced by a bias towards real words. The findings stress the need for more research into large language models’ morphological abilities and caution against hasty claims of human-like language skills.

The study rigorously analyzed ChatGPT’s morphological capabilities in 4 stated languages, revealing its underperformance, notably in English. It underscores the necessity for further research into large language models’ morphological abilities and warns against premature claims of human-like language skills. ChatGPT exhibited various performance across languages, with German reaching human-level performance. The study also noted ChatGPT’s real-world bias, emphasizing the importance of considering morphology in language model evaluations, given its fundamental role in human language.

The study employed a single model (gpt-3.5-turbo-0613), limiting generalizability to other GPT-3 versions or GPT-4 and beyond. Specializing in a small language set raises questions on result generalizability to different languages and datasets. Comparing languages is difficult resulting from uncontrolled variables. Limited annotators and low inter-annotator agreements for Tamil may impact reliability. Variable ChatGPT performance across languages suggests potential generalizability limitations.


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Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is obsessed with applying technology and AI to deal with real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.


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