Researchers rigorously examine ChatGPT’s morphological capabilities in four languages (English, German, Tamil, and Turkish). ChatGPT falls short compared to specialized systems, especially in English. The analysis highlights ChatGPT’s limitations on morphological abilities, challenging claims of human-like language proficiency.
Recent research on large language models (LLMs) has predominantly focused on syntax and semantics, overlooking morphology. The existing literature on LLM often needs to pay more attention to the full range of linguistic phenomena. While previous studies have explored the past tense in English, a comprehensive analysis of morphological skills in LLMs is needed. The method employs the Wug test to evaluate the morphological abilities of ChatGPT in the four mentioned languages. The findings challenge claims of human-like language proficiency in ChatGPT, indicating its limitations compared to specialized systems.
While recent large language models such as GPT-4, LLaMA, and PaLM have shown promise in linguistic abilities, there has been a notable gap in the evaluation of their morphological capabilities: the ability to systematically generate words. Previous studies have predominantly focused on syntax and semantics, overlooking morphology. The approach addresses the deficiency by systematically analyzing the morphological abilities of ChatGPT using the wug test in the four mentioned languages and comparing its performance with specialized systems.
The proposed method evaluates the morphological capabilities of ChatGPT through the Wug test, comparing its results with supervised baselines and human annotations using precision as a metric. Unique data sets of nonce words are created to ensure there is no prior exposure to ChatGPT. Three styles of cueing are used: zero shot, single shot, and few shots, with multiple runs for each style. The assessment takes into account morphological variation between speakers and covers four languages: English, German, Tamil and Turkish, while comparing the results with systems specially designed for performance evaluation.
The study revealed that ChatGPT needs more systems designed specifically with morphological capabilities, particularly in English. Performance varied by language, with German reaching near-human performance levels. The value of k (number of top-ranked answers considered) had an impact, widening the gap between baselines and ChatGPT as k increased. ChatGPT tended to generate implausible inflections, potentially influenced by a bias toward real words. The findings underscore the need for more research into the morphological capabilities of large linguistic models and warn against hasty claims of human-like linguistic abilities.
The study rigorously analyzed ChatGPT’s morphological capabilities in four declared languages, revealing its poor performance, especially in English. It underlines the need to further investigate the morphological capabilities of large linguistic models and warns against premature claims of human-like linguistic abilities. ChatGPT showed variable performance across languages, with German achieving human-level performance. The study also pointed out the real-world bias of ChatGPT, 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), which limits generalization to other versions of GPT-3 or GPT-4 and later. Focusing on a small set of languages raises questions about the generalizability of the results to different languages and data sets. Comparing languages is challenging due to uncontrolled variables. Limited annotators and low inter-annotator agreements for Tamil may affect reliability. ChatGPT’s variable performance across languages suggests potential generalizability limitations.
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Sana Hassan, a consulting intern at Marktechpost and a dual degree student at IIT Madras, is passionate about applying technology and artificial intelligence to address real-world challenges. With a strong interest in solving practical problems, she brings a new perspective to the intersection of ai and real-life solutions.
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