Abstract
This study investigates the proficiency of the AI-powered language model ChatGPT in analyzing both context and speech acts. Implementing Dell Hymes' contextual framework for context analysis and John Searle's approach for speech act analysis, the research aims to uncover ChatGPT's potentials and limitations in these domains. ChatGPT excels in specific contextual elements such as Ends, Act Sequence, Norms, and Genres but faces challenges in analyzing the other elements. Other limitations include a tendency to provide lengthy responses, repetition of details, and inconsistency in analyses across different chats. In speech act analysis, ChatGPT shows improvement compared to contextual analysis, with focused assessments resulting in higher accuracy. Similar to context analysis, inconsistencies and recurring errors are evident in speech act identification. The study concludes that ChatGPT's performance, while not flawless, demonstrates a significant degree of accuracy.
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TopIntroduction
In the vast domain of Natural Language Processing (NLP), the inherent complexity of human communication poses a tough challenge for machines to effectively comprehend language and respond accordingly. Among the intricate layers of linguistic understanding, the exploration of context and speech acts stands out as a critical frontier. Being able to analyze context, the encompassing framework that shapes the meaning of words and phrases (Levinson, 1983), and speech acts, the functional units of communication reflecting intentions and actions (Searle, 1969) drives NLP towards a more comprehensive understanding of human language.
The ability to decipher context is a must for machines seeking to interpret the complexities intrinsic in human conversation. Contextual clues, ranging from linguistic references to the broader situational awareness, play a crucial role in disambiguating language and inserting meaning into utterances (Austin, 1962, p 100). To what extent are NLP systems competent in figuring out both the linguistic and physical contexts in different genres of texts? This remains an open question that necessitates thorough investigation.
Simultaneously, the study of speech acts is essential in the pragmatic dimension of language as it focuses on the intentions behind words and the subsequent impact on the listener (Searle, 1969, p 48). Speech acts encompass a diverse range of communicative functions, from making assertions and requests to issuing commands and expressing emotions (Levinson, 1983, p 368). Understanding speech acts provides NLP systems with the capability to move beyond mere syntactic parsing and explore the pragmatic layers of human interaction, paving the way for more sophisticated and contextually aware conversational agents. Likewise, the question remains open, to what extent are NLP systems ready to analyze speech acts in diverse genres of language?
Language models, at the forefront of artificial intelligence especially NLP, have revolutionized humans’ interaction with technology by enabling machines to understand and generate human-like language. Among the notable advancements in this domain are models like ChatGPT which represents significant development in the field. ChatGPT (Kalla, 2023), developed by OpenAI, is a powerful language model that employs advanced artificial intelligence techniques to generate natural language responses based on given prompts or inputs.
This study aims to assess ChatGPT’s capability to analyzing context and speech acts implementing two of the most popular approaches within the domain of pragmatics, namely Dell Hymes’ context framework and John Searle’s speech act classification. More particularly, identifying potentials and limitations of ChatGPT’s analysis of context and speech acts is the main scope of the study.
Key Terms in this Chapter
Potentials: All the potential responses of AI to a given stimulus.
Limitations: The weaknesses or inadequacies of AI during information processing.
Artificial Intelligence (AI): Algorithms that are programmed to reflect and imitate artificial human thinking.
Text Analysis: The analysis of texts by mining them to find meanings with the help of analytic methods.
Context: The discourse environment in which utterances gain their meaning loads.
ChatGPT: A chatbot which processes language stimuli, makes analyses and create human-like conversations.
Speech Acts: A linguistic theory developed by Searle (1969) which emphasizes that utterances can be used for manipulating the hearer and getting a specific response from the hearer.