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Description
The focus of this paper is on Generative Artificial Intelligence (GenAI), chatbots and some implications for lexicography and dictionary use. It has been well documented that chatbots originally tended to “hallucinate” if they did not have an answer to the prompt put to them. Much larger training databases have, however, been developed and chatbots have become more accurate. Multiple iterations of chatbots from a variety of companies have been released, including specialised chatbots for different environments. AI and chatbots have also been frequent topics in recent lexicographic research and have been employed in dictionary compilation and the preparation of writing assistants (cf., e.g., Li et al. (2023), De Schryver (2023), Fuertes-Olivera (2024), Lew 2024 & Li & Tarp (2025)). From a lexicographic perspective, the importance of linking between dictionaries and other information tools (cf., e.g., Bothma and Gouws 2022, Bothma and Fourie 2024, Bothma and Fourie 2025) also becomes relevant for lexicographic uses of chatbots.
The use of GenAI as an information tool to provide information to end-users (readers) who have a specific information need when reading a text, i.e., a text reception information need, is discussed in detail. It has been shown that GenAI can provide content similar to a dictionary, but that it cannot provide contextualised answers, i.e., the reader is still dependent on their own evaluation of the GenAI-provided content to determine the meaning of the word or phrase in context. If sufficient context is provided in the prompt, the chatbot often provides only a single meaning / sense. If the chatbot misunderstood the context provided in the prompt, it could easily provide an incorrect meaning. If then queried (through a follow-up prompt) why it chose a specific meaning, it could not provide any explanation. Quite recently, however, this changed, and most chatbots now have two modes, a “search” mode and a “thinking / reasoning mode”, i.e., it is able to argue logically about its different proposed meanings in context and tends to offer a solution. This feature is discussed at the hand of a number of examples containing specific keywords that determine the correct interpretation in context, as well as examples with potentially ambiguous part-of-speech and syntactic analyses, using two different chatbots, viz. ChatGPT o3-mini and DeepSeek-V3 (DeepThink-R1). Based on the limited number of examples, it seems as if the chatbots can provide correct contextual meaning and logically motivate the choice of meaning in context, based on their critical analysis and thinking skills, typically associated with humans. Unfortunately, however, it still “hallucinates” if it has no answer, as will be shown from one non-lexicographic example, and the reader remains responsible to critically evaluate any GenAI responses – “lector caveat.” Nevertheless, in slightly more than two years, tremendous progress has been made, and one can only speculate what next developments would be.
These developments raise the question of what the role of dictionaries and the role of lexicographers will be in future in an AI-enhanced world. In conclusion, a few suggestions will be offered about lexicographic databases, appropriate interfaces, access to additional lexicographic and non-lexicographic data, refining dictionary definitions, multifunctional dictionaries, and the reuse of lexicographic information in different applications. The traditional role of dictionaries to document the status and history of a language is still a very important function and needs to be encouraged, especially in environments with limited language resources. However, exploring new commercial ventures, incorporating latest technologies, would be essential to the future of the discipline and industry.
