Is Generative AI a reliable collocation dictionary builder? Or isn’t it (yet)?

Abstract

This study explores the potential of Generative AI (GenAI), specifically ChatGPT, for the acquisition and semantic organization of collocations in support of collocation-resource construction. We examine ChatGPT’s ability to generate English collocation candidates for eleven semantic collocation categories formalized as Lexical Functions (LFs). LFs are treated as a fine-grained intermediate analytical layer from which broader, user-oriented semantic groupings can be derived. Two prompting strategies are compared: a few-shot approach, in which the model receives twenty illustrative examples of a given LF and generates fifty further candidates, and an iterative profile-refinement approach, in which a lexicographer guides the model in constructing a semantic and syntactic LF profile subsequently used for sample generation. The outputs were manually evaluated by specialists in Explanatory Combinatorial Lexicology.The results show that the few-shot strategy captures some central semantic properties of several LFs, while profile refinement generally yields higher accuracy and greater semantic coherence, especially for semantically complex LFs. The acquired LF profiles were then used to prompt ChatGPT to construct a collocation-dictionary entry for Fear, modelled on the format of the Oxford Collocations Dictionary (OCD). The comparison shows that LF-guided prompting favours semantic differentiation and explicit structural organization by sense and semantic function, whereas the OCD entry displays greater idiomatic selectivity and lexicographic compactness. Overall, LF-guided interaction with GenAI appears to be a promising semi-automatic instrument for compiling semantically structured collocation resources that can support dictionary construction, although expert validation, generalization, and post-editing remain necessary.