Analysing Reference Production of Large Language Models
Published in Proceedings of the 18th International Natural Language Generation Conference, 2025
This study investigates how large language models (LLMs) produce referring expressions (REs) and to what extent their behaviour aligns with human patterns. We evaluate LLM performance in two settings: slot filling, the conventional task of referring expression generation, where REs are generated within a fixed context, and language generation, where REs are analysed within fully generated texts. Using the WebNLG corpus, we assess how well LLMs capture human variation in reference production and analyse their behaviour by examining the influence of several factors known to affect human reference production, including referential form, syntactic position, recency, and discourse status. Our findings show that (1) task framing significantly affects LLMs’ reference production; (2) while LLMs are sensitive to some of these factors, their referential behaviour consistently diverges from human use; and (3) larger model size does not necessarily yield more human-like variation. These results underscore key limitations in current LLMs’ ability to replicate human referential choices.