Year: 2026
Type: conference-paper
Abstract: We investigate whether GPT-5 can generate literal usage examples for Japanese multiword expressions (MWEs), whose literal readings are structurally low-frequency in available corpora.Prior work on MWEs has largely focused on detecting idiomatic usages in context, leaving literal usages underrepresented particularly for Japanese MWEs whose literal readings are rare and structurally diverse.Because literal readings are rarely attested in corpora, we design a lexicon-grounded setup that uses corpus nonliteral usages as contrastive cues for controlled prompting.We evaluate the generated sentences using automatic literalness judgments and human literalness judgments, together with manual inspection.Our results show that providing contrastive non-literal information stabilizes literal generation and improves quality compared with prompts that include only literal information or no hints.The results indicate that Gemini-2.5-proaligns more closely with human judgments for idiomatic interpretations than for literal ones, highlighting the relative difficulty of modeling literal readings of MWEs.The study demonstrates that GPT-5 can complement existing resources by supplying frequency-independent literal examples and offers a controlled framework for examining contextual meaning understanding of Japanese MWEs.
Institution: Hitotsubashi University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166886415