Figurative language conveys stance, emotion, and social nuance, making its appropriate use essential in dialogue. While large language models (LLMs) often succeed in recognizing figurative expressions at the sentence level, their ability to use them coherently in conversation remains uncertain. We introduce FLUID QA, the first multilingual benchmark that evaluates figurative usage in dialogue across English, Korean, and Chinese. Each item embeds figurative choices into multi-turn contexts. To support interpretation, we include FLUTE-bi, a sentence-level diagnostic task. Results reveal a persistent gap: models that perform well on FLUTE-bi frequently fail on FLUID QA, especially in sarcasm and metaphor. These errors reflect systematic rhetorical confusion and limited discourse reasoning. FLUID QA provides a scalable framework for assessing usage-level figurative competence across languages.

This study investigates the feasibility of automating political discourse analysis using large language models (LLMs), with a focus on 87 editorials from Rodong Sinmun, North Korea’s official newspaper. We introduce a structured analytical framework that integrates Chain-of-Thought prompting for claim–evidence extraction and a GPT-4o–based automated evaluation system (G-Eval). Experimental results demonstrate that LLMs possess emerging discourse-level reasoning capabilities, showing notably improved alignment with expert analyses under one-shot prompting conditions. However, the models often reproduced ideological rhetoric uncritically or generated interpretive hallucinations, highlighting the risks of fully automated analysis. To address these issues, we propose a Hybrid Human-in-the-Loop evaluation framework that combines expert judgment with automated scoring. This study presents a novel approach to analyzing politically sensitive texts and offers empirical insights into the quantitative assessment of ideological discourse, underscoring the scalability and potential of automation-driven methodologies.