Hint recognition in Chinese and Russian diplomatic discourse using large language models

This study develops and evaluates a Large Language Model (LLM) system for hint recognition in Chinese and Russian diplomatic discourse by integrating a semantic-cognitive-pragmatic theoretical framework with Retrieval-Augmented Generation (RAG) and Chain-of-Thought (CoT) reasoning. Grounded in linguistic-pragmatic theory and employing discourse analysis, we systematically annotated real press-conference transcripts from the Chinese and Russian Ministries of Foreign Affairs, constructed a vectorized external knowledge base covering textual and logical hints, embedded CoT reasoning instructions into the prompts, and provided bilingual few-shot exemplars to guide LLM recognition. Experimental results demonstrate stable overall performance with consistently high recall across both corpora, with the Russian dataset achieving higher precision and F1 scores than the Chinese dataset. Error analysis reveals three major types of systematic bias in LLM hint recognition: semantic over-interpretation, hint-type misclassification, and literal-meaning misclassification. To address these problems, we propose several targeted improvements, including expanding negative samples with standardized "no-hint" diplomatic expressions, strengthening context anchoring to ensure pragmatic interpretations are grounded in discourse, introducing a repeated matching mechanism, calibrating sensitive trigger words, and introducing a pre-filtering and self-evaluation mechanism to better distinguish explicit statements from implicit meanings. This study provides a feasible pathway and practical guidance for improving the accuracy and stability of LLMs in multilingual, high-context implicit information recognition.