Knowledge graph (KG) reasoning infers new knowledge from existing facts and is widely applied in question answering, recommendation, and decision support. With the rapid development of large language models (LLMs), LLM-based KG reasoning frameworks have become increasingly popular by leveraging retrieved KG information. However, hallucinations in LLMs remain a critical issue. Even when relevant KG knowledge is incorporated, models may still generate incorrect outputs, leading to misinformation and unreliable decisions. Existing hallucination detection methods either focus on LLM internal states or verify consistency with retrieved contexts, but both overlook the structural information in KGs, resulting in suboptimal performance. To address this gap, we propose LUCID, the first halLUcination deteCtIon method for LLM-based knowleDge graph reasoning frameworks. LUCID extracts node and edge features from attention scores and semantic similarities, and integrates them with KG structure using a graph neural network (GNN). Moreover, we also construct manually annotated benchmark datasets for evaluation. Experiments on nine datasets show that LUCID achieves state-of-the-art performance compared to 15 baselines. The code and data are available at: https://github.com/BUPT-GAMMA/LUCID.

Multimodal machine translation (MMT) integrates visual information to address ambiguity and contextual limitations in neural machine translation (NMT). Some empirical studies have revealed that many MMT models underutilize visual data during translation. They attempt to enhance cross-modal interactions to enable better exploitation of visual data. However, they only focus on simple interactions between nouns in text and corresponding entities in image, overlooking global semantic alignment, particularly for prepositional phrases and verbs in text which are more likely to be translated incorrectly. To address this, we design a Text-Image In-depth Questioning method to deepen interactions and optimize translations. Furthermore, to mitigate errors arising from contextually irrelevant image noise, we propose a Consistency Constraint strategy to improve our approach’s robustness. Our approach achieves state-of-the-art results on five translation directions of Multi30K and AmbigCaps, with +2.35 BLEU on the challenging MSCOCO benchmark, validating our method’s effectiveness in utilizing visual data and capturing comprehensive textual semantics.