The ditlab team participated in the RAG and DMR tasks of the NTCIR-18 Transfer-2 task. For the RAG task, we proposed a late fusion method for answer generation that uses multiple contexts retrieved by the dense passage retriever. Unlike sequential approaches that input contexts sequentially into large language models (LLM), our method processes contexts in parallel and employs majority voting to determine the final answer. We also fine-tuned the LLM using a LoRA-based method to better handle quiz-style questions, achieving over 10 points gains against the baseline in terms of accuracy.For the DMR task, we introduce a modality-aware sensor encoder that processes numerical and textual sensor features separately, and enhance geolocation features by converting latitude/longitude data into address strings via k-nearest neighbor matching. Although our baseline performance is degraded from the official baseline due to the mismatch of data between the training and evaluation data, our approach improved the image-to-sensor retrieval performance from our baseline.
Tachioka et al. (2025) studied this question.