Large vision-language models (LVLMs) have gained widespread attention in the medical field for their outstanding capability in handling image-text representations. However, the misalignment between medical images and clinical text presents factuality challenges for Medical LVLMs (Med-LVLMs), often resulting in hallucinations. Multimodal Chain-of-Thought (MCoT) can reduce factual errors of Med-LVLMs by encouraging explicit step-by-step reasoning, but it poses two major challenges. First, factually accurate medical rationales are crucial for aligning medical images with the corresponding clinical texts, yet existing Med-LVLMs struggle to generate such rationales. Second, if the model's initial prediction is correct, its inherent knowledge can be disrupted by an over-reliance on the generated medical rationale, resulting in an incorrect answer. To address these challenges, we propose MMReT, a novel multimodal medical reasoning tuning approach designed to improve the factual accuracy of Med-LVLMs. First, we employ carefully designed prompts to guide GPT-4o in generating high-quality medical rationales, which are then used to fine-tune the original Med-LVLM. Second, to address errors stemming from excessive reliance on generated rationales, we introduce medical reasoning preference fine-tuning, which encourages the model to maintain an appropriate balance between leveraging its inherent knowledge and incorporating generated medical rationale. Experimental results show that MMReT substantially enhances the factuality of Med-LVLMs, outperforming previous methods with average improvements of 8.0% on VQA-RAD and 11.6% on SLAKE in factual accuracy.

The recently proposed learned bloom filter (LBF) opens a new perspective on how to reconstruct bloom filters with machine learning. However, the LBF has a massive time cost and does not apply to multidimensional spatial data. In this paper, we propose a prefix‐based and adaptive learned bloom filter (PA‐LBF) for spatial data, which efficiently supports the insertion and deletion. The proposed PA‐LBF is divided into three parts: (1) the prefix‐based classification. The Z‐order space‐filling curve is used to extract data, prefix it, and classify it. (2) The adaptive learning process. The multiple independent adaptive sub‐LBFs are designed to train the suffixes of data, combined with part 1, to reduce the false positive rate (FPR), query, and learning process time consumption. (3) The backup filter uses CBF. Two kinds of backup CBF are constructed to meet the situation of different insertion and deletion frequencies. Experimental results prove the validity of the theory and show that the PA‐LBF reduces the FPR by 84.87%, 79.53%, and 43.01% with the same memory usage compared with the LBF on three real‐world spatial datasets. Moreover, the time consumption of PA‐LBF can be reduced to 5× and 2.05× that of the LBF on the query and learning process, respectively.

Document-level relation extraction intends to extract relation facts among different entity pairs in the entire document. Previously proposed methods make numerous efforts for this task, however, these researches neglect to treat the two entities in an entity pair as an organic unity for relation extraction, which leads to the lack of valuable information about the entity pairs and insufficient information interaction. To tackle the above problem, we propose a framework, named Entity-level Attention Pooling and Information Gating (EAPIG), for document-level relation extraction. Specifically, we first utilize an encoder module to capture the long-distance dependencies of entities in the document, and then we propose two modules: the Entity-level Attention Pooling module obtains the local information of entity pairs, and the Information Gating module introduces the global information of entity pairs and promotes sufficient interaction between the local information and the global information. Experimental results on the benchmark dataset DocRED show that our approach can efficiently capture and combine the abundant information from the entity pairs to achieve better performance than the previous baselines.