The integration of large language models (LLMs) into clinical medicine represents a major advancement in natural language processing (NLP). We introduce BioMistral-Clinical 7B, a clinical LLM built on BioMistral-7B (Labrak et al., 2024), designed to support continual learning from unstructured clinical notes for real-world tasks such as clinical decision support. Using the augmented-clinical-notes dataset provided by Hugging Face (2024), we apply prompt engineering to transform unstructured text into structured JSON, capturing key clinical information (symptoms, diagnoses, treatments, outcomes). This enables efficient incremental training via self-supervised continual learning (SPeCiaL) (Caccia and Pineau, 2021). Evaluation on MedQA (Jin et al., 2021) and MedMCQA (Pal et al., 2022) shows that BioMistral-Clinical 7B improves accuracy on MedMCQA by nearly 10 points (37.4% vs. 28.0%) over the base model, while maintaining comparable performance on MedQA (34.8% vs. 36.5%). Building on this, we propose the BioMistral-Clinical System, which integrates Retrieval-Augmented Generation (RAG) (Lewis et al., 2020) to enrich responses with relevant clinical cases retrieved from a structured vector database. The full system enhances clinical reasoning by combining domain-specific adaptation with contextual retrieval.

Due to the rapid upgrade of social platforms, most of today’s fake news is published and spread in a multi-modal form. Most existing multi-modal fake news detection methods neglect the fact that some label-specific features learned from the training set cannot generalize well to the testing set, thus inevitably suffering from the harm caused by the latent data bias. In this paper, we analyze and identify the psycholinguistic bias in the text and the bias of inferring news label based on only image features. We mitigate these biases from a causality perspective and propose a Causal intervention and Counterfactual reasoning based Debiasing framework (CCD) for multi-modal fake news detection. To achieve our goal, we first utilize causal intervention to remove the psycholinguistic bias which introduces the spurious correlations between text features and news label. And then, we apply counterfactual reasoning by imagining a counterfactual world where each news has only image features for estimating the direct effect of the image. Therefore we can eliminate the image-only bias by deducting the direct effect of the image from the total effect on labels. Extensive experiments on two real-world benchmark datasets demonstrate the effectiveness of our framework for improving multi-modal fake news detection.