The task of financial analysis primarily encompasses two key areas: stock trend prediction and the corresponding financial question answering. Currently, machine learning and deep learning algorithms (ML&DL) have been widely applied for stock trend predictions, leading to significant progress. However, these methods fail to provide reasons for predictions, lacking interpretability and reasoning processes. Also, they can not integrate textual information such as financial news or reports. Meanwhile, large language models (LLM) have remarkable textual understanding and generation ability. But due to the scarcity of financial training datasets and limited integration with real-time knowledge, LLM still suffer from hallucinations and unable to keep up with the latest information. To tackle these challenges, we first release AlphaFin datasets, combining traditional research datasets, real-time financial data, and handwritten chain-of-thought (CoT) data. It has positive impact on training LLM for completing financial analysis. We then use AlphaFin datasets to benchmark a state-of-the-art method, called Stock-Chain, for effectively tackling the financial analysis task, which integrates retrieval-augmented generation (RAG) techniques. Extensive experiments are conducted to demonstrate the effectiveness of our framework on financial analysis.

Visual Question Answering (VQA) aims to answer a textual question based on a given image. Nevertheless, recent studies have shown that VQA models tend to capture the biases to answer the question, instead of using the reasoning ability, resulting in poor generalisation ability. To alleviate the issue, some existing methods consider the natural distribution of the data, and construct samples to balance the dataset, achieving remarkable performance. However, these methods may encounter some limitations: 1) rely on additional annotations, 2) the generated samples may be inaccurate, e.g., assigned wrong answers, and 3) ignore the power of positive samples. In this paper, we propose a method to Dig out Discrimination information from Generated samples (DDG) to address the above limitations. Specifically, we first construct positive and negative samples in vision and language modalities, without using additional annotations. Then, we introduce a knowledge distillation mechanism to promote the learning of the original samples by the positive samples. Moreover, we impel the VQA models to focus on vision and language modalities using the negative samples. Experimental results on the VQA-CP v2 and VQA v2 datasets show the effectiveness of our DDG.