Yijing Xu

Qianqian Xie, Weiguang Han, Zhengyu Chen et al.

LLMs have transformed NLP and shown promise in various fields, yet their potential in finance is underexplored due to a lack of comprehensive evaluation benchmarks, the rapid development of LLMs, and the complexity of financial tasks. In this paper, we introduce FinBen, the first extensive open-source evaluation benchmark, including 36 datasets spanning 24 financial tasks, covering seven critical aspects: information extraction (IE), textual analysis, question answering (QA), text generation, risk management, forecasting, and decision-making. FinBen offers several key innovations: a broader range of tasks and datasets, the first evaluation of stock trading, novel agent and Retrieval-Augmented Generation (RAG) evaluation, and three novel open-source evaluation datasets for text summarization, question answering, and stock trading. Our evaluation of 15 representative LLMs, including GPT-4, ChatGPT, and the latest Gemini, reveals several key findings: While LLMs excel in IE and textual analysis, they struggle with advanced reasoning and complex tasks like text generation and forecasting. GPT-4 excels in IE and stock trading, while Gemini is better at text generation and forecasting. Instruction-tuned LLMs improve textual analysis but offer limited benefits for complex tasks such as QA. FinBen has been used to host the first financial LLMs shared task at the FinNLP-AgentScen workshop during IJCAI-2024, attracting 12 teams. Their novel solutions outperformed GPT-4, showcasing FinBen's potential to drive innovation in financial LLMs. All datasets, results, and codes are released for the research community: https://github.com/The-FinAI/PIXIU.

Mengxi Xiao, Zihao Jiang, Lingfei Qian et al.

Accurately forecasting stock price movements is critical for informed financial decision-making, supporting applications ranging from algorithmic trading to risk management. However, this task remains challenging due to the difficulty of retrieving subtle yet high-impact patterns from noisy financial time-series data, where conventional retrieval methods, whether based on generic language models or simplistic numeric similarity, often fail to capture the intricate temporal dependencies and context-specific signals essential for precise market prediction. To bridge this gap, we introduce FinSrag, the first retrieval-augmented generation (RAG) framework with a novel domain-specific retriever FinSeer for financial time-series forecasting. FinSeer leverages a candidate selection mechanism refined by LLM feedback and a similarity-driven training objective to align queries with historically influential sequences while filtering out financial noise. Such training enables FinSeer to identify the most relevant time-series data segments for downstream forecasting tasks, unlike embedding or distance-based retrieval methods used in existing RAG frameworks. The retrieved patterns are then fed into StockLLM, a 1B-parameter LLM fine-tuned for stock movement prediction, which serves as the generative backbone. Beyond the retrieval method, we enrich the retrieval corpus by curating new datasets that integrate a broader set of financial indicators, capturing previously overlooked market dynamics. Experiments demonstrate that FinSeer outperforms existing textual retrievers and traditional distance-based retrieval approaches in enhancing the prediction accuracy of StockLLM, underscoring the importance of domain-specific retrieval frameworks in handling the complexity of financial time-series data.