AI Evaluation

A First Look at Financial Data Analysis Using ChatGPT-4o ArXiv ID: ssrn-4849578 “View on arXiv” Authors: Unknown Abstract OpenAI’s new flagship model, ChatGPT-4o, released on May 13, 2024, offers enhanced natural language understanding and more coherent responses. In this paper, we Keywords: Large Language Models (LLMs), Natural Language Processing, Generative AI, AI Evaluation, Model Performance, Technology/AI Complexity vs Empirical Score Math Complexity: 4.0/10 Empirical Rigor: 6.5/10 Quadrant: Street Traders Why: The paper involves implementing and comparing specific financial models like ARMA-GARCH, indicating moderate-to-high implementation complexity, but the core mathematics is largely descriptive and comparative rather than novel. Empirical rigor is high due to the use of real datasets (CRSP, Fama-French) and direct backtesting comparisons against Stata. flowchart TD A["Research Goal: Evaluate ChatGPT-4o for Financial Data Analysis"] --> B["Methodology: Zero-shot vs. Chain-of-Thought"] B --> C["Input: Financial Statements & Market Data"] C --> D["Process: Text Generation & Sentiment Analysis"] D --> E["Output: Financial Predictions & Explanations"] E --> F["Key Findings: High Accuracy in NLP Tasks"] F --> G["Outcome: Strong Potential but Limited Numerical Reasoning"]

Construction of a Japanese Financial Benchmark for Large Language Models ArXiv ID: 2403.15062 “View on arXiv” Authors: Unknown Abstract With the recent development of large language models (LLMs), models that focus on certain domains and languages have been discussed for their necessity. There is also a growing need for benchmarks to evaluate the performance of current LLMs in each domain. Therefore, in this study, we constructed a benchmark comprising multiple tasks specific to the Japanese and financial domains and performed benchmark measurements on some models. Consequently, we confirmed that GPT-4 is currently outstanding, and that the constructed benchmarks function effectively. According to our analysis, our benchmark can differentiate benchmark scores among models in all performance ranges by combining tasks with different difficulties. ...