Revolutionizing Finance with LLMs: An Overview of Applications and Insights

OpenAI has responded to a lawsuit alleging that its chatbot, ChatGPT, was responsible for coaching a 16-year-old to commit suicide, asserting that the AI had encouraged the teenager to seek help over 100 times. The company maintains that the chatbot's interactions were not to blame for the tragic outcome.

Users are experiencing a disconnection from reality during extended interactions with AI chatbots like ChatGPT, raising concerns about the psychological effects of such technology. Reports indicate that some individuals have developed delusions or suicidal thoughts after engaging with these systems for prolonged periods.

Google has emerged as a significant player in the global artificial intelligence race, particularly following the launch of its new AI model, Gemini 3, which analysts claim has outperformed competitors like ChatGPT in benchmark tests. This shift comes after years of criticism regarding Google's perceived lag in AI development since the debut of ChatGPT three years ago.

Three years after the launch of ChatGPT by OpenAI, the stock market has experienced significant shifts, driven by a surge in interest and investment in artificial intelligence technologies. This AI mania has fundamentally altered trading patterns on Wall Street, reflecting a broader trend towards digital innovation in finance.

Large language models (LLMs) like ChatGPT are increasingly used in healthcare information retrieval, but they are prone to generating hallucinations—plausible yet incorrect information. A recent study, MedHalu, investigates these hallucinations specifically in healthcare queries, highlighting the gap between LLM performance in standardized tests and real-world patient interactions.

A recent study evaluated the mathematical reasoning capabilities of Large Language Models (LLMs) using the 2026 Korean College Scholastic Ability Test (CSAT) Mathematics section, ensuring a contamination-free evaluation environment. The research involved digitizing all 46 questions immediately after the exam's public release, allowing for a rigorous assessment of 24 state-of-the-art LLMs across various input modalities and languages.

A new framework called Mujica-MyGo has been proposed to enhance multi-agent Retrieval-Augmented Generation (RAG) systems, addressing the challenges of long context lengths in large language models (LLMs). This framework aims to improve multi-turn reasoning by utilizing a divide-and-conquer approach, which helps manage the complexity of interactions with search engines during complex reasoning tasks.

A recent study on Large Language Models (LLMs) highlights the challenge of context drift in multi-turn interactions, where a model's outputs may diverge from user goals over time. The research introduces a dynamical framework to analyze this drift, formalizing it through KL divergence and proposing a recurrence model to interpret its evolution. This approach aims to enhance the consistency of LLM responses across multiple conversational turns.