Large Language Models (LLMs) excel in general tasks but struggle with domain-specific challenges, such as specialized terminology and localized regulations. Existing financial LLMs, like FinGPT and BloombergGPT, lack support for the Thai financial domain. We developed a Thai Financial LLM using the Investment Consultant (IC) exam dataset from the Stock Exchange of Thailand. To address dataset limitations, we applied data augmentation, ReLoRA for efficient training, Continued Pretraining (CPT) for domain knowledge, and Rank-Stabilized LoRA (rsLoRA) for fine-tuning. Supervised Fine-Tuning (SFT) simulated exam scenarios, while Direct Preference Optimization (DPO) refined the model using feedback. The model achieved scores of 72%, 72%, and 84% on IC exam levels P1, P2, and P3, respectively, demonstrating its effectiveness in Thai financial advisory tasks and its potential for specialized applications.

We introduce "Future You," an interactive, brief, single-session, digital chat intervention designed to improve future self-continuity--the degree of connection an individual feels with a temporally distant future self--a characteristic that is positively related to mental health and wellbeing. Our system allows users to chat with a relatable yet AI-powered virtual version of their future selves that is tuned to their future goals and personal qualities. To make the conversation realistic, the system generates a "synthetic memory"--a unique backstory for each user--that creates a throughline between the user's present age (between 18-30) and their life at age 60. The "Future You" character also adopts the persona of an age-progressed image of the user's present self. After a brief interaction with the "Future You" character, users reported decreased anxiety, and increased future self-continuity. This is the first study successfully demonstrating the use of personalized AI-generated characters to improve users' future self-continuity and wellbeing.

Large Language Models (LLMs) have emerged as leading tools in Natural Language Processing (NLP) due to their exceptional performance across various tasks. The advent of open-source models such as Llama [1] from Meta, Gemma [2] from Google, and Qwen [3] from Alibaba has significantly enhanced public access to advanced LLMs. Additionally, low-cost techniques for LLM fine-tuning, such as Low-rank Adaptation (LoRA) [4], have enabled the fine-tuning of these models on consumer-grade hardware, thereby accelerating their development and adoption. LLMs are now utilized in a wide array of applications, ranging from personal assistants, i.e., ChatGPT, to specialized tasks in diverse domains. In the financial sector, BloombergGPT [5], a proprietary LLM trained from the ground up with an infusion of financial data, has demonstrated superior performance on financial benchmarks compared to other models in the market.