Jingxian Liao

Hsin-Yu Tsai, Jingxian Liao, Fu-Yin Cherng et al.

The excessive use of social media has led to the challenge known as Fear of Missing Out (FoMO). Existing studies fail to provide accessible, interactive tools that focus on the emotional and cognitive aspects of FoMO. This work presents Moodie, a chatbot designed using Large Language Models to support emotion regulation and reduce FoMO. We conducted a formative study to understand the needs of individuals with FoMO and developed Moodie. Then, we conducted a preliminary evaluative study (N=21) to observe how participants interact with Moodie and a baseline chatbot (GPT-4o) over one week. The results show that while both Moodie and a baseline chatbot reduced FoMO to a similar extent, Moodie resulted in greater engagement and social connection. This finding raises interesting questions about the advantages of purpose-built chatbots compared to general-purpose models for mental health support. Future research will include chat log analysis, prototype refinements, and longitudinal evaluations.

Jianxing Liao, Junyan Xu, Yatao Sun et al.

Designing complex computer-aided design (CAD) models is often time-consuming due to challenges such as computational inefficiency and the difficulty of generating precise models. We propose a novel language-guided framework for industrial design automation to address these issues, integrating large language models (LLMs) with computer-automated design (CAutoD).Through this framework, CAD models are automatically generated from parameters and appearance descriptions, supporting the automation of design tasks during the detailed CAD design phase. Our approach introduces three key innovations: (1) a semi-automated data annotation pipeline that leverages LLMs and vision-language large models (VLLMs) to generate high-quality parameters and appearance descriptions; (2) a Transformer-based CAD generator (TCADGen) that predicts modeling sequences via dual-channel feature aggregation; (3) an enhanced CAD modeling generation model, called CADLLM, that is designed to refine the generated sequences by incorporating the confidence scores from TCADGen. Experimental results demonstrate that the proposed approach outperforms traditional methods in both accuracy and efficiency, providing a powerful tool for automating industrial workflows and generating complex CAD models from textual prompts. The code is available at https://jianxliao.github.io/cadllm-page/