A5110247102- Mental Health via Writing
- Topic Modeling
- Machine Learning in Healthcare
- Mental Health Research Topics
- FinTech, Crowdfunding, Digital Finance
- Neural Networks and Applications
- Music and Audio Processing
- Sentiment Analysis and Opinion Mining
- Fuzzy Logic and Control Systems
- Advanced Text Analysis Techniques
- Traditional Chinese Medicine Studies
- Web Data Mining and Analysis
- Speech Recognition and Synthesis
- Health, Environment, Cognitive Aging
- Natural Language Processing Techniques
Jiangxi Normal University
2023-2024
University of Manchester
2024
Wuhan University
2024
Guangzhou University
2000
The latest large language models (LLMs) such as ChatGPT, exhibit strong capabilities in automated mental health analysis. However, existing relevant studies bear several limitations, including inadequate evaluations, lack of prompting strategies, and ignorance exploring LLMs for explainability. To bridge these gaps, we comprehensively evaluate the analysis emotional reasoning ability on 11 datasets across 5 tasks. We explore effects different strategies with unsupervised distantly supervised...
As an integral part of people's daily lives, social media is becoming a rich source for automatic mental health analysis.As traditional discriminative methods bear poor generalization ability and low interpretability, the recent large language models (LLMs) have been explored interpretable analysis on media, which aims to provide detailed explanations along with predictions in zero-shot or few-shot settings.The results show that LLMs still achieve unsatisfactory classification performance...
LLMs have transformed NLP and shown promise in various fields, yet their potential finance is underexplored due to a lack of thorough evaluations the complexity financial tasks. This along with rapid development LLMs, highlights urgent need for systematic evaluation benchmark LLMs. In this paper, we introduce FinBen, first comprehensive open-sourced benchmark, specifically designed thoroughly assess capabilities domain. FinBen encompasses 35 datasets across 23 tasks, organized into three...
With the development of web technology, social media texts are becoming a rich source for automatic mental health analysis. As traditional discriminative methods bear problem low interpretability, recent large language models have been explored interpretable analysis on media, which aims to provide detailed explanations along with predictions. The results show that ChatGPT can generate approaching-human its correct classifications. However, LLMs still achieve unsatisfactory classification...
A neural network system which combines a self-organizing feature map and multilayer perception for the problem of isolated word speech recognition is presented. new method combining self-organization learning K-means clustering used training map, an efficient adaptive nearby-search coding based on 'locality' designed. The shown to save about 50% computation without degradation in rate compared full-search coding. Various experiments different choices parameters were conducted TI 20 database...
Large Language Models (LLMs) can play a vital role in psychotherapy by adeptly handling the crucial task of cognitive reframing and overcoming challenges such as shame, distrust, therapist skill variability, resource scarcity. Previous LLMs mainly converted negative emotions to positive ones, but these approaches have limited efficacy, often not promoting clients' self-discovery alternative perspectives. In this paper, we unveil Helping Empowering through Adaptive Mental Enhancement (HealMe)...
Recent advancements in large language models (LLMs) aim to tackle heterogeneous human expectations and values via multi-objective preference alignment. However, existing methods are parameter-adherent the policy model, leading two key limitations: (1) high-cost repetition of their alignment algorithms for each new target model; (2) they cannot expand unseen objectives due static objectives. In this work, we propose Meta-Objective Aligner (MetaAligner), a model that performs conditional...