In mental health counseling, condensing dialogues into concise and relevant summaries (aka counseling notes) holds pivotal significance. Large Language Models (LLMs) exhibit remarkable capabilities in various generative tasks; however, their adaptation to domain-specific intricacies remains challenging, especially within mental health contexts. Unlike standard LLMs, mental health experts first plan to apply domain knowledge in writing summaries. Our work enhances LLMs' ability by introducing a novel planning engine to orchestrate structuring knowledge alignment. To achieve high-order planning, we divide knowledge encapsulation into two major phases: (i) holding dialogue structure and (ii) incorporating domain-specific knowledge. We employ a planning engine on Llama-2, resulting in a novel framework, PIECE. Our proposed system employs knowledge filtering-cum-scaffolding to encapsulate domain knowledge. Additionally, PIECE leverages sheaf convolution learning to enhance its understanding of the dialogue's structural nuances. We compare PIECE with 14 baseline methods and observe a significant improvement across ROUGE and Bleurt scores. Further, expert evaluation and analyses validate the generation quality to be effective, sometimes even surpassing the gold standard. We further benchmark PIECE with other LLMs and report improvement, including Llama-2 (+2.72%), Mistral (+2.04%), and Zephyr (+1.59%), to justify the generalizability of the planning engine.
Exploring the Efficacy of Large Language Models in Summarizing Mental Health Counseling Sessions: A Benchmark Study
Indian Institute of Technology Delhi（印度理工学院德里分校）；Indraprastha Institute of Information Technology Delhi（英德拉普拉斯塔信息技术学院）；YourDOST；All India Institute of Medical Sciences, Rishikesh（全印医学科学学院里希盖什分校）；All India Institute of Medical Sciences, New Delhi（全印医学科学学院新德里分校）
Beyond Empathy: Integrating Diagnostic and Therapeutic Reasoning with Large Language Models for Mental Health Counseling
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)（广东省人工智能与数字经济发展实验室）；SKL-IOTSC, CIS, University of Macau（澳门科学馆物联网与智慧城市研究中心）；Shandong University（山东大学）；Auckland University of Technology（技术大学（奥克兰））；University of Electronic Science and Technology of China（电子科技大学）
Ψ-Arena: Interactive Assessment and Optimization of LLM-based Psychological Counselors with Tripartite Feedback
Central South University（中南大学）；CoAI Group（CoAI小组）；DCST（国防科学技术大学）；IAI（人工智能院）；BNRIST（北京航空航天大学）；Tsinghua University（清华大学）；Lingxin AI（灵犀AI）；Fuxi AI Lab（伏羲AI实验室）；NetEase Inc.（网易公司）
Guiding Language Models to Be More Empathetic: Culturally Sensitive Mental Health Advice Generation Through Human-LLM Collaboration
Ahsanullah University of Science and Technology（阿山努拉科技大学）；Bangladesh University of Business and Technology（孟加拉国商业与技术大学）；The University of New South Wales（新南威尔士大学）；Jashore University of Science and Technology（贾绍尔科技大学）；American International University - Bangladesh（孟加拉国美国国际大学）
ReSum: Unlocking Long-Horizon Search Intelligence via Context Summarization
The Chinese University of Hong Kong（香港中文大学）；Tongyi Lab, Alibaba Group（阿里云实验室）；Penn State University（宾夕法尼亚州立大学）；The Hong Kong University of Science（香港科学大学）
Towards an Automated Multimodal Approach for Video Summarization: Building a Bridge Between Text, Audio and Facial Cue-Based Summarization
Center for Machine Vision and Signal Analysis, Faculty of ITEE, University of Oulu, Finland（机器视觉与信号分析中心，ITEE学院，奥卢大学，芬兰）；Center for Machine Vision（机器视觉与信号分析中心）；Signal Analysis, Faculty of ITEE, University of Oulu, Finland（信号分析，ITEE学院，奥卢大学，芬兰）
Faithful or Fabricated? A Causal Framework for Rationalization Bias in LLM Judges
School of Computing and Electrical Engineering（计算与电子工程学院）；Indian Institute of Technology (IIT) Mandi（印度理工学院（IIT）曼迪）；London U.K.（伦敦英国）；Warwick Manufacturing Group U.K.（沃里克制造集团英国）
KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approaches
Rice University（莱斯大学）；Case Western Reserve University（凯斯西储大学）；Texas A&M University（德克萨斯农工大学）；Stevens Institute of Technology（史蒂文斯理工学院）；University of Minnesota Twin Cities（明尼苏达大学双城分校）
HelloBench: Evaluating Long Text Generation Capabilities of Large Language Models
Beihang University（北京航空航天大学）；Shanghai AI Laboratory（上海人工智能实验室）；Peking University（北京大学）；M-A-P；University of Chinese Academy of Sciences（中国科学院大学）；Nanjing University（南京大学）
Columbia University（哥伦比亚大学）；Institute for Clarity in Documentation（清晰文档研究所）；Inria Paris-Rocquencourt（Inria巴黎- Rocquencourt研究所）；Rajiv Gandhi University（拉贾·甘地大学）；Tsinghua University（清华大学）；Palmer Research Laboratories（帕勒姆研究实验室）
Prompt Programming for Cultural Bias and Alignment of Large Language Models
Information Systems and Modeling, Los Alamos National Laboratory（信息系统与建模，洛斯阿拉莫斯国家实验室）；Advanced Research in Cyber Systems, Los Alamos National Laboratory（网络安全系统高级研究，洛斯阿拉莫斯国家实验室）；Center for National Security and International Studies（国家安全与国际研究中心）；Analytics Division, Los Alamos National Laboratory（分析部门，洛斯阿拉莫斯国家实验室）
Text2Mem: A Unified Memory Operation Language for Memory Operating System
MemTensor (Shanghai) Technology（MemTensor（上海）技术有限公司）；University of Oxford（牛津大学）；National University of Singapore（新加坡国立大学）；Shanghai Jiao Tong University（上海交通大学）
