ElevateMind: Your Digital Mental Health Al

Empathetic Virtual Chatbot for Mental Health Support

Facing the global challenge of mental health, which accounts for 8 million annual deaths and leaves over 28 million in the U.S. without adequate care, we introduce a novel solution: an empathetic virtual chatbot. This tool is designed to provide accessible and empathetic support, bridging the gap in mental healthcare.

Our Approach

Our mission is clear: to offer immediate, compassionate support through advanced technology. Here's what we're doing:

📊 Dataset Optimization

• BERT Summarization: Condensing chatbot interactions into efficient, context-rich summaries within a 512-token limit.

🤖 Chatbot Development

• Fine-Tuning LLaMA 2 [8]: Creating a chatbot that excels in empathetic and engaging conversations.

🔍 Benchmarking Efficiency

• Model Evaluation: Evaluating efficiency quantitatively and qualitatively with cosine similarity as Correlation metric and Dialog Topic, Lexical and Semantic Features as Diversity metric.

Join us in revolutionizing mental health care, making empathetic support accessible to all.

Dataset

• Data Source: Experiment utilizes a HuggingFace Dataset with 850K conversations between users (usr) in distress and a system (sys) acting as a counselor.
• Data Selection: A subset of 25,000 conversations is chosen due to compute constraints.

Dataset Transformation and Preprocessing

• Conversion to Instruct-based Format: Multi-turn dataset is transformed to suit LLaMA 2, a prompt-based model. This involves summarizing long conversations.
• Preprocessing Steps:
  • Conversations are merged into a single string using escape characters.
  • BERT Sentence Summarizer creates summarized contexts from past conversations.

Model Selection and Constraints

• Model Choice: The LLaMA-2-7B-HF variant is selected for its feasibility within compute constraints.
• Context Token Length Constraint: Memory limitations of Nvidia Tesla V100 GPUs make the LLaMA "chat" version infeasible, leading to the use of preprocessing techniques.

Training

• Memory Constraints and Solutions:
  • LLaMA-7B-Instruct requires up to 112 GB of memory, exceeding the 16 GB VRAM of Nvidia Tesla V100 GPUs.
  • Techniques like 4-bit quantized Low-Rank Adaptation (LoRA) and gradient accumulation are used to reduce memory footprint.
• Framework: The Ludwig framework, based on PyTorch, is used for training.
• Hyperparameters: [Insert additional information about hyperparameters here]

Baseline Setup:

• Zeroshot approach of asking emotional support questions to LLaMA model without finetuning.

Conversation Generation

• Generation Procedure: A user-interactive conversation prompt is created with options of using previous context or not. Feel free to try our eval.py and eval_wo_con.py for trying this user-friendly experience.
• Execution: This prompt given by user is inputted into the fine-tuned LLaMA model for multi-turn conversations with users.

Evaluation

• Qualitative Evaluation: Conversations generated by each model was thoroughly analysed for qualitative understanding.
• Quantitative Evaluation:
  • Correlation Metric: Cosine similarity of sentence BERT embeddings of our generated conversation between help-seeker and our chatbot.
  • Diversity Metric: Dialog topic, Lexical and Semantic features