Summary of Emotion-aware Embedding Fusion in Llms (flan-t5, Llama 2, Deepseek-r1, and Chatgpt 4) For Intelligent Response Generation, by Abdur Rasool et al.
Emotion-Aware Embedding Fusion in LLMs (Flan-T5, LLAMA 2, DeepSeek-R1, and ChatGPT 4) for Intelligent Response Generation
by Abdur Rasool, Muhammad Irfan Shahzad, Hafsa Aslam, Vincent Chan, Muhammad Ali Arshad
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study addresses the challenge of enhancing emotional and contextual understanding in large language models (LLMs) for psychiatric applications like auto-mated chatbot-facilitated psychotherapy. The authors introduce Emotion-Aware Embedding Fusion, a novel framework combining hierarchical fusion, attention mechanisms, and multiple emotion lexicons to prioritize semantic and emotional features in therapy transcripts. They use state-of-the-art LLMs such as Flan-T5, LLAMA 2, DeepSeek-R1, and ChatGPT 4, as well as neural networks to segment therapy session transcripts into hierarchical levels (word, sentence, and session). The processed embeddings are stored in Facebook AI’s similarity search vector database, enabling efficient clustering across dense vector spaces. Upon user queries, relevant segments are retrieved and provided as context to LLMs, enhancing their ability to generate empathetic responses. The framework is evaluated across multiple practical use cases to demonstrate real-world applicability, including integrating the system into existing mental health platforms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps chatbots understand emotions better in therapy sessions. It develops a new way to combine information from different sources to prioritize emotional and contextual features. They use powerful language models like Flan-T5 and ChatGPT 4, along with special techniques to analyze therapy transcripts. The system can then provide personalized responses based on the context of the conversation. This technology has many practical uses, such as helping mental health platforms offer more empathetic support. |
Keywords
» Artificial intelligence » Attention » Clustering » Embedding » Llama » T5
