Summary of Hello Again! Llm-powered Personalized Agent For Long-term Dialogue, by Hao Li et al.
Hello Again! LLM-powered Personalized Agent for Long-term Dialogue
by Hao Li, Chenghao Yang, An Zhang, Yang Deng, Xiang Wang, Tat-Seng Chua
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The Open-domain dialogue system has made significant advancements with the development of large language models (LLMs), but most existing systems focus on brief single-session interactions, neglecting real-world demands for long-term companionship and personalized interactions. To address this need, an event summary and persona management are crucial, enabling reasoning for appropriate long-term dialogue responses. Recent progress in human-like cognitive and reasoning capabilities of LLMs suggests that LLM-based agents could significantly enhance automated perception, decision-making, and problem-solving. The Long-term Dialogue Agent (LD-Agent) is introduced, incorporating three independently tunable modules: event perception, persona extraction, and response generation. The LD-Agent employs long and short-term memory banks to focus on historical and ongoing sessions and a topic-based retrieval mechanism to enhance memory retrieval accuracy. The persona module conducts dynamic persona modeling for both users and agents. Retrieved memories and extracted personas are integrated into the generator to induce appropriate responses. Empirical demonstrations of effectiveness, generality, and cross-domain capabilities across various benchmarks, models, and tasks are provided. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about building a better chatbot that can have long conversations with people. Right now, most chatbots are only good for short interactions, but we want to create one that can be your friend for hours or even days. To do this, we need a system that can remember what happened earlier in the conversation and adjust its responses accordingly. We also need to figure out who the person is talking to (the persona) and respond appropriately. The Long-term Dialogue Agent (LD-Agent) is our solution, which uses three separate modules to manage events, personas, and responses. It’s like a super smart AI that can learn and adapt to different situations. |
