Summary of From Llm to Conversational Agent: a Memory Enhanced Architecture with Fine-tuning Of Large Language Models, by Na Liu et al.
From LLM to Conversational Agent: A Memory Enhanced Architecture with Fine-Tuning of Large Language Models
by Na Liu, Liangyu Chen, Xiaoyu Tian, Wei Zou, Kaijiang Chen, Ming Cui
First submitted to arxiv on: 5 Jan 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 This paper introduces RAISE (Reasoning and Acting through Scratchpad and Examples), an advanced architecture that enhances the integration of Large Language Models like GPT-4 into conversational agents. The RAISE architecture incorporates a dual-component memory system, mirroring human short-term and long-term memory, to maintain context and continuity in conversations. This approach involves several phases, including Conversation Selection, Scene Extraction, CoT Completion, and Scene Augmentation, leading to the LLMs Training phase. Preliminary evaluations suggest that RAISE has some advantages over traditional agents, indicating its potential for broader applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make conversational agents smarter by using big language models like GPT-4. The new approach is called RAISE and it helps the agents keep track of what’s being talked about and remember things from earlier in the conversation. This makes the agents better at handling complex conversations that go back and forth many times. The researchers tested this new approach with a real estate sales scenario and found that it works better than traditional agents. |
Keywords
» Artificial intelligence » Gpt
