Summary of Rap: Retrieval-augmented Planning with Contextual Memory For Multimodal Llm Agents, by Tomoyuki Kagaya et al.
RAP: Retrieval-Augmented Planning with Contextual Memory for Multimodal LLM Agents
by Tomoyuki Kagaya, Thong Jing Yuan, Yuxuan Lou, Jayashree Karlekar, Sugiri Pranata, Akira Kinose, Koki Oguri, Felix Wick, Yang You
First submitted to arxiv on: 6 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 proposed Retrieval-Augmented Planning (RAP) framework aims to enhance Large Language Models’ (LLMs) planning capabilities by dynamically leveraging past experiences relevant to the current situation. This versatile approach excels in both text-only and multimodal environments, making it suitable for a wide range of tasks. Empirical evaluations demonstrate RAP’s effectiveness, achieving state-of-the-art performance in textual scenarios and enhancing multimodal LLM agents’ performance for embodied tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The RAP framework helps computers make better decisions by using past experiences to plan for the future. It works well in different environments, like text-only or combining text with pictures. This makes it useful for many applications. The researchers tested RAP and found that it did a great job, outperforming others in some cases. |
