Summary of A Review Of Prominent Paradigms For Llm-based Agents: Tool Use (including Rag), Planning, and Feedback Learning, by Xinzhe Li
A Review of Prominent Paradigms for LLM-Based Agents: Tool Use (Including RAG), Planning, and Feedback Learning
by Xinzhe Li
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Software Engineering (cs.SE)
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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 presents a unified taxonomy to review and discuss frameworks for developing Large Language Model (LLM)-based agents across various tasks, including tool use, planning, and feedback learning. The authors aim to address the challenges posed by inconsistent workflows and taxation in these frameworks. Specifically, they define environments/tasks, LLM-profiled roles or LMPRs (policy models, evaluators, and dynamic models), and universally applicable workflows found in prior work. This allows for a comparison of key perspectives on the implementations of LMPRs and workflow designs across different agent paradigms and frameworks. The authors identify three limitations in existing workflow designs and systematically discuss future work. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make computers smarter by giving them instructions on what to do. There are many ways to teach a computer, but the problem is that they’re all different and confusing. So, this paper creates a system to group these methods together and show which ones work best for different tasks. It also shows where we can improve and what we need to do next. |
Keywords
» Artificial intelligence » Large language model
