Summary of Reasoning Capacity in Multi-agent Systems: Limitations, Challenges and Human-centered Solutions, by Pouya Pezeshkpour et al.
Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions
by Pouya Pezeshkpour, Eser Kandogan, Nikita Bhutani, Sajjadur Rahman, Tom Mitchell, Estevam Hruschka
First submitted to arxiv on: 2 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 In this paper, researchers explore ways to improve the practical adoption of large language models (LLMs) in real-world scenarios. They propose multi-agent systems as a solution to integrate LLMs with existing proprietary data and models. However, current approaches focus on single objectives for optimization, neglecting potential constraints like budget, resources, or time. The authors introduce the concept of reasoning capacity as a unifying criterion to enable constraint integration during optimization and holistic evaluation. They define reasoning capacity formally and demonstrate its utility in identifying limitations within system components. The paper argues that human feedback can be used to address these shortcomings and enhance consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are really good at many tasks, but they need to work better with other tools and data in real life. One way to do this is by using special systems that connect different parts together. These systems are called multi-agent systems. Right now, people make them focus on just one goal, but that doesn’t account for things like money, time, or resources being limited. The researchers want to change this by creating a new idea called “reasoning capacity.” This helps make the system work better with what it has and understand its own strengths and weaknesses. They show how this can help fix problems in the system and make it more reliable. |
Keywords
* Artificial intelligence * Optimization
