Summary of Multi-agent Software Development Through Cross-team Collaboration, by Zhuoyun Du et al.
Multi-Agent Software Development through Cross-Team Collaboration
by Zhuoyun Du, Chen Qian, Wei Liu, Zihao Xie, Yifei Wang, Yufan Dang, Weize Chen, Cheng Yang
First submitted to arxiv on: 13 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA); 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 The abstract discusses the limitations of Large Language Models (LLMs) in generating software autonomously. While LLMs can collaborate like humans, each phase yields only one possible outcome, leading to suboptimal results. To address this, the authors propose Cross-Team Collaboration (CTC), a framework enabling orchestrated teams to jointly propose decisions and communicate insights for superior content generation. Experimental results show significant quality improvements over state-of-the-art baselines, demonstrating the efficacy of CTC in software development. The generalization ability across domains is also promising, with implications for LLM agent growth in areas beyond software development. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are helping computers generate code on their own. This is great news! However, there’s a problem. When multiple agents work together, they only come up with one possible solution. What if we could explore more options? Researchers created a new way to let teams of LLMs collaborate and share ideas. This helps them find better solutions. They tested this approach on software development and found that it produces much better results than current methods. This could be useful in many areas, not just coding. |
Keywords
» Artificial intelligence » Generalization
