Do multi-agent LLMs improve robot interaction?
Two Heads Are Better Than One: Collaborative LLM Embodied Agents for Human-Robot Interaction
This research explores whether multiple LLMs working together (collaborative AI) can control a robot better than a single LLM. They tested three setups: one single LLM, two LLMs (coder and reviewer), and three LLMs (planner, coder, and reviewer). While the two-LLM setup was the most reliable (fewest errors), overall, there wasn't a significant performance difference between the multi-agent and single-agent systems across all tasks. Interestingly, the three-LLM system performed worse than expected, possibly due to increased communication overhead and LLM "forgetfulness" with longer prompts. The two-LLM system excelled in more complex, abstract tasks, while the single LLM was sometimes better at simple problem-solving. The study suggests that simply adding more LLMs doesn't guarantee better performance, and collaborative architecture matters greatly. It highlights the potential for retrieval augmented generation (RAG) to address context window limitations.