Summary of Bootstrapping Cognitive Agents with a Large Language Model, by Feiyu Zhu et al.
Bootstrapping Cognitive Agents with a Large Language Model
by Feiyu Zhu, Reid Simmons
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: 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 This paper proposes a framework that combines the strengths of large language models (LLMs) and cognitive architectures. LLMs contain noisy general knowledge but are difficult to train or fine-tune, while cognitive architectures have excellent interpretability but require manual work to instantiate. The authors bootstrapped a cognitive-based model using the noisy knowledge encoded in LLMs and tested it through an embodied agent performing kitchen tasks. Results show that their framework yields better efficiency compared to an agent solely based on LLMs. The study indicates that LLMs are a valuable source of information for cognitive architectures, which can verify and update this knowledge to a specific domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper combines two types of AI models: large language models (LLMs) and cognitive architectures. LLMs have lots of general knowledge but are hard to work with. Cognitive architectures are great at understanding what they’re doing, but take a lot of effort to set up. The authors mixed the best parts of both and tested it by having an “agent” do household tasks. They found that their combination works better than just using LLMs alone. This shows that LLMs can help cognitive architectures learn new things, and vice versa. |
