Summary of Cognitive Llms: Towards Integrating Cognitive Architectures and Large Language Models For Manufacturing Decision-making, by Siyu Wu et al.
Cognitive LLMs: Towards Integrating Cognitive Architectures and Large Language Models for Manufacturing Decision-making
by Siyu Wu, Alessandro Oltramari, Jonathan Francis, C. Lee Giles, Frank E. Ritter
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Symbolic Computation (cs.SC)
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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 presents a new neuro-symbolic architecture called LLM-ACTR, which aims to bridge the gap between cognitive architectures and large language models (LLMs). The goal is to enable reliable machine reasoning capabilities in production systems. By integrating the ACT-R Cognitive Architecture with LLMs, the framework extracts and embeds knowledge of internal decision-making processes as latent neural representations, injects this information into trainable adapter layers, and fine-tunes the LLMs for downstream prediction. Experiments on novel Design for Manufacturing tasks show improved task performance and grounded decision-making capability compared to LLM-only baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to combine different types of artificial intelligence (AI) models. The goal is to make AI more human-like by letting it reason slowly and carefully, like humans do. Right now, some AI models are good at making quick decisions, but they’re not very good at thinking things through or understanding the context. Other AI models are great at thinking critically, but they’re not as good at making quick decisions. The new system brings together two types of AI models to create a better way of reasoning that’s more like how humans think. |
