Summary of Neurosymbolic Ai For Enhancing Instructability in Generative Ai, by Amit Sheth et al.
Neurosymbolic AI for Enhancing Instructability in Generative AI
by Amit Sheth, Vishal Pallagani, Kaushik Roy
First submitted to arxiv on: 26 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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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 explores the limitations of Large Language Models (LLMs) in interpreting complex instructions and generalizing them to novel tasks. Despite advancements in instruction tuning, LLMs still struggle to consistently follow multi-step instructions. The authors propose a neurosymbolic AI approach that decomposes high-level instructions into structured tasks, grounds these tasks into executable actions using a neural semantic parser, and implements these actions with a neuro-symbolic executor while maintaining an explicit state representation. This approach aims to enhance the reliability and context-awareness of task execution, allowing LLMs to respond more precisely and flexibly to various instructional contexts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper looks at how well computers can follow instructions. Even though they’ve gotten better at understanding simple commands, they still have trouble with longer instructions that need multiple steps. The authors suggest a new way of using artificial intelligence (AI) that breaks down complex tasks into smaller, easier-to-understand actions. This helps the AI machines work more reliably and make better decisions in different situations. |
Keywords
» Artificial intelligence » Instruction tuning
