Summary of Proceedings Of the First International Workshop on Next-generation Language Models For Knowledge Representation and Reasoning (nelamkrr 2024), by Ken Satoh et al.
Proceedings of the First International Workshop on Next-Generation Language Models for Knowledge Representation and Reasoning (NeLaMKRR 2024)
by Ken Satoh, Ha-Thanh Nguyen, Francesca Toni, Randy Goebel, Kostas Stathis
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 capabilities of natural language processing (NLP) models, specifically transformer-based language models, in exhibiting reasoning abilities. Traditionally, AI has focused on logic-based representations of knowledge for reasoning, but recent advancements in NLP have led to the emergence of language models that may exhibit reasoning capacities as they grow in size and are trained on more data. The study investigates the extent to which these models can reason, despite ongoing discussions about what constitutes reasoning in this context. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about how computers think critically. Right now, AI is really good at understanding language, but we’re not sure if it’s actually thinking or just doing tricks with words. The researchers want to know if the new kind of AI models that are getting better and better can actually reason like humans do. They’re looking into whether these models can solve problems, make smart decisions, and understand complex ideas. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Transformer
