Summary of Thought-like-pro: Enhancing Reasoning Of Large Language Models Through Self-driven Prolog-based Chain-of-thought, by Xiaoyu Tan (1) et al.
Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Driven Prolog-based Chain-of-Thought
by Xiaoyu Tan, Yongxin Deng, Xihe Qiu, Weidi Xu, Chao Qu, Wei Chu, Yinghui Xu, Yuan Qi
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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 The paper introduces a novel learning framework, THOUGHT-LIKE-PRO, which utilizes imitation learning to imitate the Chain-of-Thought (CoT) process from symbolic Prolog logic engine trajectories. This self-driven approach enables large language models (LLMs) to formulate rules and statements from given instructions and leverage the Prolog engine to derive results. LLMs then convert Prolog-derived successive reasoning trajectories into natural language CoT for imitation learning. The framework improves the reasoning abilities of LLMs, demonstrating robust generalization across out-of-distribution tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us get closer to having super smart computers that can understand and reason like humans. Right now, these “large language models” are really good at answering questions and doing math problems, but they need help to make decisions on their own. The researchers created a new way for the computers to learn by copying how humans think, using a special kind of logic called Prolog. This lets the computers understand instructions and use rules to solve problems. The results show that this new approach makes the computers even better at solving problems, and they can do it without needing help from us. |
Keywords
» Artificial intelligence » Generalization
