Summary of Critical-questions-of-thought: Steering Llm Reasoning with Argumentative Querying, by Federico Castagna et al.
Critical-Questions-of-Thought: Steering LLM reasoning with Argumentative Querying
by Federico Castagna, Isabel Sassoon, Simon Parsons
First submitted to arxiv on: 19 Dec 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 proposed method employs Toulmin’s model of argumentation to improve the reasoning capabilities of Large Language models (LLMs). By probing the rationale behind the models’ reasoning process, the LLM can assess whether some logical mistake is occurring and correct it before providing the final reply. This approach successfully steers the models’ output through a reasoning pipeline, resulting in better performance against the baseline and its Chain-of-Thought (CoT) implementation. The method is evaluated on the MT-Bench Reasoning and Math tasks across a range of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make computers smarter by teaching them how to reason logically. Right now, even super powerful AI models struggle with math and logic problems that are new or unfamiliar. To help these models do better, this study uses ideas from argumentation theory, which is all about creating valid arguments. The idea is that if a model can understand why it’s making a mistake, it can fix the problem before giving an answer. This approach works well and helps AI models perform better on math and reasoning tasks. |
