Summary of Enhancing Reasoning Capabilities Of Llms Via Principled Synthetic Logic Corpus, by Terufumi Morishita et al.
Enhancing Reasoning Capabilities of LLMs via Principled Synthetic Logic Corpus
by Terufumi Morishita, Gaku Morio, Atsuki Yamaguchi, Yasuhiro Sogawa
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Logic in Computer Science (cs.LO)
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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 A novel approach, Additional Logic Training (ALT), is proposed to enhance large language models’ (LLMs) reasoning capabilities. The method involves generating logical reasoning samples using program-generated logic theory and empirical insights. A synthetic corpus, Formal Logic Deduction Diverse (FLD)_2, is constructed to test the LLMs’ ability to reason with unknown facts, diverse reasoning rules, linguistic expressions, and challenging distractors. Experimental results demonstrate that ALT on FLD)_2 significantly improves the reasoning capabilities of state-of-the-art LLMs, including LLaMA-3.1-70B, resulting in up to 30 points gained on logical reasoning benchmarks, up to 10 points on math and coding benchmarks, and 5 points on the BBH benchmark suite. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can do lots of things, but they have trouble making good decisions. Scientists created a special way to teach these computers called Additional Logic Training (ALT). They made a bunch of practice problems for the computers to solve, using rules from math and logic. Then, they tested it on some very smart computer models and found out that it really works! The computers got much better at solving problems and making good decisions. |
Keywords
» Artificial intelligence » Llama
