Summary of Rationalyst: Pre-training Process-supervision For Improving Reasoning, by Dongwei Jiang et al.
RATIONALYST: Pre-training Process-Supervision for Improving Reasoning
by Dongwei Jiang, Guoxuan Wang, Yining Lu, Andrew Wang, Jingyu Zhang, Chuyu Liu, Benjamin Van Durme, Daniel Khashabi
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The proposed model, RATIONALYST, addresses the challenge of incomplete reasoning steps generated by large language models (LLMs) by pre-training on a vast collection of rationale annotations. By leveraging a web-scale unlabelled dataset (the Pile) and various reasoning datasets with minimal human intervention, RATIONALYST consistently generalizes across diverse reasoning tasks, including mathematical, commonsense, scientific, and logical reasoning. Fine-tuned from LLaMa-3-8B, the model improves the accuracy of reasoning by an average of 3.9% on 7 representative reasoning benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Rationalyst is a new way for computers to understand why they’re saying something. Right now, AI models just spit out answers without explaining their thought process. This paper introduces Rationalyst, a model that learns from huge amounts of data and can explain its reasoning steps. The researchers used a massive dataset to train Rationalyst, which then showed it could solve various problems in math, science, and logic. Compared to other models, Rationalyst was better at getting the right answers. |
Keywords
» Artificial intelligence » Llama
