Summary of Reliable Reasoning Beyond Natural Language, by Nasim Borazjanizadeh et al.
Reliable Reasoning Beyond Natural Language
by Nasim Borazjanizadeh, Steven T. Piantadosi
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 neurosymbolic approach prompts Large Language Models (LLMs) to extract and encode relevant information from problem statements as logical code statements, then uses Prolog for iterative computations. This enhances performance on the GSM8k and Navigate datasets. The authors also introduce a novel dataset, Non-Linear Reasoning (NLR), which targets shortcomings of next token prediction paradigms in LLMs. Prolog integration enables LLMs to achieve high performance on NLR, even outperforming GPT4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps Large Language Models be better at solving math problems and understanding language. They use a special way of thinking called logic programming to make the models more reliable and flexible. The approach works well for certain types of math problems and shows that it’s possible to make big improvements in how well language models can reason. |
Keywords
» Artificial intelligence » Token
