Summary of Are Machines Better at Complex Reasoning? Unveiling Human-machine Inference Gaps in Entailment Verification, by Soumya Sanyal et al.
Are Machines Better at Complex Reasoning? Unveiling Human-Machine Inference Gaps in Entailment Verification
by Soumya Sanyal, Tianyi Xiao, Jiacheng Liu, Wenya Wang, Xiang Ren
First submitted to arxiv on: 6 Feb 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 paper tackles the challenge of entailment verification (EV) by developing a benchmark that includes multi-sentence premises from various natural language processing (NLP) domains. The authors demonstrate that large language models (LLMs) excel in multi-hop reasoning tasks, while humans perform better in simple deductive reasoning tasks. To address this disparity, they fine-tune a Flan-T5 model for EV using two training objectives and achieve state-of-the-art results that rival GPT-4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about helping computers understand the meaning of text by making smart connections between ideas. They’re trying to figure out how well humans and super-smart computer models can work together to make these connections. The researchers found that while computers are great at understanding complex ideas, they need help from humans when it comes to simple problems. To solve this problem, they created a special kind of AI model that’s really good at making sense of text. This new model is so powerful that it can even help computers generate better answers by getting rid of bad ideas. |
Keywords
» Artificial intelligence » Gpt » Natural language processing » Nlp » T5
