Summary of Seemingly Plausible Distractors in Multi-hop Reasoning: Are Large Language Models Attentive Readers?, by Neeladri Bhuiya et al.
Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?
by Neeladri Bhuiya, Viktor Schlegel, Stefan Winkler
First submitted to arxiv on: 8 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 This research focuses on the impressive multi-hop reasoning abilities of Large Language Models (LLMs), specifically their capacity to gather and combine information from multiple texts. The study builds upon the existing capabilities of LLMs, which have already demonstrated proficiency in reading comprehension, advanced mathematical and reasoning skills, as well as scientific knowledge. The researchers aim to better understand and utilize this multi-hop reasoning capability, exploring its potential applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models can do many amazing things, like read books and do math problems. But one cool thing they’re really good at is finding information from multiple sources and combining it into a new idea. This paper looks at how LLMs do this “multi-hop reasoning” and tries to figure out how we can use it for other tasks. |
