Summary of Complex Reasoning Over Logical Queries on Commonsense Knowledge Graphs, by Tianqing Fang et al.
Complex Reasoning over Logical Queries on Commonsense Knowledge Graphs
by Tianqing Fang, Zeming Chen, Yangqiu Song, Antoine Bosselut
First submitted to arxiv on: 12 Mar 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 The proposed COM2 dataset enables language models to learn complex commonsense reasoning skills, crucial for understanding relationships between events and inferring implicit context. The dataset consists of multi-hop logical queries verbalized into multiple-choice and text generation questions, leveraging handcrafted rules and large language models. By training on COM2, language models exhibit significant improvements in zero-shot performance for question answering and generative commonsense reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary COM2 helps language models learn complex event relationships and implicit context by creating a dataset of multi-hop logical queries from the Commonsense Knowledge Graph (CSKG). The queries are verbalized into multiple-choice and text generation questions using handcrafted rules and large language models. This leads to better zero-shot performance for question answering and generative commonsense reasoning tasks. |
Keywords
» Artificial intelligence » Knowledge graph » Question answering » Text generation » Zero shot
