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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 20·5 min read
Identifying Implicit Premises for Logical Reconstruction of Argument Graphs
TL;DR
A neuro-symbolic pipeline was developed to identify and generate implicit premises in argument graphs, enhancing logical reconstruction from natural language text. This method effectively demonstrates entailment or contradiction relationships between statements using large language models and logical formulae.
✦ Why It Matters
Implement this neuro-symbolic approach to enhance your argument analysis tools today.
Key Takeaways
- Utilize large language models for generating implicit premises.
- Combine explicit and implicit premises for logical clarity.
- Evaluate argument structures using the Microtext Argumentative Corpus.
- Enhance automated reasoning tasks with improved argument analysis.
How It Works
The proposed neuro-symbolic pipeline integrates large language models to generate implicit premises from natural language text. These generated premises are then converted into logical formulae, which are used alongside explicit premises to analyze the logical relationships between statements.
This approach allows for a more comprehensive understanding of arguments by filling in gaps where implicit premises exist.