Summary of Logically Consistent Language Models Via Neuro-symbolic Integration, by Diego Calanzone et al.
Logically Consistent Language Models via Neuro-Symbolic Integration
by Diego Calanzone, Stefano Teso, Antonio Vergari
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 paper introduces a novel approach for teaching large language models (LLMs) to be logically consistent with external facts and rules. This is achieved through a loss function based on neuro-symbolic reasoning, which improves self-consistency even when fine-tuned on limited data. The method also enables the combination of multiple logical constraints in a principled way, resulting in more consistent LLMs that outperform baselines. Additionally, the approach allows for systematic extrapolation to unseen but semantically similar factual knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are smart computers that can understand and generate human-like text. However, they often make mistakes by providing false information or contradicting themselves. To solve this problem, researchers have tried large-scale fine-tuning or using external tools. In this study, scientists propose a middle ground approach to teach LLMs to be consistent with facts and rules. This method can combine multiple logical constraints and improve performance. It also allows LLMs to make predictions about new but related information. |
Keywords
» Artificial intelligence » Fine tuning » Loss function
