Summary of Towards Logically Consistent Language Models Via Probabilistic Reasoning, by Diego Calanzone et al.
Towards Logically Consistent Language Models via Probabilistic Reasoning
by Diego Calanzone, Stefano Teso, Antonio Vergari
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 In this paper, the authors aim to address the limitations of large language models (LLMs) in generating reliable and logical information. Current LLMs are prone to producing non-factual data and often contradict themselves when reasoning about beliefs. To mitigate these issues, the authors introduce a training objective based on probabilistic reasoning that incorporates external knowledge in the form of facts and rules. This approach enables their LLMs to be more logically consistent than previous baselines and allows them to generalize to unseen but semantically similar factual information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can generate text, but they’re not always reliable. They might produce false information or contradict themselves when trying to reason about the world. The authors of this paper wanted to improve this by teaching their LLMs to be more logical and consistent with what we know is true. To do this, they used a special training method that helps the model understand how facts are related. This makes it better at producing accurate text and applying what it knows to new situations. |
