Summary of Transformer-based Language Models For Reasoning in the Description Logic Alcq, by Angelos Poulis et al.
Transformer-based Language Models for Reasoning in the Description Logic ALCQ
by Angelos Poulis, Eleni Tsalapati, Manolis Koubarakis
First submitted to arxiv on: 12 Oct 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 paper investigates the logical reasoning capabilities of transformer-based language models by constructing a natural language dataset called DELTA_D using the expressive description logic language . The dataset comprises 384K examples that increase in two dimensions: reasoning depth and linguistic complexity. Supervised fine-tuned DeBERTa-based models and large language models like GPT-3.5 and GPT-4 with few-shot prompting are evaluated on entailment checking tasks. Results show that the DeBERTa-based model can master the task, while GPTs improve significantly even with a small number of samples (9 shots). The paper’s findings demonstrate the potential of large language models for logical reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well big language models can understand and reason about logic. It creates a special dataset called DELTA_D that has lots of examples to help the models learn. The researchers want to see if these models can do tasks that require understanding logic, like figuring out if one sentence follows from another. They use three different models: DeBERTa-based and two versions of GPT. The results show that one model is really good at this task, and even the other two models get better with just a few examples to learn from. This helps us understand what these big language models are capable of. |
Keywords
» Artificial intelligence » Few shot » Gpt » Prompting » Supervised » Transformer
