Summary of Learning Semantic Structure Through First-order-logic Translation, by Akshay Chaturvedi et al.
Learning Semantic Structure through First-Order-Logic Translation
by Akshay Chaturvedi, Nicholas Asher
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 This paper investigates whether transformer-based language models can accurately extract predicate-argument structures from simple sentences. Initially, the study reveals that these models often struggle to identify which predicates relate to specific objects. To address this issue, two tasks and regimes are explored: question answering (Q/A) and first-order logic (FOL) translation, with prompting and finetuning approaches. In FOL translation, several large language models are finetuned on synthetic datasets designed to assess their generalization capabilities. For Q/A, encoder models like BERT and RoBERTa are finetuned, while LLMs use prompting. The results demonstrate that FOL translation is better suited for learning predicate-argument structures in large language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well computer programs called language models can understand simple sentences. It seems these programs sometimes get confused about which actions apply to what objects. To help, the researchers tried two approaches: asking questions and translating sentences into a special language called first-order logic. They also used different ways to train the models, such as fine-tuning or giving them hints. The results show that using first-order logic is better for helping these models understand sentence structures. |
Keywords
» Artificial intelligence » Bert » Encoder » Fine tuning » Generalization » Prompting » Question answering » Transformer » Translation
