Summary of Do Llms Really Adapt to Domains? An Ontology Learning Perspective, by Huu Tan Mai et al.
Do LLMs Really Adapt to Domains? An Ontology Learning Perspective
by Huu Tan Mai, Cuong Xuan Chu, Heiko Paulheim
First submitted to arxiv on: 29 Jul 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 Large Language Models (LLMs) have achieved remarkable success in various natural language processing tasks. Recent studies suggest that LLMs can perform well on lexical semantic tasks like Knowledge Base Completion (KBC) or Ontology Learning (OL). However, it remains unclear whether their success is due to their ability to reason over unstructured data or learn linguistic patterns and senses. This paper investigates whether LLMs adapt to domains and extract structured knowledge consistently, or simply learn lexical senses without reasoning. To answer this question, we devise a controlled experiment using WordNet to synthesize parallel corpora with English and gibberish terms. We examine the outputs of LLMs for each corpus in two OL tasks: relation extraction and taxonomy discovery. The results show that off-the-shelf LLMs don’t consistently reason over semantic relationships between concepts, instead leveraging senses and frames. However, fine-tuning improves their performance on lexical semantic tasks even when encountering unseen domain-specific terms during pre-training, hinting at the applicability of pre-trained LLMs for OL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about understanding how Large Language Models (LLMs) work with language. They’re really good at some jobs like filling in missing information or learning new words. But it’s not clear why they’re so good. Some people think it’s because they can understand patterns and meanings, while others believe it’s just because they’ve learned lots of words. To figure this out, the researchers created special texts with made-up words and English words. They then asked the LLMs to do two jobs: find relationships between words and create a list of categories. The results show that the LLMs don’t really understand how to connect words together, but they’re still good at finding patterns in words. When they’re trained extra, though, they get even better at these tasks! |
Keywords
» Artificial intelligence » Fine tuning » Knowledge base » Natural language processing
