Summary of Fine-tuning Large Language Models For Entity Matching, by Aaron Steiner et al.
Fine-tuning Large Language Models for Entity Matching
by Aaron Steiner, Ralph Peeters, Christian Bizer
First submitted to arxiv on: 12 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 explores the potential of fine-tuning generative large language models (LLMs) for entity matching, a task that has seen significant improvement with prompt engineering and in-context learning. The authors analyze fine-tuning along two dimensions: representation of training examples and selection/generation of training examples using LLMs. They experiment with adding different types of LLM-generated explanations to the training set and investigate how fine-tuning affects the model’s ability to generalize to other datasets, both within and across topical domains. The results show that fine-tuning improves performance for smaller models but has mixed results for larger ones. Additionally, fine-tuning improves generalization to in-domain datasets while hindering cross-domain transfer. The authors also examine the impact of adding structured explanations to the training set and propose methods for selecting and generating training examples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using big language models to match entities (like people or places) with information about them. Right now, these models are really good at this task without needing any special help. The researchers want to see if making these models “learn” more can make them even better. They tried two ways of doing this: changing the way they learn and giving them more information to practice with. They found that when they did this, some smaller models got a lot better at matching entities, but bigger models didn’t do as well. The models also got better at recognizing information about entities within certain topics, but not across different topics. |
Keywords
» Artificial intelligence » Fine tuning » Generalization » Prompt
