Summary of Relation Extraction with Fine-tuned Large Language Models in Retrieval Augmented Generation Frameworks, by Sefika Efeoglu and Adrian Paschke
Relation Extraction with Fine-Tuned Large Language Models in Retrieval Augmented Generation Frameworks
by Sefika Efeoglu, Adrian Paschke
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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, researchers focus on Relation Extraction (RE), a crucial task in Information Extraction (IE) that identifies relationships between entities in text. While various RE methods exist, including supervised, unsupervised, weakly supervised, and rule-based approaches, recent studies leveraging pre-trained language models (PLMs) have shown significant success. The paper explores the performance of fine-tuned PLMs and their integration into the Retrieval Augmented-based (RAG) RE approach to address challenges in identifying implicit relations between entities at the sentence level. Empirical evaluations on various datasets, including TACRED, TACRED-Revisited, Re-TACRED, and SemEVAL, show significant performance improvements with fine-tuned PLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists are working on a way to better understand relationships in text data. This is important because it helps us make sense of unstructured information like news articles or social media posts. There are many ways to do this, but the researchers are looking at using special computer models that have been trained on lots of text data. They’re trying to see if these models can be fine-tuned to do even better job of finding relationships in text. The results show that these models are really good at identifying relationships that aren’t easy to find just by looking at the words in a sentence. |
Keywords
» Artificial intelligence » Rag » Supervised » Unsupervised
