Summary of Evaluating Text Summaries Generated by Large Language Models Using Openai’s Gpt, By Hassan Shakil et al.
Evaluating Text Summaries Generated by Large Language Models Using OpenAI’s GPT
by Hassan Shakil, Atqiya Munawara Mahi, Phuoc Nguyen, Zeydy Ortiz, Mamoun T. Mardini
First submitted to arxiv on: 7 May 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 research investigates the performance of OpenAI’s GPT models as independent evaluators of text summaries generated by six transformer-based models from Hugging Face. The study focuses on evaluating these summaries based on essential properties such as conciseness, relevance, coherence, and readability using traditional metrics like ROUGE and Latent Semantic Analysis (LSA). The results show significant correlations between GPT evaluations and traditional metrics, particularly in assessing relevance and coherence. This demonstrates the potential of GPT as a robust tool for evaluating text summaries, offering insights that complement established metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses special computer models to test how good other computer models are at summarizing texts. They used six different models to create summaries, and then tested those summaries using another model called GPT. GPT looked at the summaries and gave its own opinion on how good they were. The results showed that what GPT thought was important in a summary matched what experts usually look for. This is useful because it means we can use GPT to help figure out which models are best at summarizing texts. |
Keywords
» Artificial intelligence » Gpt » Rouge » Transformer
