Summary of Evaluating the Translation Performance Of Large Language Models Based on Euas-20, by Yan Huang et al.
Evaluating the Translation Performance of Large Language Models Based on Euas-20
by Yan Huang, Wei Liu
First submitted to arxiv on: 6 Aug 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 Recent breakthroughs in deep learning have led to significant advancements in natural language processing (NLP) tasks. Large Language Models (LLMs) like BERT and GPT have achieved impressive results in various NLP tasks, including machine translation (MT). Despite the progress made by LLMs, MT still faces several challenges. To address these issues, we present Euas-20, a dataset designed to evaluate the performance of LLMs on translation tasks, assess their ability to translate across different languages, and investigate the impact of pre-training data on the translation capabilities of LLMs for researchers and developers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Have you ever wondered how machines can understand and translate different languages? Recent advancements in artificial intelligence have made this possible. Large language models like BERT and GPT are super smart computers that can learn to recognize patterns in language. They’ve even helped with machine translation, making it easier for people who speak different languages to communicate. But there’s still more work to be done to make sure machines can translate accurately and consistently. To help improve the process, we created a dataset called Euas-20 to test how well these models can translate and what makes them better or worse at doing so. |
Keywords
» Artificial intelligence » Bert » Deep learning » Gpt » Natural language processing » Nlp » Translation
