Summary of Machine Translation with Large Language Models: Prompt Engineering For Persian, English, and Russian Directions, by Nooshin Pourkamali et al.
Machine Translation with Large Language Models: Prompt Engineering for Persian, English, and Russian Directions
by Nooshin Pourkamali, Shler Ebrahim Sharifi
First submitted to arxiv on: 16 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); 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 This paper explores the capabilities of generative large language models (LLMs) in natural language processing (NLP) tasks. Specifically, it examines how these models excel in tasks such as machine translation, question answering, text summarization, and natural language understanding. The authors investigate the strengths of LLMs, which have shown impressive performance in various applications. By analyzing the results, the researchers aim to better understand the potential of LLMs in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative large language models are super smart computers that can do many cool things with words! They’re really good at translating languages, answering questions, and making summaries. These AI models are also great at understanding what we mean when we talk or write. This paper talks about how well these models do in different tasks. The people who wrote the paper want to see if they can use these models for real-world problems. |
Keywords
* Artificial intelligence * Language understanding * Natural language processing * Nlp * Question answering * Summarization * Translation
