Summary of Benchmarking Large Language Models For Persian: a Preliminary Study Focusing on Chatgpt, by Amirhossein Abaskohi et al.
Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT
by Amirhossein Abaskohi, Sara Baruni, Mostafa Masoudi, Nesa Abbasi, Mohammad Hadi Babalou, Ali Edalat, Sepehr Kamahi, Samin Mahdizadeh Sani, Nikoo Naghavian, Danial Namazifard, Pouya Sadeghi, Yadollah Yaghoobzadeh
First submitted to arxiv on: 3 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 investigates the effectiveness of large language models (LLMs) for the Persian language. While ChatGPT and subsequent LLMs have shown impressive performance in English, their efficiency in low-resource languages like Persian remains an open question. The study presents a comprehensive benchmarking evaluation of LLMs across various Persian language tasks, focusing on GPT-3.5-turbo, but also including GPT-4 and OpenChat-3.5 for a more holistic assessment. The evaluation encompasses a diverse set of tasks categorized into classic, reasoning, and knowledge-based domains, comparing LLMs against existing task-specific fine-tuned models. To enable thorough comparison, the study introduces two new benchmarks for Persian: one based on elementary school math questions and another derived from entrance exams for 7th and 10th grades. The findings reveal that while LLMs, especially GPT-4, excel in tasks requiring reasoning abilities and a broad understanding of general knowledge, they often lag behind smaller pre-trained models fine-tuned specifically for particular tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well large language models can understand the Persian language. These models are very good at understanding English, but not as much is known about how they do with languages that don’t have as many examples to learn from. The researchers tested several of these models on different tasks, such as answering math questions or providing general knowledge. They also created new tests for Persian and compared the results to smaller models that were specifically trained for certain tasks. The study found that while some language models are very good at understanding complex information, they can struggle when it comes to specific tasks. This is important because Persian has its own unique characteristics, like a different alphabet and writing style. |
Keywords
* Artificial intelligence * Gpt
