Summary of Mm-eval: a Hierarchical Benchmark For Modern Mongolian Evaluation in Llms, by Mengyuan Zhang and Ruihui Wang and Bo Xia and Yuan Sun and Xiaobing Zhao
MM-Eval: A Hierarchical Benchmark for Modern Mongolian Evaluation in LLMs
by Mengyuan Zhang, Ruihui Wang, Bo Xia, Yuan Sun, Xiaobing Zhao
First submitted to arxiv on: 14 Nov 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 The paper addresses the challenges of large language models in low-resource languages like Mongolian by categorizing capabilities into language abilities (syntax and semantics) and cognitive abilities (knowledge and reasoning). It develops MM-Eval, a specialized dataset to systematically evaluate these areas. This research aims to improve the performance of LLMs in Mongolian by identifying strengths and weaknesses in different aspects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps large language models perform better in languages like Mongolian. It groups the model’s skills into two categories: how well it understands the language (syntax and semantics) and what it knows about the world (knowledge and reasoning). To see how well the model does, researchers created a special set of data to test these areas. |
Keywords
» Artificial intelligence » Semantics » Syntax
