Summary of Language Ranker: a Metric For Quantifying Llm Performance Across High and Low-resource Languages, by Zihao Li et al.
Language Ranker: A Metric for Quantifying LLM Performance Across High and Low-Resource Languages
by Zihao Li, Yucheng Shi, Zirui Liu, Fan Yang, Ali Payani, Ninghao Liu, Mengnan Du
First submitted to arxiv on: 17 Apr 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 A novel approach to evaluate Large Language Models (LLMs) is proposed, addressing the significant gap in performance between high-resource and low-resource languages. The study introduces the Language Ranker, an intrinsic metric that benchmarks and ranks languages based on LLM internal representations. By comparing LLM internal representations with a baseline derived from English, this method assesses multilingual capabilities in a robust and language-agnostic manner. The analysis reveals high-resource languages exhibit higher similarity scores with English, indicating superior performance, while low-resource languages show lower similarity scores, demonstrating the effectiveness of the metric. Additionally, experiments reveal strong correlation between LLM performance across different languages and the proportion of those languages in its pre-training corpus. This study highlights the Language Ranker as a tool for evaluating LLM performance, particularly in low-resource languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are super smart computers that can understand and generate human-like text. But these models have one big problem: they’re way better at understanding some languages than others. For example, they’re great with English, German, and French, but struggle with languages that don’t have as many examples to learn from. Scientists wanted to find a way to measure how well these language models perform in different languages. So, they created a new tool called the Language Ranker. This tool compares how well the model understands different languages by looking at its internal workings and comparing it to English. The results show that languages with more examples for the model to learn from do better, while languages with fewer examples do worse. This helps scientists understand why language models are so good or bad in certain languages and can help them make better ones. |
