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Quantifying the Multilingual Performance Gaps of Large Language Models Across Languages

Core Concepts
Large language models exhibit significant performance gaps across different languages, with high-resource languages like English significantly outperforming low-resource languages.
The paper proposes a method called the "Language Ranker" to quantitatively measure and compare the performance of large language models (LLMs) across different languages. The key findings are: The performance rankings of different LLMs in all languages are roughly the same, indicating a consistent pattern in their multilingual abilities. LLMs of different sizes exhibit the same partial order of performance, with larger models performing better on low-resource languages but worse on high-resource languages. There is a strong correlation between an LLM's performance in different languages and the proportion of the pre-training corpus dedicated to each language. The authors use the OPUS-100 multilingual dataset to evaluate the performance of four popular open-source LLMs: LlaMa2, Qwen, Mistral-v0.1, and Gemma. They measure the cosine similarity between the representations of the target language and English to quantify the performance gap. The results show that high-resource languages like German, French, and Chinese have representations more similar to English, while low-resource languages like Igbo, Kazakh, and Oriya exhibit lower similarity. The paper also explores the impact of model size on multilingual performance, finding a positive correlation between model size and performance on low-resource languages, but a negative correlation for high-resource languages. This suggests that as LLMs grow in size, they become better at understanding low-resource languages but may struggle with the increased complexity of high-resource language data.
The proportion of the pre-training corpus for some languages in LlaMa2 is as follows: German: 0.17% French: 0.16% Swedish: 0.15% Chinese: 0.13% Finnish: 0.03% Norwegian: 0.03%
"The excellent performance of LLM is often limited to some common languages, such as English." "High-resource languages have representations more similar to English, whereas low-resource languages show less similarity." "There is a modest positive correlation between the size of an LLM and its performance on low-resource languages."

Deeper Inquiries

How can the proposed Language Ranker be extended to capture more nuanced linguistic properties beyond just representation similarity?

The proposed Language Ranker can be extended by incorporating additional linguistic features and metrics to capture more nuanced properties beyond representation similarity. One approach could be to include syntactic and semantic analysis to evaluate how well the LLMs understand the grammatical structures and meaning of different languages. This could involve measuring the model's performance in tasks such as syntactic parsing, semantic similarity, and language-specific linguistic phenomena. By integrating these additional metrics, the Language Ranker can provide a more comprehensive assessment of the LLMs' capabilities across languages.

What other factors, beyond corpus proportion, might contribute to the performance gaps of LLMs across languages?

In addition to corpus proportion, several other factors can contribute to the performance gaps of LLMs across languages. One significant factor is the linguistic complexity and diversity of languages. Languages with intricate grammatical rules, diverse vocabulary, and unique linguistic features may pose challenges for LLMs trained primarily on high-resource languages like English. Cultural nuances, idiomatic expressions, and language-specific contexts can also impact the model's performance in understanding and generating text accurately in different languages. Furthermore, the quality and diversity of training data, the presence of biases in the data, and the fine-tuning strategies for specific languages can all influence the LLMs' performance across languages.

How can the insights from this study be leveraged to develop more equitable and inclusive large language models that better serve a diverse range of languages and communities?

The insights from this study can be leveraged to develop more equitable and inclusive large language models by focusing on several key strategies: Diverse Training Data: Ensuring that LLMs are trained on a diverse and representative dataset that includes a wide range of languages, dialects, and cultural contexts. This can help mitigate biases and improve the model's performance across languages. Multilingual Fine-Tuning: Implementing targeted fine-tuning strategies for low-resource languages to enhance the model's proficiency in understanding and generating text in these languages. This can involve additional training on specific language corpora and linguistic resources. Cross-Lingual Transfer Learning: Leveraging cross-lingual transfer learning techniques to transfer knowledge and representations from high-resource languages to low-resource languages. This can help improve the model's generalization capabilities and performance in underrepresented languages. Community Engagement: Collaborating with linguists, language experts, and community members to gather insights, feedback, and linguistic resources that can inform the development and evaluation of large language models. Engaging with diverse language communities can ensure that the models are inclusive and relevant to a wide range of users. By incorporating these strategies and insights, developers can work towards building more inclusive and equitable large language models that better serve the linguistic diversity of communities worldwide.