Balanced Multi-Factor In-Context Learning for Multilingual Large Language Models

Abstract: Multilingual LLMs (MLLMs) are able to leverage in-context learning (ICL) to achieve high performance by leveraging cross-lingual knowledge transfer without parameter updates. However, their effectiveness is highly sensitive to example selection, particularly in multilingual settings. Based on the findings of existing work, three key factors influence multilingual ICL: (1) semantic similarity, (2) linguistic alignment, and (3) language-specific performance. However, existing approaches address these factors independently, without explicitly disentangling their combined impact, leaving optimal example selection underexplored. To address this gap, we propose balanced multi-factor ICL (\textbf{BMF-ICL}), a method that quantifies and optimally balances these factors for improved example selection. Experiments on mCSQA and TYDI across four MLLMs demonstrate that BMF-ICL outperforms existing methods. Further analysis highlights the importance of incorporating all three factors and the importance of selecting examples from multiple languages.

• The paper introduces BMF-ICL, a method that balances semantic, linguistic, and language-specific factors to optimize example selection in multilingual in-context learning.
• It utilizes LaBSE for semantic similarity and lang2vec for capturing linguistic alignment to effectively gauge cross-lingual nuances.
• Experimental results on mCSQA and TYDI benchmarks confirm enhanced performance even without target-language examples, underscoring robust cross-lingual adaptability.

Balanced Multi-Factor In-Context Learning for Multilingual LLMs

The paper "Balanced Multi-Factor In-Context Learning for Multilingual LLMs" by Kaneko et al. addresses a nuanced challenge within the field of Multilingual LLMs (MLLMs): the optimization of in-context learning (ICL) strategies across multiple languages. The authors focus on improving the efficacy of MLLMs, which leverage cross-lingual knowledge transfer to perform complex tasks without the need for parameter updates. However, the process heavily depends on selecting pertinent example data, which is seldom effectively managed in multilingual settings due to several underlying factors that are typically considered in isolation.

The authors identify three primary factors influencing the effectiveness of multilingual ICL: semantic similarity, linguistic alignment, and language-specific performance. They point out that existing methodologies treat these factors independently, neglecting the potential advantages of their combined impact. This gap in approach misses the opportunity for optimal example selection that considers all these factors collectively.

To remedy this, the authors propose a new methodology termed Balanced Multi-Factor ICL (BMF-ICL). BMF-ICL introduces explicit metrics for quantifying and balancing semantic similarity, linguistic alignment, and language-specific performance, aiming for a more holistic and effective example selection process. Semantic similarity is evaluated using LaBSE—a language-agnostic sentence embedding model—while linguistic alignment is measured through lang2vec, which captures typological features. Language-specific performance relates to the likelihood of generating accurate outputs.

The experimental results, conducted on benchmark datasets such as mCSQA and TYDI using multiple MLLMs, demonstrate the superiority of BMF-ICL. Quantitative evaluations underscore the significance of selecting examples from multiple languages, and the analysis reveals that BMF-ICL's consideration of all three factors yields consistently better performance than conventional methods.

Results indicate that BMF-ICL not only improves accuracy across a broad range of languages but does so consistently across models like BLOOMZ, Aya, GPT-3.5, and GPT-4. Particularly notable is the demonstrated improvement even in scenarios where target-language examples are not included, highlighting BMF-ICL's robust cross-lingual adaptability.

These findings challenge the conventional wisdom of relying on heuristic or single-factor approaches for multilingual example selection. The implications for both theoretical and practical domains in AI are significant. Theoretically, BMF-ICL provides a framework for understanding the complexities of example selection in cross-lingual contexts. Practically, it offers a scalable solution for improving multilingual model performance without necessitating extensive supervision or language-specific tuning.

Looking forward, the implications suggest further exploration into the applications of BMF-ICL could enable more effective deployment in diverse global contexts, particularly for low-resource languages that greatly benefit from refined ICL techniques. As such, BMF-ICL represents a meaningful advancement in exploiting the multilingual capabilities of LLMs, paving the way for improved global communication technologies.