The Impact of Demonstrations on Multilingual In-Context Learning: A Multidimensional Analysis (2402.12976v2)

Abstract: In-context learning is a popular inference strategy where LLMs solve a task using only a few labeled demonstrations without needing any parameter updates. Although there have been extensive studies on English in-context learning, multilingual in-context learning remains under-explored, and we lack an in-depth understanding of the role of demonstrations in this context. To address this gap, we conduct a multidimensional analysis of multilingual in-context learning, experimenting with 5 models from different model families, 9 datasets covering classification and generation tasks, and 56 typologically diverse languages. Our results reveal that the effectiveness of demonstrations varies significantly across models, tasks, and languages. We also find that strong instruction-following models including Llama 2-Chat, GPT-3.5, and GPT-4 are largely insensitive to the quality of demonstrations. Instead, a carefully crafted template often eliminates the benefits of demonstrations for some tasks and languages altogether. These findings show that the importance of demonstrations might be overestimated. Our work highlights the need for granular evaluation across multiple axes towards a better understanding of in-context learning.

Multidimensional Analysis of Multilingual In-Context Learning: Unveiling the Variability in Demonstrations' Impact

Introduction

In-context learning (ICL) has gained traction as a powerful inference strategy, enabling LLMs to solve tasks by leveraging a few labeled demonstrations without requiring parameter updates. Despite its popularity, the variance in the effectiveness of demonstrations, especially within a multilingual context, is significantly underexplored. This paper contributes to filling this gap through a comprehensive examination across multiple dimensions: models, tasks, and languages. By evaluating a selection of five LLMs across nine datasets covering 56 languages, our findings reveal a wide variability in the impact of demonstrations, challenging the current understanding of their importance.

Experimental Framework

The paper meticulously designs its experimental framework to dissect the nuances of in-context learning across multiple axes:

• Models: The paper categorizes LLMs into base models (XGLM and Llama 2), which are only pre-trained on unlabelled corpora, and chat models (Llama 2-Chat, GPT-3.5, and GPT-4), which undergo further refinement with instruction tuning and reinforcement learning.
• Tasks and Datasets: A diverse set of tasks, including both classification and generation tasks across 9 multilingual datasets, enables a thorough evaluation. The task varieties range from natural language inference and paraphrase identification to extractive question answering and machine translation, covering 56 languages.
• In-Context Learning Protocol: The paper explores the impact of varying the number of demonstrations (0, 2, 4, 8) on model performance. Demonstrations are presented in the same language as the test example, with English-used templates, aligning with the pattern-verbalizer framework for in-context learning.

Key Findings

The paper presents four critical insights that emerge from the multidimensional analysis:

1. Varying Effectiveness: Demonstrations' effectiveness widely varies depending on the model, task, and language, with base models showing minimal improvement or even degradation compared to zero-shot learning on many tasks. Interestingly, chat models exhibit less sensitivity to the quality of demonstrations, indicating their potential to derive task formats rather than task-specific knowledge from demonstrations.
2. Demonstration Quality: Sophisticated demonstration selection methods do not uniformly benefit in-context learning, highlighting instances where they perform worse than using no demonstrations at all. This suggests that the quality of demonstrations, while crucial, does not guarantee enhanced performance across all settings.
3. Template vs. Demonstrations: For chat models, employing a focused formatting template can negate the need for demonstrations, underscoring the nuanced relationship between template design and the utility of demonstrations in in-context learning.
4. Performance Saturation: Incremental benefits from adding demonstrations plateau quickly, with marginal improvements observed beyond 2 to 4 demonstrations. This finding is consistent with observations that reducing the number of demonstrations does not significantly impact task performance, challenging the perceived criticality of demonstrations in enhancing model performance.

Implications and Future Directions

The variability in demonstrations' impact across models, tasks, and languages raises important questions about the generalization of in-context learning strategies, especially in multilingual contexts. The findings suggest that the added value of demonstrations may be overestimated, advocating for a nuanced understanding of when and how demonstrations contribute to model performance.

Future research should extend this multidimensional analysis to newer models and emerging tasks, considering the rapid advancement in LLM capabilities. Additionally, exploring alternative methods for demonstration selection and template design could uncover more efficient strategies for leveraging in-context learning, especially in resource-scarce languages.

In conclusion, this paper provides a foundational step towards a granular understanding of multilingual in-context learning, highlighting the complexity and variability inherent in the interaction between demonstrations, templates, and LLMs. It charts a course for future explorations that aim to refine our understanding and utilization of in-context learning paradigms, particularly in the diverse and multifaceted landscape of multilingual natural language processing.