Exploring the Impact of Native and Non-Native Language Prompts on Large Language Model Performance Across Social Media and News Tasks

Concepts de base

Across different prompting techniques and language models, non-native language prompts outperform native language prompts in eliciting desired outputs for a variety of social media and news-related NLP tasks.

Résumé

This study investigates the impact of different prompt structures (native, non-native, and mixed) on the performance of large language models (LLMs) across 11 NLP tasks associated with 12 Arabic datasets. The authors conducted 197 experiments involving 3 LLMs (GPT-4o, Llama-3.1-8b, and Jais-13b-chat) and 3 prompting techniques (zero-shot and few-shot).

The key findings are:

1. Few-shot prompting shows improved performance compared to zero-shot, corroborating previous findings.
2. Across different prompt setups, the non-native prompt outperforms the native and mixed prompts, with Llama 3.1 being 7% and 8% better than non-native and native prompts, respectively.
3. For new tasks with no training data, the zero-shot setup with non-native prompts performs the best across all models.
4. GPT-4o outperforms all models in all prompt setups.

The authors also provide an error analysis, highlighting common issues with the Jais-13b model, such as misclassifying few-shot samples, hallucinating irrelevant responses, and returning only one class for the majority of samples.

Stats

The dataset contains a total of 164,498 training instances and 48,772 original test instances across the 12 datasets.

Citations

"Our findings suggest that, on average, the non-native prompt performs the best, followed by mixed and native prompts."
"For a new task where no training data is available, the zero-shot setup is the ideal solution, and based on our findings, non-native prompts perform better across all models."
"GPT-4o outperforms all models in all prompt setups."

Idées clés tirées de

Native vs Non-Native Language Prompting: A Comparative Analysis

by Mohamed Baya... à arxiv.org 09-12-2024

https://arxiv.org/pdf/2409.07054.pdf

Questions plus approfondies

How can the insights from this study be applied to improve the performance of LLMs on low-resource languages beyond Arabic?

The insights from this study highlight the effectiveness of non-native language prompting in enhancing the performance of large language models (LLMs) on various natural language processing (NLP) tasks. To apply these insights to improve LLM performance on low-resource languages beyond Arabic, several strategies can be implemented:

Leveraging High-Resource Languages: Similar to the findings that non-native prompts (in English) outperformed native prompts (in Arabic), researchers can explore the use of high-resource languages as a bridge for low-resource languages. By crafting prompts in a high-resource language, LLMs can utilize their extensive training on these languages to better understand and generate responses in low-resource languages.

Multilingual Prompting Techniques: The study suggests that mixed prompts, which combine elements from both native and non-native languages, can yield promising results. This approach can be extended to other low-resource languages by developing multilingual prompting techniques that incorporate relevant phrases or structures from both the target low-resource language and a more dominant language.

Task-Specific Prompt Engineering: The research emphasizes the importance of prompt design tailored to specific tasks. By analyzing the unique characteristics of low-resource languages and their associated tasks, researchers can create optimized prompts that enhance the model's understanding and performance in these languages.

Data Augmentation: To further support low-resource languages, data augmentation techniques can be employed. This includes generating synthetic data using high-resource language models and translating it into the target low-resource language, thereby enriching the training datasets and improving model performance.

Community Engagement: Collaborating with native speakers and linguists can provide valuable insights into the nuances of low-resource languages. Engaging with local communities can help in creating culturally relevant prompts and datasets, ultimately leading to better model performance.

What are the potential biases and limitations introduced by the use of non-native language prompts, and how can they be mitigated?

The use of non-native language prompts can introduce several biases and limitations that may affect the performance and fairness of LLMs:

Language Bias: Non-native prompts may lead to a bias where the model favors responses that align more closely with the structure and semantics of the non-native language (e.g., English) rather than the native language. This can result in outputs that are less relevant or accurate for the target language.

Cultural Context: Non-native prompts may lack the cultural context necessary for understanding certain phrases or idioms in the native language. This can lead to misinterpretations or inappropriate responses that do not resonate with native speakers.

Overgeneralization: Relying heavily on non-native prompts may cause the model to overgeneralize based on the dominant language's patterns, potentially neglecting the unique linguistic features of the low-resource language.

To mitigate these biases and limitations, the following strategies can be employed:

Balanced Prompting: Develop a balanced approach that incorporates both native and non-native elements in prompts. This mixed strategy can help the model leverage the strengths of both languages while minimizing biases.

Cultural Sensitivity Training: Incorporate cultural sensitivity training into the model's development process. This can involve using datasets that reflect the cultural nuances of the target language and ensuring that prompts are contextually appropriate.

Bias Audits: Regularly conduct bias audits on the model's outputs to identify and address any discrepancies or biases that arise from the use of non-native prompts. This can help in refining the prompting strategies and improving overall model fairness.

User Feedback Mechanisms: Implement feedback mechanisms that allow users to report inaccuracies or biases in the model's responses. This feedback can be invaluable for continuous improvement and adaptation of the prompting strategies.

Given the varying performance of the models across different prompting techniques, how can we develop more robust and generalizable prompting strategies that work well across a diverse range of tasks and languages?

To develop more robust and generalizable prompting strategies that perform well across a diverse range of tasks and languages, the following approaches can be considered:

Unified Prompt Framework: Establish a unified framework for prompt design that incorporates best practices from various prompting techniques (e.g., zero-shot, few-shot, mixed). This framework should be adaptable to different languages and tasks, allowing for consistent application across diverse scenarios.

Dynamic Prompt Adaptation: Implement dynamic prompting techniques that adjust the prompt structure based on the specific characteristics of the input data and the task at hand. This adaptability can enhance the model's ability to generate relevant responses across different contexts.

Cross-Language Transfer Learning: Leverage transfer learning techniques to train models on high-resource languages and then fine-tune them on low-resource languages. This approach can help in developing prompts that are informed by the strengths of the dominant language while being applicable to the target language.

Collaborative Prompt Development: Foster collaboration among researchers, linguists, and domain experts to co-create prompts that are linguistically and culturally appropriate for various languages. This collaborative effort can lead to more effective and contextually relevant prompts.

Evaluation and Iteration: Continuously evaluate the performance of prompting strategies across different tasks and languages. Use metrics such as precision, recall, and F1 scores to assess effectiveness and iterate on the prompt designs based on empirical results.

Incorporating User Input: Engage end-users in the prompt development process by soliciting their input on prompt effectiveness and relevance. This user-centered approach can help ensure that the prompts resonate with the target audience and improve overall model performance.

By implementing these strategies, researchers can create more effective and generalizable prompting techniques that enhance the performance of LLMs across a wide array of languages and tasks.