SeaEval Benchmark for Multilingual Foundation Models: Cultural Reasoning and Cross-Lingual Consistency

核心概念

The author presents SeaEval, a benchmark for multilingual foundation models, focusing on cultural reasoning and cross-lingual consistency to assess model capabilities comprehensively.

摘要

SeaEval introduces new evaluation criteria covering linguistic, cultural contexts, and cross-lingual consistency. Key findings highlight sensitivity to paraphrased instructions, exposure bias in label arrangements, inconsistent performance across languages, and imbalanced multilingual proficiency. The study emphasizes the need for more generalizable semantic representations and enhanced multilingual contextualization.
The content discusses the desired properties of multilingual foundation models, task selection for evaluation benchmarks, data curation process, evaluation protocols including instruction sensitivity and cross-lingual consistency metrics. Results show GPT-4 excelling in handling multilingual tasks while BLOOMZ stands out in cross-lingual consistency. Disparities in model performance across languages are observed with English usually surpassing others. Limitations include the need for more languages/cultural datasets and evaluating safety/efficiency aspects of models.

統計資料

SeaEval encompasses a total of 28 datasets.
Baichuan-2 model shows remarkable performance in understanding Chinese culture.
BLOOMZ demonstrates better alignment across languages but still shows unsatisfactory consistency scores.
GPT-4 excels in handling multilingual tasks with superior capabilities.
Exposure bias on label arrangements affects model predictions.

引述

"Models exhibit varied behavior with paraphrased instructions."
"Most models give inconsistent answers when asked fact-based questions in different languages."
"GPT-4 demonstrates outstanding performance across cultures and languages."

從以下內容提煉的關鍵洞見

SeaEval for Multilingual Foundation Models

by Bin Wang,Zhe... 於 arxiv.org 03-06-2024

https://arxiv.org/pdf/2309.04766.pdf

深入探究

How can automated methods be utilized to collect diverse datasets for various languages?

Automated methods can be employed to collect diverse datasets for various languages by utilizing web scraping techniques to gather text data from a wide range of sources in different languages. Natural Language Processing (NLP) tools can then be used to filter and preprocess this data, ensuring its quality and relevance. Additionally, machine translation models can help translate the collected data into multiple languages, enabling the creation of multilingual datasets. By leveraging these automated processes, researchers can efficiently curate large and varied datasets that encompass linguistic diversity.

What are the implications of disparities in model performance across different languages?

Disparities in model performance across different languages have significant implications for the effectiveness and generalizability of multilingual foundation models. When certain languages consistently outperform others, it indicates a bias or limitation within the model's training data or architecture. This disparity may lead to unequal representation and accuracy levels across language groups, impacting the model's ability to provide reliable results in real-world applications. Addressing these disparities is crucial for enhancing the inclusivity and fairness of multilingual models.

How can safety and efficiency dimensions of foundation models be effectively evaluated?

The evaluation of safety and efficiency dimensions in foundation models requires comprehensive testing methodologies that assess both ethical considerations and computational performance. Safety evaluations should include tests for bias detection, robustness against adversarial attacks, privacy protection measures, and adherence to ethical guidelines. Efficiency assessments involve measuring inference speed, memory usage optimization, scalability potential on different hardware configurations, etc.
To evaluate safety aspects effectively:

Conduct bias audits on training data
Test robustness against adversarial inputs
Implement privacy-preserving mechanisms
4.Ensure compliance with ethical standards

For evaluating efficiency:
1.Measure inference speed under varying workloads
2.Optimize memory consumption during operation
3.Test scalability on different hardware setups
By employing rigorous testing protocols that cover both safety-related concerns as well as computational efficiency metrics ensures a holistic evaluation process for foundation models before deployment in practical settings