Assessing Multilingual LLMs' Factual Accuracy with FActScore

Core Concepts

The author evaluates multilingual LLMs' factual accuracy using a novel pipeline and highlights geographical biases in fact generation.

Abstract

The study assesses multilingual LLMs' factual accuracy, revealing a Western-centric bias. English outperforms other languages in generating correct facts. The research questions focus on uniform factual accuracy across languages and precision alignment with language. The methodology involves task selection, model usage, prompt translation, and factuality measurement. Results show significant variations in factuality across languages and geographic regions, emphasizing the need for enhanced assessment methods.

Stats

English consistently maintains an advantage in both factual accuracy and quantity of generated facts compared to other languages. Across analyzed languages, America and Europe are the primary focal points for accurate outputs. Languages exhibit preferential accuracy towards regions where they are predominantly spoken.

Quotes

Key Insights Distilled From

Multi-FAct

by Sheikh Shafa... at arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18045.pdf

Deeper Inquiries

How do cultural biases impact the factuality of LLMs in different languages?

Cultural biases can significantly impact the factuality of Large Language Models (LLMs) across different languages. These biases can manifest in various ways, such as favoring certain regions or cultures over others, leading to inaccuracies and distortions in generated content. For example, Western-centric bias may result in more accurate information about Western countries while neglecting details about non-Western regions. This bias can skew the representation of knowledge within LLMs towards a particular cultural perspective, affecting the overall factual accuracy.

What implications do geographical biases have on the representation of knowledge within LLMs?

Geographical biases in LLMs can have profound implications on how knowledge is represented within these models. When LLMs exhibit preferences for specific geographic regions or continents, it results in uneven distribution and accuracy of facts across different areas. This leads to an imbalanced portrayal of global information, with certain regions receiving more attention and factual precision than others. As a consequence, users relying on LLM-generated content may encounter skewed or incomplete representations of knowledge based on where they are geographically situated.

How can future research address the limitations of small sample bias in evaluating multilingual factuality?

Future research aiming to address the limitations of small sample bias in evaluating multilingual factuality could employ several strategies: Increase Sample Size: By expanding the dataset to include multiple individuals from each country or region rather than just one political leader, researchers can mitigate potential biases arising from limited samples. Diversify Topics: Instead of focusing solely on political figures, incorporating diverse topics like historical events or cultural landmarks could provide a broader range for evaluation and reduce sample bias. Human Evaluation Baseline: Establishing a human evaluation baseline for non-biographical domains would offer a comparative standard for assessing factual accuracy beyond individual scores derived from automated methods. Cross-Validation Techniques: Implementing cross-validation techniques across multiple datasets and language models could help validate findings and ensure robustness against small sample bias. Incorporate Open Datasets: Utilizing open datasets that cover various domains and languages would enhance data diversity and reduce reliance on proprietary models with limited access. By implementing these approaches collectively or individually, future research endeavors can overcome small sample bias challenges inherent in evaluating multilingual factuality effectively and comprehensively.