Towards Cross-lingual Values Judgment: A Consensus-Pluralism Perspective
Introduced X-Value benchmark to assess cross-lingual values judgment in LLMs with 4,750 QA pairs.
Key Findings
Methodology
The study proposes a two-stage human-AI collaborative annotation framework, first identifying issue scope and nature, then conducting fine-grained values judgment. Multiple LLMs are used for final review to ensure annotation accuracy.
Key Results
- X-Value benchmark includes 4,750 QA pairs covering 14 languages and 7 major global issues, providing 12 granular annotation metadata.
- Systematic evaluation across 17 LLMs reveals limitations in cross-lingual values judgment.
- Analysis shows significant performance disparities across categories and languages, indicating a need for improved values judgment capabilities.
Significance
The study fills a gap in evaluating LLMs' cross-lingual values judgment capabilities, highlighting the impact of cultural diversity and disciplinary complexity on values judgment, advancing research in multilingual model value alignment.
Technical Contribution
The proposed X-Value benchmark is the first to systematically evaluate LLMs' deep-level values judgment capabilities, providing comprehensive annotation metadata and multidimensional evaluation standards for future model improvements.
Novelty
This study introduces the first cross-lingual values judgment benchmark, combining cultural diversity and disciplinary complexity, offering a new annotation framework and evaluation method.
Limitations
- Current models perform poorly in fine-grained values judgment, especially in multilingual scenarios.
- The annotation process relies on human-AI collaboration, which may introduce subjective bias.
Future Work
Future research could explore improving models' values judgment capabilities, particularly their adaptability to cultural diversity and disciplinary complexity.
AI Executive Summary
As large language models are deployed globally, existing evaluation paradigms primarily focus on factual task performance, neglecting the ability to judge deep-level values across languages. To bridge this gap, the study proposes a novel two-stage human-AI collaborative annotation framework, identifying issue scope and nature, establishing specific annotation criteria, and utilizing multiple LLMs for final review. Building on this framework, the study introduces X-Value, the first Cross-lingual Values Judgment Benchmark designed to evaluate the capability of LLMs in judging deep-level values of content. X-Value comprises 4,750 Question-Answer pairs across 14 languages, covering 7 major global issue categories, and provides 12 granular annotation metadata to facilitate rigorous evaluation of model performance. Systematic evaluations of X-Value are conducted across 17 LLMs using distinct prompting strategies. Multi-dimensional analysis of accuracy and F1-scores reveals their limitations in cross-lingual values judgment and indicates performance disparities across categories and languages. This work highlights the urgent need to improve the underlying, values-aware content judgment capability of LLMs.
Deep Analysis
Background
With the global deployment of large language models, existing evaluation methods focus on cross-cultural reasoning and factual knowledge, overlooking the ability to judge implicit cultural values. This ability enables models to identify implicit stances, controversial instances, and misinformation within multilingual content.
Core Problem
There is a significant gap in benchmarks for evaluating cultural values judgment capabilities of LLMs. Existing research primarily focuses on alignment with universal values, overlooking cultural deviations inherent in different languages.
Innovation
The study proposes a two-stage human-AI collaborative annotation framework, first identifying issue scope and nature, then conducting fine-grained values judgment. Multiple LLMs are used for final review to ensure annotation accuracy.
Methodology
- �� Stage 1: Identify issue scope (Global or Regional) and nature (Consensus or Pluralism).
- �� Stage 2: Conduct holistic and fine-grained values judgment, combining human-AI collaboration for review.
- �� Utilize multiple LLMs for final review to ensure annotation accuracy.
Experiments
Systematic evaluations were conducted across 17 LLMs using distinct prompting strategies. Analysis revealed limitations in cross-lingual values judgment and significant performance disparities across categories and languages.
Results
Multi-dimensional analysis of accuracy and F1-scores reveals limitations in cross-lingual values judgment and indicates performance disparities across categories and languages.
Applications
X-Value can be used to evaluate and improve LLMs' values judgment capabilities, particularly their adaptability in multilingual and multicultural contexts.
Limitations & Outlook
Current models perform poorly in fine-grained values judgment, especially in multilingual scenarios. The annotation process relies on human-AI collaboration, which may introduce subjective bias.
Plain Language Accessible to non-experts
Imagine a large international conference where people from around the world gather to discuss various global issues. Everyone has different cultural backgrounds and perspectives. Our study acts like a moderator, helping to coordinate these diverse views and ensure that everyone understands and respects each other's positions. We use a novel method to identify and evaluate the deep values of these perspectives, ensuring that the discussion is inclusive and fair.
ELI14 Explained like you're 14
Imagine you and your friends at school discussing a hot topic like global warming. Everyone has their own opinion; some think it's a big deal, others not so much. Our study is like a super-smart teacher helping everyone understand different viewpoints and find an answer everyone can agree on. We use a special method to make sure everyone's voice is heard and the discussion is fair.
Glossary
Large Language Model
An AI model capable of understanding and generating natural language text.
Core tool for cross-lingual values judgment.
Cross-lingual
Involving multiple languages in capability or task.
Evaluating model performance in multilingual settings.
Values Judgment
Assessment of cultural and moral values implicit in content.
Core task of the study.
Human-AI Collaboration
Process of humans and AI working together to complete tasks.
Key step in the annotation framework.
Annotation Metadata
Additional information used to describe and evaluate a dataset.
Provides multidimensional evaluation of model performance.
Open Questions Unanswered questions from this research
- 1 How to improve models' fine-grained values judgment in multilingual scenarios?
- 2 How to reduce subjective bias in the annotation process?
Applications
Immediate Applications
Multilingual Content Moderation
Can be used for content moderation on social media platforms to ensure compliance with diverse cultural values.
Long-term Vision
Global Cultural Exchange
Facilitates understanding and exchange between different cultures, reducing misunderstandings and biases.
Abstract
As large language models (LLMs) are employed worldwide, existing evaluation paradigms for their multilingual capabilities primarily focus on factual task performance, neglecting the ability to judge content's deep-level values across multiple languages. To bridge this gap, we first reveal two primary challenges in constructing values judgment benchmarks, cultural diversity and disciplinary complexity, and propose a novel two-stage human-AI collaborative annotation framework to alleviate them. This framework identifies the issue scope and nature, establishes specific annotation criteria, and utilizes multiple LLMs for final review. Building upon this framework, we introduce \textbf{X-Value}, the first \textit{Cross-lingual Values Judgment Benchmark} designed to evaluate the capability of LLMs in judging deep-level values of content. X-Value comprises 4,750 Question-Answer pairs across 14 languages, covering 7 major global issue categories, and provides 12 granular annotation metadata to facilitate a rigorous evaluation of model performance. Systematic evaluations of X-Value are conducted across 17 LLMs using distinct prompting strategies. Multi-dimensional analysis of accuracy and F1-scores reveals their limitations in cross-lingual values judgment and indicates performance disparities across categories and languages. This work highlights the urgent need to improve the underlying, values-aware content judgment capability of LLMs.\footnote{Samples of X-Value are available at https://huggingface.co/datasets/Whitolf/X-Value.}