Comprehensive Evaluation of Large Language Models' Understanding of Fundamental Social Norms

核心概念

Large language models have significantly advanced text understanding and generation, but there is a debate on whether these models are consistent with human and societal values and norms. This paper introduces a new challenge to test whether LLMs understand fundamental social norms, and proposes a multi-agent framework to improve their performance.

摘要

The paper presents a new dataset called "Social" to examine the ability of large language models (LLMs) to understand human social norms. The dataset consists of 12,383 high-quality multi-choice questions belonging to 402 skills, covering a wide range of social norms including rules, laws, culture, history, and communication.

The authors evaluate the performance of state-of-the-art LLMs, including GPT3.5-Turbo and LLaMA2-Chat, on the Social dataset. The results show that recent advancements in LLMs, particularly the use of reinforcement learning with human feedback (RLHF), have significantly improved the models' ability to understand social norms. However, the best-performing LLMs are still slightly below the performance of average elementary students.

To further enhance LLMs' understanding of social norms, the authors propose a multi-agent framework called "SocialAgent". SocialAgent integrates three LLM agents: a retrieval agent to collect relevant web knowledge, a programming agent to perform symbolic reasoning, and a reasoning agent to trigger step-by-step logical thinking. The ensemble of these agents helps LLMs reach parity with human performance on the Social dataset.

The paper also provides a detailed analysis of the dataset, including the skill distribution, grade-level performance, and case studies. The findings suggest that while LLMs have made progress in understanding fundamental social norms, there is still significant room for improvement, especially in more advanced social norm skills.

統計資料

The Social dataset contains 12,383 multi-choice questions covering 402 unique skills across two subjects: social studies and language arts.

引述

"Social norms are social and shared among members of a group. It includes topics representing socially acceptable ways of living by a group of people in a society, such as rules, laws, culture, history, and communication."
"Understanding these skills is important to the wide adoption of LLMs."
"SocialAgent helps improve the zero-shot performance in understanding social norms."

從以下內容提煉的關鍵洞見

Measuring Social Norms of Large Language Models

by Ye Yuan,Kexi... 於 arxiv.org 04-04-2024

https://arxiv.org/pdf/2404.02491.pdf

深入探究

How can the Social dataset be expanded to include more diverse cultural and linguistic perspectives beyond the U.S. education system?

To expand the Social dataset to include more diverse cultural and linguistic perspectives beyond the U.S. education system, several steps can be taken:

Collaboration with International Experts: Collaborate with experts from different countries and cultures to identify key social norms and educational standards that should be included in the dataset. This will ensure a more global perspective.

Incorporation of Multilingual Content: Translate the existing dataset into multiple languages to make it accessible to a wider audience. Additionally, include questions and skills that are specific to different languages and cultures.

Research on Global Social Norms: Conduct research on social norms and educational curricula from various countries to identify commonalities and differences. Incorporate these findings into the dataset.

Community Feedback: Seek feedback from a diverse group of individuals representing different cultural backgrounds to ensure that the dataset is inclusive and representative of a wide range of perspectives.

Pilot Testing: Conduct pilot testing of the expanded dataset with participants from different cultural backgrounds to validate the relevance and accuracy of the questions and skills included.

By incorporating these strategies, the Social dataset can be expanded to encompass a more diverse range of cultural and linguistic perspectives, making it more comprehensive and inclusive.

What are the potential biases and limitations in the current design of the Social dataset, and how can they be addressed?

Biases and Limitations:

Cultural Bias: The dataset may have a bias towards U.S. social norms and educational standards, potentially overlooking the diversity of global perspectives.

Language Bias: The dataset may be limited to English, excluding non-English speakers and regions where English is not the primary language.

Educational System Bias: The dataset may focus heavily on the U.S. K-12 curriculum, neglecting other educational systems and standards.

Addressing Biases and Limitations:

Diversification: Collaborate with international experts to incorporate a more diverse range of cultural and linguistic perspectives into the dataset.

Translation: Translate the dataset into multiple languages to make it accessible to a broader audience and reduce language bias.

Inclusion of Global Social Norms: Conduct thorough research on global social norms and educational systems to ensure a more comprehensive representation in the dataset.

Community Engagement: Engage with a diverse community of users to gather feedback and insights on potential biases and limitations, and make necessary adjustments.

Regular Updates: Continuously update and refine the dataset to address emerging biases and ensure it remains relevant and inclusive.

By actively addressing these biases and limitations, the Social dataset can become more robust, diverse, and reflective of a wide range of social norms and educational perspectives.

How can the multi-agent framework of SocialAgent be further improved to better capture the nuances and complexities of social norms across different contexts and domains?

Improvements to SocialAgent:

Enhanced Knowledge Integration: Incorporate a wider range of knowledge sources beyond Wikipedia to provide more comprehensive context for answering social norm questions.

Domain-Specific Agents: Develop specialized agents for different domains (e.g., legal, cultural, historical) to better capture the nuances of social norms in specific contexts.

Contextual Understanding: Implement mechanisms for the agents to understand and interpret context-specific information to provide more accurate responses.

Cross-Domain Reasoning: Enable the agents to perform cross-domain reasoning to address complex social norm questions that span multiple domains.

Adaptive Learning: Implement adaptive learning mechanisms to allow the agents to continuously improve their understanding of social norms based on user interactions and feedback.

Ethical Considerations: Integrate ethical considerations into the framework to ensure that the agents adhere to ethical principles and societal values when providing responses.

Evaluation and Validation: Establish robust evaluation metrics and validation processes to assess the performance of the agents in capturing the nuances and complexities of social norms across different contexts and domains.

By implementing these improvements, the multi-agent framework of SocialAgent can be enhanced to better capture the intricacies of social norms and provide more accurate and contextually relevant responses across diverse contexts and domains.