Survey on Cultural Representation in Large Language Models

Conceitos essenciais

Studies on cultural representation in large language models lack a unified definition of culture, focus mainly on values and norms, lack robustness in methods, and require more interdisciplinary studies.

Resumo

The survey analyzes 39 recent papers on cultural representation in LLMs.
Studies probe models using black-box approaches with input prompts under different cultural conditions.
Most studies focus on values and norms as proxies for culture, neglecting other semantic domains.
Recommendations include defining culture explicitly, exploring unexplored aspects of culture, improving interpretability and robustness of methods, incorporating interdisciplinary perspectives, and creating multilingual datasets.

Estatísticas

"We present a survey of 39 recent papers that aim to study cultural representation and inclusion in large language models."
"Our analysis indicates that only certain aspects of “culture,” such as values and objectives, have been studied."
"Based on these observations, we provide several recommendations for a holistic and practically useful research agenda for furthering cultural inclusion in LLMs."

Citações

"We call for a more explicit acknowledgment of the link between the datasets employed and the facets of culture studied."
"Most studies however focus on the Objectives and Values axes of the Hershcovich et al. scheme."

Principais Insights Extraídos De

Towards Measuring and Modeling "Culture" in LLMs

by Muhammad Far... às arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15412.pdf

Perguntas Mais Profundas

How can interdisciplinary studies enhance our understanding of cultural biases in LLMs?

Interdisciplinary studies play a crucial role in enhancing our understanding of cultural biases in Large Language Models (LLMs) by bringing together insights from various fields such as anthropology, sociology, psychology, and human-computer interaction. Here are some ways in which interdisciplinary studies can contribute:

Cultural Context: Anthropology provides valuable perspectives on how culture shapes language use and communication patterns. By incorporating anthropological theories into the study of LLMs, researchers can better understand how cultural biases manifest in language models.

Human-Centered Design: Human-computer interaction research focuses on designing technology that aligns with human needs and values. Applying principles from HCI to the development and evaluation of LLMs can help identify and address cultural biases that may impact user experiences.

Psychological Insights: Psychological research offers insights into cognitive processes related to perception, bias, and decision-making. By integrating psychological frameworks into the study of LLMs, researchers can gain a deeper understanding of how cultural biases influence model behavior.

Ethical Considerations: Ethics is a critical aspect when studying cultural biases in AI systems like LLMs. Ethicists bring ethical frameworks and considerations to ensure responsible research practices and mitigate potential harm caused by biased models.

Diverse Perspectives: Interdisciplinary collaboration brings together diverse perspectives that enrich the analysis of cultural biases in LLMs. Different disciplines offer unique lenses through which to examine complex issues related to culture and language technology.

By embracing interdisciplinary approaches, researchers can develop more comprehensive analyses of cultural biases in LLMs, leading to more informed decisions about model development, deployment, and societal impact.

What are the implications of neglecting certain semantic domains in studying cultural representation?

Neglecting certain semantic domains in studying cultural representation within Large Language Models (LLMs) has several significant implications:

Incomplete Understanding: Semantic domains such as quantity, kinship terms, spatial relations hold rich information about culture-specific nuances that shape communication patterns within a community or group.

Biased Representations: Ignoring specific semantic domains may lead to biased representations within LLMs as these aspects play a crucial role in shaping culturally appropriate language use.

3Limited Generalizability: Neglecting certain semantic domains hinders the generalizability of findings across different cultures or languages since each domain contributes uniquely to the overall fabric of culture.
4Missed Opportunities for Improvement: Overlooking key semantic domains limits opportunities for improving cross-cultural awareness within language models by addressing gaps where bias might be most prevalent.
5Reduced Effectiveness: Failure to consider all relevant semantic domains diminishes the effectiveness of efforts aimed at promoting diversity and inclusivity within NLP systems.

How can we ensure reliability & generalizability when probing LLMS for Cultural Awareness?

Ensuring reliability & generalizability when probing Large Language Models (LLMs) for Cultural Awareness involves implementing robust methodologies & strategies:
1Standardized Evaluation Protocols: Establish standardized protocols for evaluating model responses under different cultural conditions using consistent prompts & benchmarks across studies
2Cross-Validation Techniques: Employ cross-validation techniques where multiple subsets are used alternately for training/testing data ensuring results' consistency
3Diverse Dataset Creation: Create diverse datasets covering various demographic proxies & linguistic-cultural interactions reflecting real-world complexities
4White-Box Approaches: Incorporate white-box approaches alongside black-box methods allowing internal states observation providing interpretability
5**Peer Review Process: Implement rigorous peer review processes involving experts from diverse backgrounds ensuring methodological soundness & validity
By adopting these measures collectively along with transparent reporting standards will enhance trustworthiness& applicabilityof findings regardingcultural awarenessin LLMS