Extrinsic Evaluation of Cultural Competence in Large Language Models (2406.11565v3)

Abstract: Productive interactions between diverse users and language technologies require outputs from the latter to be culturally relevant and sensitive. Prior works have evaluated models' knowledge of cultural norms, values, and artifacts, without considering how this knowledge manifests in downstream applications. In this work, we focus on extrinsic evaluation of cultural competence in two text generation tasks, open-ended question answering and story generation. We quantitatively and qualitatively evaluate model outputs when an explicit cue of culture, specifically nationality, is perturbed in the prompts. Although we find that model outputs do vary when varying nationalities and feature culturally relevant words, we also find weak correlations between text similarity of outputs for different countries and the cultural values of these countries. Finally, we discuss important considerations in designing comprehensive evaluation of cultural competence in user-facing tasks.

Evaluation of Cultural Competence in LLMs

The paper, "Extrinsic Evaluation of Cultural Competence in LLMs," focuses on evaluating the cultural competence of LLMs for text generation tasks. Cultural competence here is defined as the ability of LLMs to effectively interact with users from diverse sociocultural backgrounds. Recognizing that user interactions with language technologies should be culturally relevant, it studies LLM outputs to identify adaptations made to accommodate cultural differences.

The evaluation focuses on two specific text generation tasks: open-ended question answering and story generation. The evaluation is extrinsic in nature, contrasting it with intrinsic evaluations that typically assess LLMs' inherent knowledge of cultural norms, values, and artifacts. The research consists of two main components: the variation in outputs when cultural cues are presented, and the presence of culturally relevant vocabulary in model outputs.

Three key research questions guide this paper:

1. Do model outputs vary when explicit cues of culture are present in the input prompt?
2. Do model outputs contain culturally relevant vocabulary?
3. Are model outputs for countries with similar cultural values also similar?

The paper explores these questions by perturbing prompts with explicit cultural cues, in this case, nationalities, and analyzing model outputs both quantitatively and qualitatively.

Methodology and Findings

The methodology involves evaluating six LLMs across 195 nationalities using two major tasks: open-ended QA and story generation. Thousands of prompts are generated, with 5 responses sampled for each prompt per model, across two tasks, creating a large corpus of model output for analysis.

1. Lexical Variation: The paper finds non-trivial variations in outputs when nationalities are perturbed in prompts, particularly for the story generation task. This signifies that models adapt their outputs based on cultural cues. The variance in outputs is quantified using word edit distance, ensuring that variations due to prompt perturbations are statistically significant. An ANOVA test confirms that the variance across nationalities is significantly greater than variance within the same nationality prompt outputs.
2. Presence of Culturally Relevant Vocabulary: The existence of culturally specific vocabulary in outputs hints at models' adaptation to explicit cultural cues. Analysis using TF-IDF highlights words associated with specific cultures in both tasks, signifying some level of cultural awareness within the models' outputs. In story generation, for example, names and culturally relevant artifacts appear frequently, while the QA outputs demonstrated adaptation in political terms and institutions that are unique to each culture.
3. Correlation with Cultural Values: The paper finds weak correlations between the distribution of text outputs and established cross-cultural psychological measures, like Hofstede's Cultural Dimensions and the World Values Survey. The lack of strong correlation suggests a disconnect between intrinsic measures of cultural values and how these are expressed in LLM outputs during real-world task performance.

Implications and Future Research

The findings raise several implications for the advancement of AI and cultural competence in LLMs. Firstly, they highlight the importance of evaluating cultural competence extrinsically, through tasks likely to represent user interactions with LLMs. Intrinsic evaluations are not adequately reflective of cultural competence in user-facing applications, which suggests the development of more holistic evaluation frameworks that consider output adaptation and user experience.

Further, the paper acknowledges the need for comprehensive human evaluation that accounts for the contextuality and potential representational harms of cultural adaptations in LLM outputs. It calls into question whether adaptations triggered by implicit and explicit cultural cues are appropriate or desired by all users.

Finally, the paper recognizes the complexity of culture and suggests that future work should incorporate dynamic evaluation strategies that account for the multifaceted and evolving nature of culture. Participatory design could offer a path towards more inclusive and intersectionally aware evaluations.

In conclusion, the paper presents novel insights into the cultural competence of LLMs in dynamic tasks, emphasizing the necessity for continued research in this space. Future developments in the domain of AI should focus on building richly embedded cultural understandings and responsive adaptability, challenged by the limitations uncovered in this research.