Communicate to Play: Pragmatic Reasoning for Efficient Cross-Cultural Communication in Codenames

Abstract: Cultural differences in common ground may result in pragmatic failure and misunderstandings during communication. We develop our method Rational Speech Acts for Cross-Cultural Communication (RSA+C3) to resolve cross-cultural differences in common ground. To measure the success of our method, we study RSA+C3 in the collaborative referential game of Codenames Duet and show that our method successfully improves collaboration between simulated players of different cultures. Our contributions are threefold: (1) creating Codenames players using contrastive learning of an embedding space and LLM prompting that are aligned with human patterns of play, (2) studying culturally induced differences in common ground reflected in our trained models, and (3) demonstrating that our method RSA+C3 can ease cross-cultural communication in gameplay by inferring sociocultural context from interaction. Our code is publicly available at github.com/icwhite/codenames.

• The paper developed RSA+C3, an enhanced pragmatic reasoning framework that integrates cultural context to improve communication in gameplay.
• It utilized contrastive learning and LLM prompting to simulate culturally diverse Codenames players, aligning their behavior with human patterns.
• Evaluation metrics showed increased win rates and guess accuracy, demonstrating the framework's effectiveness in mitigating cross-cultural pragmatic failures.

An Analysis of Pragmatic Reasoning for Cross-Cultural Communication in Codenames Duet

Introduction

The paper "Communicate to Play: Pragmatic Reasoning for Efficient Cross-Cultural Communication in Codenames" by Isadora White, Sashrika Pandey, and Michelle Pan, presents a comprehensive study on addressing cross-cultural communication through Pragmatic Reasoning and Rational Speech Acts (RSA). The work aims to model and mitigate pragmatic failure—confusion arising from differing socio-cultural contexts—by proposing an enhanced RSA framework, RSA+C3, which adapts to cultural discrepancies during interaction in the game "Codenames Duet."

Overview

The authors focus on several key aspects:

1. Creation and Modeling of Codenames Players: Utilizing contrastive learning and LLM-prompting, the study models players aligned with human gameplay patterns.
2. Cultural Context Modeling: The paper explores how trained models reflect socio-cultural differences.
3. Pragmatic Reasoning with RSA+C3: Evaluating RSA+C3's effectiveness in inferring socio-cultural context, thus improving cross-cultural communication during gameplay.

Modeling Players with Word Embeddings and LLMs

The researchers developed simulated Codenames players using two methodologies: word embeddings and LLM-prompting. Word embeddings were trained on specific cultural data splits derived from the Cultural Codes dataset, capturing demographic attributes like education, country, and political leaning. By employing contrastive learning, the embeddings were optimized to increase similarity for culturally aligned clues and guesses.

The evaluation metrics, such as "Giver target accuracy" and "Guess accuracy," illustrated the effectiveness of these embeddings. Results showed a pronounced improvement in the alignment of simulated players' behaviors with actual human data, emphasizing that cultural context significantly influences gameplay mechanics.

Incorporating Cultural Context

The paper dives into how differing cultural backgrounds affect gameplay. By training and testing models on culturally distinct subsets of the dataset, the findings revealed noticeable variations in player behavior. For example, using demographic prompts, such as native language or political affiliation, the authors could demonstrate improved alignment of guesses and clues in their LLM models.

Pragmatic Reasoning with RSA and RSA+C3

The standard RSA framework was extended to RSA+C3 to account for cross-cultural differences. The traditional RSA model relies on shared common ground between interlocutors. In contrast, RSA+C3 incorporates an adaptive component that learns from interaction, dynamically inferring socio-cultural context.

Interactive evaluation results demonstrated that RSA+C3 significantly enhanced the win rates in cross-cultural player simulations, compared to the baseline RSA and literal models. This confirmed that pragmatic reasoning, augmented with cultural adaptation, leads to more effective communication and collaborative success in Codenames Duet.

Implications and Future Directions

From a theoretical perspective, the research advances our understanding of pragmatic reasoning in AI communication systems. By integrating cultural inference mechanisms, RSA+C3 holds promise for a broader application in multi-agent systems and real-world scenarios where cultural diversity is prevalent.

Practically, these findings could be extended to various domains, such as educational tools and international collaborations, where efficient cross-cultural communication is paramount. Future research might explore more complex interaction scenarios and broader demographic datasets to refine and generalize the RSA+C3 framework further.

Conclusion

The paper provides a meticulous approach to addressing pragmatic failures in cross-cultural communication, underlined by rigorous modeling and evaluation of player interactions in Codenames Duet. By incorporating a dynamic, culture-aware layer to the RSA framework, the study significantly enhances our capability to design AI systems that robustly navigate the intricacies of socio-cultural contexts.