Emergent Language: A Survey and Taxonomy (2409.02645v2)

Abstract: The field of emergent language represents a novel area of research within the domain of artificial intelligence, particularly within the context of multi-agent reinforcement learning. Although the concept of studying language emergence is not new, early approaches were primarily concerned with explaining human language formation, with little consideration given to its potential utility for artificial agents. In contrast, studies based on reinforcement learning aim to develop communicative capabilities in agents that are comparable to or even superior to human language. Thus, they extend beyond the learned statistical representations that are common in natural language processing research. This gives rise to a number of fundamental questions, from the prerequisites for language emergence to the criteria for measuring its success. This paper addresses these questions by providing a comprehensive review of 181 scientific publications on emergent language in artificial intelligence. Its objective is to serve as a reference for researchers interested in or proficient in the field. Consequently, the main contributions are the definition and overview of the prevailing terminology, the analysis of existing evaluation methods and metrics, and the description of the identified research gaps.

• The paper reviews 181 publications to detail how environmental factors and agent capabilities drive spontaneous language emergence in AI systems.
• The paper demonstrates that compositionality and learning efficiency are key to forming scalable and adaptable communication protocols.
• The paper identifies research gaps and advocates for standardized evaluation metrics to better validate emergent language phenomena.

The concept of emergent language has garnered significant interest within the AI community, particularly within the domain of multi-agent reinforcement learning (MARL). Emergent language research investigates the capability of artificial agents to develop and utilize language-like communication for improved task performance. This emerging field extends beyond traditional NLP and introduces several key areas for exploration.

Definition and Scope

Emergent language refers to the spontaneous development of a communication system among agents within a computational environment. This phenomenon is studied not just to understand human language evolution, but also to enhance the collaborative capabilities of AI systems. Agents interact within a structured environment and, through iterative processes driven by reinforcement learning, converge on a communication protocol that maximizes their collective performance.

Key Questions and Terminology

A comprehensive survey in this field provides a rigorous review of 181 publications, identifying fundamental questions such as:

1. Prerequisites for Language Emergence: What environmental conditions and agent capabilities are necessary for language to emerge?
2. Success Criteria: How can the effectiveness of emergent languages be measured and evaluated?

The survey also defines core terminology and concepts, establishes evaluation metrics, and points out existing research gaps (2409.02645).

Factors Influencing Language Emergence

Key factors influencing emergent language include:

• Environmental Pressures: Specific tasks or challenges that shape the language structure, such as the necessity for coordination in cooperative tasks (1906.02403).
• Compositionality: The ability of the emergent language to form novel composite concepts through systematic combination of simpler expressions, which can provide advantages in language transmission and generalization (2004.09124).
• Ease of Teaching: Sequential introduction of new agents to replace old ones, ensuring the emergent language is easy to learn and adapt over time (1906.02403).

Evaluation Metrics

The metrics used to evaluate emergent languages often dictate how the emergence is perceived. Some studies suggest that emergent abilities in LLMs might partly be artifacts of the chosen evaluation metrics rather than intrinsic model capabilities (2304.15004). This insight has led to a more critical stance on how emergent behaviors are measured and interpreted within the AI community.

Integration with LLMs

In the context of LLMs, emergent abilities represent a sudden and stark improvement in capabilities as models scale up. However, there is ongoing debate about whether these emergent abilities are genuine or merely artifacts of metric selection (2206.07682, 2304.15004, 2309.01809).

Research Challenges and Future Directions

The survey outlines several challenges and promising directions for future research:

• Better Understanding of Emergence: Further exploration into the dynamics that lead to the development of sophisticated communication protocols.
• Interdisciplinary Integration: Bridging insights from cognitive science, linguistics, and AI to enrich the understanding of emergent language systems.
• Standardized Evaluation Frameworks: Establishing transparent and consistent evaluation metrics to ensure the validity of observed emergent phenomena.

In summary, the paper of emergent language within multi-agent AI systems is a rapidly evolving field that holds promise for both theoretical insights and practical applications. It not only improves our understanding of language formation and communication but also enhances the development of more capable and collaborative AI agents. The comprehensive review of current literature serves as a critical resource for researchers aiming to explore this intriguing domain.