How can LLMs learn emergent language?
A Survey on Emergent Language
September 5, 2024
https://arxiv.org/pdf/2409.02645This paper surveys the field of Emergent Language (EL), where artificial intelligence agents develop their own communication systems without explicit programming. It explores how agents create and adapt linguistic structures like humans do, moving beyond simply understanding and using pre-existing natural language (NL) like current LLMs.
Key points for LLM-based multi-agent systems:
- EL systems can offer a deeper, more functional understanding of language compared to LLMs: LLMs excel at imitation but struggle with understanding the underlying purpose and meaning of communication, a gap that EL aims to bridge.
- The paper proposes a taxonomy to standardize EL research: This addresses the inconsistencies in terminology and evaluation methods currently hindering the field.
- It analyzes various metrics to quantify EL characteristics: This analysis can guide the development of more measurable and interpretable EL systems, potentially enhancing future LLM capabilities.
- It highlights the importance of aligning EL with NL: This alignment is crucial for enabling seamless human-agent communication, a key goal of both EL and advanced LLM development.
- The paper advocates for studying the interaction between EL and representation learning: This can shed light on how agents develop internal representations of language, potentially advancing our understanding of how meaning is constructed and processed.