How can agents communicate meaningfully in collaborative tasks?
Semantics and Spatiality of Emergent Communication
This paper investigates how different training objectives in emergent communication (EC) affect the meaningfulness of the communication protocols that agents develop. It focuses on two common EC training tasks: reconstruction, where an agent tries to recreate an input based on another agent's message about it, and discrimination, where an agent tries to identify the correct input from a set of candidates based on the message.
The key takeaway for LLM-based multi-agent systems is that the reconstruction objective tends to lead to more semantically consistent communication protocols, where similar inputs map to similar messages. This is a desirable property for meaningful communication. In contrast, the discrimination objective, while often leading to good task performance, can result in less meaningful, even counterintuitive, communication protocols where the relationship between inputs and messages is more arbitrary. This suggests that for developing LLM-based multi-agent systems with meaningful communication, distance-based objectives like reconstruction might be preferred over probability-based objectives like discrimination, especially when interpretability and generalization are important.