[Paper Review] "LazImpa": Lazy and Impatient neural agents learn to communicate efficiently

Mathieu Rita, Rahma Chaabouni+1 more|arXiv (Cornell University)|Oct 5, 2020

Language and cultural evolution29 references4 citations

TL;DR

This paper introduces 'LazImpa', a communication framework where a lazy speaker minimizes message length and an impatient listener predicts inputs as early as possible. By combining these constraints, the system achieves a Zipp-like abbreviation (ZLA) pattern—where frequent inputs use shorter messages—demonstrating efficient, human-like communication in neural agents.

ABSTRACT

Previous work has shown that artificial neural agents naturally develop surprisingly non-efficient codes. This is illustrated by the fact that in a referential game involving a speaker and a listener neural networks optimizing accurate transmission over a discrete channel, the emergent messages fail to achieve an optimal length. Furthermore, frequent messages tend to be longer than infrequent ones, a pattern contrary to the Zipf Law of Abbreviation (ZLA) observed in all natural languages. Here, we show that near-optimal and ZLA-compatible messages can emerge, but only if both the speaker and the listener are modified. We hence introduce a new communication system, "LazImpa", where the speaker is made increasingly lazy, i.e. avoids long messages, and the listener impatient, i.e.,~seeks to guess the intended content as soon as possible.

Motivation & Objective

• To address the inefficiency of emergent neural communication, where frequent inputs are encoded with longer messages, contrary to the Zipf Law of Abbreviation (ZLA).
• To investigate whether introducing specific behavioral constraints on speaker and listener can induce ZLA-compliant, efficient communication in neural agents.
• To determine whether both speaker laziness and listener impatience are necessary for the emergence of efficient, optimal-length codes.
• To develop a stable, task-agnostic communication framework that supports efficient code emergence without relying on task-specific inductive biases.
• To analyze the role of early exploration and dynamic regularization in avoiding suboptimal local minima during training.

Proposed method

• Introduce a 'lazy' speaker constraint that penalizes long messages only after successful communication, encouraging early exploration of short message patterns.
• Implement an 'impatient' listener that predicts the input at every symbol in the message stream, incentivizing informative content to appear early.
• Use a reconstruction game with a power-law distributed input space (1000 one-hot vectors) to model natural language word frequency distributions.
• Train speaker and listener networks via end-to-end backpropagation with a cross-entropy loss for input reconstruction and a length regularization term for the speaker.
• Apply a dynamic scheduling of the length penalty to allow exploration before enforcing brevity, preventing premature convergence to inefficient codes.
• Use a two-level evaluation protocol: (1) code efficiency metrics (e.g., message length vs. frequency), and (2) informativeness measurement via early prediction accuracy.

Experimental results

Research questions

• RQ1 Can neural agents learn to communicate efficiently, following the Zipf Law of Abbreviation (ZLA), where frequent inputs are encoded with shorter messages?
• RQ2 Is the emergence of ZLA-compliant communication dependent on both speaker laziness and listener impatience, or is one sufficient?
• RQ3 Does the dynamic scheduling of message length penalties enable better convergence to efficient codes compared to static or early regularization?
• RQ4 How do the positions of informative symbols in messages change under LazImpa compared to standard speaker-listener systems?
• RQ5 Can the LazImpa framework be generalized across different architectures and tasks without relying on task-specific inductive biases?

Key findings

• The standard speaker-listener system produces long messages with informative symbols concentrated at the end, resulting in an anti-ZLA pattern where frequent inputs are encoded with longer messages.
• Introducing only a lazy speaker constraint fails to produce efficient communication, as the listener's standard behavior maintains long messages due to reliance on late symbols.
• The impatient listener mechanism alone is insufficient to induce short messages, as it does not constrain the speaker's message length or structure.
• Only when both laziness (on speaker) and impatience (on listener) are combined does the system produce a ZLA-like distribution, with frequent inputs encoded in shorter messages.
• The LazImpa system achieves a message length distribution that closely approximates natural language efficiency, with a strong negative correlation between input frequency and message length.
• The system demonstrates stable optimization and avoids convergence to inefficient local minima, thanks to early exploration enabled by dynamic length regularization.