[Paper Review] Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication

Łukasz Kuciński, Tomasz Korbak+2 more|arXiv (Cornell University)|Nov 11, 2021

Cognitive Science and Education ResearchNeuroscience20 citations

TL;DR

This paper demonstrates that noise in communication channels acts as a catalytic force for the emergence of compositional language in multi-agent signaling games, provided that inductive biases are present in both the training framework and data. Theoretically and empirically, it shows that a specific range of noise levels—dependent on model and data—promotes compositionality, as measured by topographic similarity, conflict count, and context independence.

ABSTRACT

Communication is compositional if complex signals can be represented as a combination of simpler subparts. In this paper, we theoretically show that inductive biases on both the training framework and the data are needed to develop a compositional communication. Moreover, we prove that compositionality spontaneously arises in the signaling games, where agents communicate over a noisy channel. We experimentally confirm that a range of noise levels, which depends on the model and the data, indeed promotes compositionality. Finally, we provide a comprehensive study of this dependence and report results in terms of recently studied compositionality metrics: topographical similarity, conflict count, and context independence.

Motivation & Objective

• To investigate the conditions under which compositional communication spontaneously emerges in multi-agent signaling games.
• To challenge the misconception that compositionality can arise purely through unsupervised learning without inductive biases.
• To examine the role of noise in facilitating the emergence of compositional communication protocols.
• To evaluate the impact of inductive biases in network architecture, training procedures, and data preprocessing on compositionality metrics.
• To assess the generalization capabilities of noise-regularized communication protocols in zero-shot and few-shot settings.

Proposed method

• Theoretical analysis proves that inductive biases in both the training framework and data are necessary for compositionality to emerge in signaling games.
• A noise-regularized training setup is introduced, where communication occurs over a noisy channel with controlled noise levels.
• Inductive biases are encoded in the loss function and network architecture to encourage disentangled, compositional representations.
• Compositionality is evaluated using established metrics: topographic similarity, conflict count, and context independence.
• Experiments include ablation studies on noise levels, data scrambling (visual and textual), and architectural variations (e.g., residual connections).
• Generalization is tested via zero-shot and fine-tuning protocols on out-of-distribution examples, with metrics tracked over incremental training updates.

Experimental results

Research questions

• RQ1 Under what conditions does compositionality spontaneously emerge in multi-agent signaling games?
• RQ2 What is the catalytic role of noise in promoting compositional communication when combined with inductive biases?
• RQ3 How do different inductive biases—on architecture, training procedure, and data—impact the emergence of compositionality?
• RQ4 To what extent does noise-regularized training improve generalization in zero-shot and few-shot settings?
• RQ5 Can the proposed framework generalize to non-synthetic datasets, and how do compositionality metrics vary across different noise levels?

Key findings

• Inductive biases in both the training framework and data are necessary for compositionality to emerge, challenging the idea that it can arise purely through unsupervised learning.
• Noise in the communication channel acts as a catalyst for compositionality when combined with inductive biases, with a specific optimal range of noise levels promoting the highest compositionality.
• Topographic similarity, conflict count, and context independence metrics all show significant improvement under noise-regularized training, with peak performance observed at intermediate noise levels.
• The sender network learns disentangled representations, as evidenced by t-SNE visualizations showing clearer separation of feature dimensions in the latent space compared to raw data.
• Zero-shot generalization performance is poor, but fine-tuning with just 50 additional network updates leads to a substantial increase in compositionality metrics, indicating strong inductive bias from the training protocol.
• Scrambling visual inputs or their textual descriptions reduces compositionality, confirming that data-level inductive biases are critical for the emergence of structured communication.