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Found 11 papers, 4 papers with code
An Information Bottleneck Characterization of the Understanding-Workload Tradeoff
This empirical link between human factors and information-theoretic concepts provides an important mathematical characterization of the workload-understanding tradeoff which enables user-tailored XAI design.
Towards True Lossless Sparse Communication in Multi-Agent Systems
We evaluate the learned communication `language' through direct causal analysis of messages in non-sparse runs to determine the range of lossless sparse budgets, which allow zero-shot sparsity, and the range of sparse budgets that will inquire a reward loss, which is minimized by our learned gating function with few-shot sparsity.
Towards Human-Agent Communication via the Information Bottleneck Principle
Emergent communication research often focuses on optimizing task-specific utility as a driver for communication.
Prototype Based Classification from Hierarchy to Fairness
Artificial neural nets can represent and classify many types of data but are often tailored to particular applications -- e. g., for "fair" or "hierarchical" classification.
When Does Syntax Mediate Neural Language Model Performance? Evidence from Dropout Probes
Recent causal probing literature reveals when language models and syntactic probes use similar representations.
Probe-Based Interventions for Modifying Agent Behavior
Neural nets are powerful function approximators, but the behavior of a given neural net, once trained, cannot be easily modified.
Interpretable Learned Emergent Communication for Human-Agent Teams
In human-agent teams tested in benchmark environments, where agents have been modeled using the Enforcers, we find that a prototype-based method produces meaningful discrete tokens that enable human partners to learn agent communication faster and better than a one-hot baseline.
Emergent Discrete Communication in Semantic Spaces
Neural agents trained in reinforcement learning settings can learn to communicate among themselves via discrete tokens, accomplishing as a team what agents would be unable to do alone.
What if This Modified That? Syntactic Interventions via Counterfactual Embeddings
Neural language models exhibit impressive performance on a variety of tasks, but their internal reasoning may be difficult to understand.
Adversarially Guided Self-Play for Adopting Social Conventions
Robotic agents must adopt existing social conventions in order to be effective teammates.
