# Continual adaptation for efficient machine communication

Source: [Continual adaptation for efficient machine communication](<https://www.sota2.com/research/paper/68fa687e1e209131cea1c508>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:38:00.142Z

Authors: Robert D. Hawkins, Minae Kwon, Dorsa Sadigh, Noah D. Goodman

Published: 2019-11-22T07:26:40.000Z

## Abstract

To communicate with new partners in new contexts, humans rapidly form new linguistic conventions. Recent neural language models are able to comprehend and produce the existing conventions present in their training data, but are not able to flexibly and interactively adapt those conventions on the fly as humans do. We introduce an interactive repeated reference task as a benchmark for models of adaptation in communication and propose a regularized continual learning framework that allows an artificial agent initialized with a generic language model to more accurately and efficiently communicate with a partner over time. We evaluate this framework through simulations on COCO and in real-time reference game experiments with human partners.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1911.09896>)

[PDF](<https://arxiv.org/pdf/1911.09896.pdf>)