# XferBench: a Data-Driven Benchmark for Emergent Language

**Authors:** Brendon Boldt, David Mortensen  
**arXiv:** [2407.03456](https://arxiv.org/abs/2407.03456)  
**Date:** 2024-07-03  
**Summary page:** https://www.summarizepaper.com/fr/arxiv-id/2407.03456v1/

## Abstract

In this paper, we introduce a benchmark for evaluating the overall quality of emergent languages using data-driven methods. Specifically, we interpret the notion of the "quality" of an emergent language as its similarity to human language within a deep learning framework. We measure this by using the emergent language as pretraining data for a downstream NLP tasks in human language -- the better the downstream performance, the better the emergent language. We implement this benchmark as an easy-to-use Python package that only requires a text file of utterances from the emergent language to be evaluated. Finally, we empirically test the benchmark's validity using human, synthetic, and emergent language baselines.