# 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/en/arxiv-id/2407.03456v1/

## Key points

- XferBench is a benchmark for evaluating emergent languages using data-driven methods
- Focuses on assessing similarity of emergent languages to human language within deep learning framework
- Utilizes emergent language as pretraining data for downstream natural language processing tasks
- Benchmark emphasizes simplicity and ease of use, requiring only a text file with utterances from the emergent language
- Designed to evaluate emergent communication systems that generate utterances represented as sequences of discrete tokens
- Emphasizes typological diversity in downstream tasks involving multiple human languages for comprehensive assessment
- Offers valuable insights into quality and effectiveness of emerging communication systems through empirical testing against various datasets
- Provides standardized and accessible benchmarking tool for evaluating emergent languages within deep learning frameworks

## Summary

The paper "XferBench: a Data-Driven Benchmark for Emergent Language" introduces a novel benchmark for evaluating the quality of emergent languages using data-driven methods. The benchmark focuses on assessing the similarity of emergent languages to human language within a deep learning framework. This is achieved by utilizing the emergent language as pretraining data for downstream natural language processing tasks in human language, with better performance on these tasks indicating higher quality in the emergent language. One key aspect of the benchmark is its simplicity and ease of use, as it only requires a text file containing utterances from the emergent language to be evaluated. By providing a practical tool that can be easily accessed and utilized by researchers across various fields, XferBench aims to contribute to the broader research community's efforts in studying emergent languages. The design goal of XferBench emphasizes the use of textual corpora as input, limiting its applicability to emergent communication systems that generate utterances represented as sequences of discrete tokens. While this choice may restrict richer representations that incorporate grounded semantics or non-verbal behavior, it enables efficient evaluation across different EC systems without significant implementation costs. Furthermore, XferBench's emphasis on typological diversity in downstream tasks involving multiple human languages ensures a comprehensive assessment of emergent languages' practical utility in machine learning contexts. By capturing a notion of similarity between emergent and human languages through empirical testing against human, synthetic, and baseline datasets, XferBench offers valuable insights into the quality and effectiveness of emerging communication systems. Overall, XferBench provides a valuable contribution to the field by offering a standardized and accessible benchmarking tool for evaluating emergent languages' overall quality within deep learning frameworks. Through empirical validation and testing against diverse datasets, this benchmark serves as an essential resource for researchers seeking to assess and compare different types of emerging communication systems effectively.