Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis

This repository contain datasets and results for the paper:

Large Language Models are Easily Confused: A Quantitative Metric, Security Implications and Typological Analysis

Github repository for the code: 

Quantifying Language Confusion GitHub repo

DATA include the following datasets:

i) raw language graphs and

ii) the calculated language similarities from the language graphs,

iii) MTEI: the files from the experimental results of multilingual inversion attacks, and calculated language confusion entropy from the data;

iv) LCB: the files from the language confusion benchmark and calculated language confusion entropy from the data 

Results include aggregated results for further analysis:

i) inversion_language_confusion: results from MTEI

ii) prompting_language_confusion: results from LCB