Quantifying Language Disparities in Multilingual Large Language Models

《量化多语言大型语言模型中的语言差异》核心总结与关键部分翻译

一、文章主要内容总结

该研究聚焦多语言大型语言模型(MLLMs)评估中存在的碎片化、混淆变量干扰等问题,提出了一套可解释的测量框架,以精准量化模型间与语言间的性能差异,尤其关注低资源语言的公平评估。

1. 研究背景与问题

  • 数据不平衡与偏差:全球7000多种语言中,数字数据集对语言的代表性极不均衡,导致MLLMs存在内在偏差(影响模型内部表征)和适配偏差(影响下游任务性能)。
  • 现有评估局限:现有多语言评估结果碎片化,仅覆盖少数语言;分数受语言选择、训练与评估设置等多变量干扰,难以跨语言/数据集/指标比较;结论多为“语言性能不均衡”的定性判断,缺乏差异 magnitude 量化;易低估或高估低资源语言性能,阻碍语言公平性推进。

2. 核心框架设计(两步法)

(1)估计性能潜力(Performance Potential, PP)

基于测量理论与线性混合效应模型,分离语言、任务、模型的独立影响,定义某语言-任务对在当前NLP水平下的期望得分,公式为:
PPℓ,t:=E^m[sℓ,t,m]=μ+αℓ+βtPP_{

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nn.output = 'softmax'; % use softmax outputopts.numepochs = 5; % Number of full sweeps through dataopts.batchsize = 1000; % Take a mean gradient step over this many samplesopts.plot = 1; % enable plottingnn = nntrain(nn, tx, ty, opts, vx, vy); % nntrain takes validation set as last two arguments (optionally)[er, bad] = nntest(nn, test_x, test_y);assert(er < 0.1, 'Too big error');```[![Bitdeli Badge](https://d2weczhvl823v0.cloudfront.net/rasmusbergpalm/deeplearntoolbox/trend.png)](https://bitdeli.com/free "Bitdeli Badge")

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