Modern ML systems ingest data aggregated from diverse sources, such as
synthetic, human-annotated, and live customer traffic. Understanding
\textit{which} examples are important to the performance of a learning
algorithm is crucial for efficient model training. Recently, a growing body of
literature has given rise to various "influence scores," which use training
artifacts such as model confidence or checkpointed gradients to identify
important subsets of data. However, these methods have primarily been developed
in computer vision settings, and it remains unclear how well they generalize to
language-based tasks using pretrained models.
In this paper, we explore the applicability of influence scores in language
classification tasks. We evaluate a diverse subset of these scores on the SNLI
dataset by quantifying accuracy changes in response to pruning training data
through random and influence-score-based sampling. We then stress-test one of
the scores -- "variance of gradients" (VoG) from Agarwal et al. (2022) -- in an
NLU model stack that was exposed to dynamic user speech patterns in a voice
assistant type of setting. Our experiments demonstrate that in many cases,
encoder-based language models can be finetuned on roughly 50% of the original
data without degradation in performance metrics. Along the way, we summarize
lessons learned from applying out-of-the-box implementations of influence
scores, quantify the effects of noisy and class-imbalanced data, and offer
recommendations on score-based sampling for better accuracy and training
efficiency.

应用影响力分数评估语言分类任务，通过修剪训练数据来量化准确性变化，并提供基于分数采样的建议，以提高准确性和训练效率。

高效语言数据抽样的规模化影响分数

Influence Scores at Scale for Efficient Language Data Sampling

Text-to-image models take a sentence (i.e., prompt) and generate images
associated with this input prompt. These models have created award wining-art,
videos, and even synthetic datasets. However, text-to-image (T2I) models can
generate images that underrepresent minorities based on race and sex. This
paper investigates which word in the input prompt is responsible for bias in
generated images. We introduce a method for computing scores for each word in
the prompt; these scores represent its influence on biases in the model's
output. Our method follows the principle of \emph{explaining by removing},
leveraging masked language models to calculate the influence scores. We perform
experiments on Stable Diffusion to demonstrate that our method identifies the
replication of societal stereotypes in generated images.

本文的研究目的是探讨文本转图像模型（T2I）如何在生成图像时通过特定的单词体现出种族和性别的偏见，作者通过遮蔽语言模型计算各单词的影响得分，实验结果表明该方法能够用于识别生成图像中的社会刻板印象。