Scaling analysis is a technique in computational political science that
assigns a political actor (e.g. politician or party) a score on a predefined
scale based on a (typically long) body of text (e.g. a parliamentary speech or
an election manifesto). For example, political scientists have often used the
left--right scale to systematically analyse political landscapes of different
countries. NLP methods for automatic scaling analysis can find broad
application provided they (i) are able to deal with long texts and (ii) work
robustly across domains and languages. In this work, we implement and compare
two approaches to automatic scaling analysis of political-party manifestos:
label aggregation, a pipeline strategy relying on annotations of individual
statements from the manifestos, and long-input-Transformer-based models, which
compute scaling values directly from raw text. We carry out the analysis of the
Comparative Manifestos Project dataset across 41 countries and 27 languages and
find that the task can be efficiently solved by state-of-the-art models, with
label aggregation producing the best results.

计算政治学的技术之一是扩展分析，根据（通常较长的）文本集合（如议会演讲或选举宣言）给予政治参与者（如政治家或政党）一个预定义的评分。本研究通过实施和比较两种自动扩展分析政党宣言的方法：标签聚合和基于长输入的 Transformer 模型，对 41 个国家和 27 种语言的比较宣言项目数据集进行分析，发现最佳结果可通过先进的模型和标签聚合的方式高效解决。

多语言政党立场估计：从标签聚合到长输入变换器

Multilingual estimation of political-party positioning: From label  aggregation to long-input Transformers

Despite a growing interest in diffusion-based language models, existing work
has not shown that these models can attain nontrivial likelihoods on standard
language modeling benchmarks. In this work, we take the first steps towards
closing the likelihood gap between autoregressive and diffusion-based language
models, with the goal of building and releasing a diffusion model which
outperforms a small but widely-known autoregressive model. We pursue this goal
through algorithmic improvements, scaling laws, and increased compute. On the
algorithmic front, we introduce several methodological improvements for the
maximum-likelihood training of diffusion language models. We then study scaling
laws for our diffusion models and find compute-optimal training regimes which
differ substantially from autoregressive models. Using our methods and scaling
analysis, we train and release Plaid 1B, a large diffusion language model which
outperforms GPT-2 124M in likelihood on benchmark datasets and generates fluent
samples in unconditional and zero-shot control settings.

该研究基于算法改进和计算资源扩展来提升扩散式语言模型，成功构建了一款表现优于已有模型的大规模扩散语言模型 Plaid 1B。