Understanding when two pieces of text convey the same information is a goal
touching many subproblems in NLP, including textual entailment and
fact-checking. This problem becomes more complex when those two pieces of text
are in different languages. Here, we introduce X-PARADE (Cross-lingual
Paragraph-level Analysis of Divergences and Entailments), the first
cross-lingual dataset of paragraph-level information divergences. Annotators
label a paragraph in a target language at the span level and evaluate it with
respect to a corresponding paragraph in a source language, indicating whether a
given piece of information is the same, new, or new but can be inferred. This
last notion establishes a link with cross-language NLI. Aligned paragraphs are
sourced from Wikipedia pages in different languages, reflecting real
information divergences observed in the wild. Armed with our dataset, we
investigate a diverse set of approaches for this problem, including classic
token alignment from machine translation, textual entailment methods that
localize their decisions, and prompting of large language models. Our results
show that these methods vary in their capability to handle inferable
information, but they all fall short of human performance.

X-PARADE 是第一个跨语言段落级别信息差异分析数据集，研究了不同语言之间的信息一致性问题，并探索了跨语言 NLI 的可能方法，结果表明各种方法都无法达到人类的表现水平。

X-PARADE: 跨语言文本蕴涵和段落信息差异

X-PARADE: Cross-Lingual Textual Entailment and Information Divergence  across Paragraphs

As major progress is made in open-ended text generation, measuring how close
machine-generated text is to human language remains a critical open problem. We
introduce MAUVE, a comparison measure for open-ended text generation, which
directly compares the learnt distribution from a text generation model to the
distribution of human-written text using divergence frontiers. MAUVE scales up
to modern text generation models by computing information divergences in a
quantized embedding space. Through an extensive empirical study on three
open-ended generation tasks, we find that MAUVE identifies known properties of
generated text, scales naturally with model size, and correlates with human
judgments, with fewer restrictions than existing distributional evaluation
metrics.

介绍了 MAUVE 作为一种比较度量开放式文本生成的测量方法，通过分析信息的分布差异来判断机器生成文本与人类语言之间的距离，实验结果表明，MAUVE 可以有效评估生成文本质量，并与人类判断高度相关。

利用发散前沿测量神经文本与人类文本之间的差距的 MAUVE

MAUVE: Measuring the Gap Between Neural Text and Human Text using  Divergence Frontiers

The replicator equation is interpreted as a continuous inference equation and
a formal similarity between the discrete replicator equation and Bayesian
inference is described. Further connections between inference and the
replicator equation are given including a discussion of information divergences
and exponential families as solutions for the replicator dynamic, using Fisher
information and information geometry.

本研究将复制子方程解释为连续推理方程，并描述了离散复制子方程与贝叶斯推理之间的形式相似性，进一步阐明了推理和复制子方程之间的联系，包括讨论信息分歧和指数族作为复制者动态解的解决方案，使用 Fisher 信息和信息几何。