Recent work has shown that, while large language models (LLMs) demonstrate
strong word translation or bilingual lexicon induction (BLI) capabilities in
few-shot setups, they still cannot match the performance of 'traditional'
mapping-based approaches in the unsupervised scenario where no seed translation
pairs are available, especially for lower-resource languages. To address this
challenge with LLMs, we propose self-augmented in-context learning (SAIL) for
unsupervised BLI: starting from a zero-shot prompt, SAIL iteratively induces a
set of high-confidence word translation pairs for in-context learning (ICL)
from an LLM, which it then reapplies to the same LLM in the ICL fashion. Our
method shows substantial gains over zero-shot prompting of LLMs on two
established BLI benchmarks spanning a wide range of language pairs, also
outperforming mapping-based baselines across the board. In addition to
achieving state-of-the-art unsupervised BLI performance, we also conduct
comprehensive analyses on SAIL and discuss its limitations.

最近的研究表明，尽管大型语言模型在少样本设置中展示了强大的词汇翻译或双语词典归纳能力，但在无监督场景中（特别是对于资源较少的语言），它们仍无法与 ' 传统 ' 的基于映射方法的性能相媲美。为了解决这一挑战，我们提出了自我增强的上下文学习（SAIL）用于无监督的双语词典归纳：从零样本提示开始，SAIL 迭代地通过从一个 LLM 中归纳一组高置信度的词汇翻译对用于上下文学习（ICL），然后再次应用于同一个 LLM 进行 ICL。我们的方法在两个广泛覆盖的双语词典归纳基准测试中都显著优于 LLMs 的零样本提示，并且在整体上优于基于映射的基准线。除了达到现有无监督双语词典归纳性能的最新水平外，我们还对 SAIL 进行了全面的分析，并讨论了其局限性。

自我增强上下文学习用于无监督词语翻译

Self-Augmented In-Context Learning for Unsupervised Word Translation

Domain adaptation for visual recognition has undergone great progress in the
past few years. Nevertheless, most existing methods work in the so-called
closed-set scenario, assuming that the classes depicted by the target images
are exactly the same as those of the source domain. In this paper, we tackle
the more challenging, yet more realistic case of open-set domain adaptation,
where new, unknown classes can be present in the target data. While, in the
unsupervised scenario, one cannot expect to be able to identify each specific
new class, we aim to automatically detect which samples belong to these new
classes and discard them from the recognition process. To this end, we rely on
the intuition that the source and target samples depicting the known classes
can be generated by a shared subspace, whereas the target samples from unknown
classes come from a different, private subspace. We therefore introduce a
framework that factorizes the data into shared and private parts, while
encouraging the shared representation to be discriminative. Our experiments on
standard benchmarks evidence that our approach significantly outperforms the
state-of-the-art in open-set domain adaptation.

我们提出了一个能够将数据因子化为共享和私有部分并鼓励共享表示具有判别性的框架，以自动检测目标数据中出现的新类别并将其丢弃，从而在开放域适应中显著优于现有技术。