Recent studies have shown that leveraging off-the-shelf or fine-tuned
retrievers, capable of retrieving high-quality in-context examples,
significantly improves in-context learning of English. However, adapting these
methods to other languages, especially low-resource ones, presents challenges
due to the scarcity of available cross-lingual retrievers and annotated data.
In this paper, we introduce XAMPLER: Cross-Lingual Example Retrieval, a method
tailored to tackle the challenge of cross-lingual in-context learning using
only annotated English data. XAMPLER first trains a retriever with
positive/negative English samples, which are constructed based on the
predictions of the multilingual large language model for in-context learning.
Then, the trained retriever is directly employed to retrieve English examples
as few-shot examples for in-context learning of target languages. Experiments
on the massively multilingual text classification benchmark of SIB200 with 176
languages demonstrate that XAMPLER substantially improves the in-context
learning performance across languages. Our code is available at
this https URL

通过仅使用英语注释数据，我们介绍了一种名为 XAMPLER 的跨语言示例检索方法，用于解决跨语言上下文学习的挑战，并在 176 种语言的大规模多语言文本分类基准测试中展示了其显著改进的跨语言上下文学习性能。

XAMPLER：跨语言背景中检索示例的学习

XAMPLER: Learning to Retrieve Cross-Lingual In-Context Examples

In this paper, we introduce SUTRA, multilingual Large Language Model
architecture capable of understanding, reasoning, and generating text in over
50 languages. SUTRA's design uniquely decouples core conceptual understanding
from language-specific processing, which facilitates scalable and efficient
multilingual alignment and learning. Employing a Mixture of Experts framework
both in language and concept processing, SUTRA demonstrates both computational
efficiency and responsiveness. Through extensive evaluations, SUTRA is
demonstrated to surpass existing models like GPT-3.5, Llama2 by 20-30% on
leading Massive Multitask Language Understanding (MMLU) benchmarks for
multilingual tasks. SUTRA models are also online LLMs that can use knowledge
from the internet to provide hallucination-free, factual and up-to-date
responses while retaining their multilingual capabilities. Furthermore, we
explore the broader implications of its architecture for the future of
multilingual AI, highlighting its potential to democratize access to AI
technology globally and to improve the equity and utility of AI in regions with
predominantly non-English languages. Our findings suggest that SUTRA not only
fills pivotal gaps in multilingual model capabilities but also establishes a
new benchmark for operational efficiency and scalability in AI applications.

通过引入 SUTRA，一种能够理解、推理和生成超过 50 种语言文本的多语言大型语言模型架构，本文展示了其在多语言任务上超越 GPT-3.5 和 Llama2 等现有模型 20-30% 的结果，并对其未来在多语言人工智能领域的广泛影响进行了探讨。

SUTRA：可扩展的多语言语言模型架构

SUTRA: Scalable Multilingual Language Model Architecture

Emotion classification in text is a challenging and subjective task, due to
the involved cognitive inference processes that are required to interpret a
textual stimulus. In addition, the set of emotion categories is highly
domain-specific. For instance, literature analysis might require the use of
aesthetic emotions (e.g., finding something beautiful), and social media
analysis could benefit from fine-grained sets (e.g., separating anger from
annoyance) in contrast to basic emotion categories. This renders the task an
interesting field for zero-shot classifications, in which the label set is not
known at model development time. Unfortunately, most resources for emotion
analysis are English, and therefore, most studies on emotion analysis have been
performed in English, including those that involve prompting language models
for text labels. This leaves us with a research gap that we address in this
paper: In which language should we prompt for emotion labels on non-English
texts? This is particularly of interest when we have access to a multilingual
large language model, because we could request labels with English prompts even
for non-English data. Our experiments with natural language inference-based
language models show that it is consistently better to use English prompts even
if the data is in a different language.

情感分类是一个具有挑战性和主观性的任务，需要解释文本刺激所需的认知推理过程。本文针对存在的研究空白，探讨了在非英语文本中使用哪种语言进行情感标签提示的问题，并通过自然语言推理基于语言模型的实验结果表明，即使数据来源是不同语言，使用英语提示更为有效。