Large Language Models (LLMs) combined with instruction tuning have made
significant progress when generalizing to unseen tasks. However, they have been
less successful in Information Extraction (IE), lagging behind task-specific
models. Typically, IE tasks are characterized by complex annotation guidelines
which describe the task and give examples to humans. Previous attempts to
leverage such information have failed, even with the largest models, as they
are not able to follow the guidelines out-of-the-box. In this paper we propose
GoLLIE (Guideline-following Large Language Model for IE), a model able to
improve zero-shot results on unseen IE tasks by virtue of being fine-tuned to
comply with annotation guidelines. Comprehensive evaluation empirically
demonstrates that GoLLIE is able to generalize to and follow unseen guidelines,
outperforming previous attempts at zero-shot information extraction. The
ablation study shows that detailed guidelines is key for good results.

GoLLIE 模型是一种基于大型语言模型的信息抽取模型，通过遵循详细的注释指南，能够在未见过的任务上获得零样本结果，并在综合评估中表现优于之前的尝试。

GoLLIE：注释指南提升零样本信息提取

GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction

Generative modeling has been the dominant approach for large-scale
pretraining and zero-shot generalization. In this work, we challenge this
convention by showing that discriminative approaches perform substantially
better than generative ones on a large number of NLP tasks. Technically, we
train a single discriminator to predict whether a text sample comes from the
true data distribution, similar to GANs. Since many NLP tasks can be formulated
as selecting from a few options, we use this discriminator to predict the
option with the highest probability. This simple formulation achieves
state-of-the-art zero-shot results on the T0 benchmark, outperforming T0 by
16.0\%, 7.8\%, and 11.5\% respectively on different scales. In the finetuning
setting, our approach also achieves new state-of-the-art results on a wide
range of NLP tasks, with only 1/4 parameters of previous methods. Meanwhile,
our approach requires minimal prompting efforts, which largely improves
robustness and is essential for real-world applications. Furthermore, we also
jointly train a generalized UD in combination with generative tasks, which
maintains its advantage on discriminative tasks and simultaneously works on
generative tasks.

本文挑战传统的生成建模方法，提出较好的判别式方法，实验结果表明，该方法在自然语言处理任务上具有更好的性能，无需复杂的提示即可获得最新的状态 - 良好的零 - shot 结果

用于零样本泛化的通用判别器

A Universal Discriminator for Zero-Shot Generalization

Multitask prompted finetuning (MTF) has been shown to help large language
models generalize to new tasks in a zero-shot setting, but so far explorations
of MTF have focused on English data and models. We apply MTF to the pretrained
multilingual BLOOM and mT5 model families to produce finetuned variants called
BLOOMZ and mT0. We find finetuning large multilingual language models on
English tasks with English prompts allows for task generalization to
non-English languages that appear only in the pretraining corpus. Finetuning on
multilingual tasks with English prompts further improves performance on English
and non-English tasks leading to various state-of-the-art zero-shot results. We
also investigate finetuning on multilingual tasks with prompts that have been
machine-translated from English to match the language of each dataset. We find
training on these machine-translated prompts leads to better performance on
human-written prompts in the respective languages. Surprisingly, we find models
are capable of zero-shot generalization to tasks in languages they have never
intentionally seen. We conjecture that the models are learning higher-level
capabilities that are both task- and language-agnostic. In addition, we
introduce xP3, a composite of supervised datasets in 46 languages with English
and machine-translated prompts. Our code, datasets and models are publicly
available at this https URL.

研究发现多任务 finetuning 可以帮助大型多语言模型成功推广至非英语任务中，并且使用机器翻译英文为前缀可以获得更好的性能，最终实现零 - shot 的结果。