Question-Options Generation (QOG) is a task that involves generating a set of
question-options pairs given context. This task has various applications,
including fine-tuning large models, information retrieval, and automated
multiple-choice question generation for education. In this paper, we develop
QOG models using three different methods based on fine-tuning
sequence-to-sequence language models (LMs). Experiments demonstrate that the
end-to-end QOG model is computationally efficient and stable during both
training and inference, outperforming other methods. Furthermore, our analysis
indicates that our QOG models are competitive on the QOG task compared to the
large language model Llama 3-8B.

基于序列到序列语言模型的三种不同方法，本文开发了问题选项生成（QOG）模型，实验证明这种端到端 QOG 模型在训练和推断过程中具有计算效率高且稳定的特点，优于其他方法，与大型语言模型 Llama 3-8B 相比，我们的分析表明，我们的 QOG 模型在 QOG 任务上具有竞争力。

基于语言模型的问题与选项生成

QOG:Question and Options Generation based on Language Model

Sequence-to-sequence language models can be used to produce abstractive
summaries which are coherent, relevant, and concise. Still, model sizes can
make deployment in latency-sensitive or web-scale implementations difficult.
This paper studies the relationship between model size, structured pruning,
inference efficiency, and summarization accuracy on widely used summarization
datasets. We show that model accuracy is tied to the encoder size while
inference efficiency is connected to the decoder. Using asymmetric pruning can
lead to nearly 3x improvement in inference latency with ~1 point loss in
Rouge-2. Moreover, we find both the average degradation and the role of
asymmetry to be consistent across model sizes and variations in datasets.

该研究研究了模型大小、结构修剪、推理效率和摘要准确性之间的关系，发现准确性与编码器大小相关，而推理效率与解码器相关，使用非对称修剪可以在保持相同摘要准确性的情况下将推理延迟减少近 3 倍。

序列到序列模型的结构剪枝：提高推理效率的非对称方法

To Asymmetry and Beyond: Structured Pruning of Sequence to Sequence  Models for Improved Inference Efficiency

We present the Verifee Dataset: a novel dataset of news articles with
fine-grained trustworthiness annotations. We develop a detailed methodology
that assesses the texts based on their parameters encompassing editorial
transparency, journalist conventions, and objective reporting while penalizing
manipulative techniques. We bring aboard a diverse set of researchers from
social, media, and computer sciences to overcome barriers and limited framing
of this interdisciplinary problem. We collect over $10,000$ unique articles
from almost $60$ Czech online news sources. These are categorized into one of
the $4$ classes across the credibility spectrum we propose, raging from
entirely trustworthy articles all the way to the manipulative ones. We produce
detailed statistics and study trends emerging throughout the set. Lastly, we
fine-tune multiple popular sequence-to-sequence language models using our
dataset on the trustworthiness classification task and report the best testing
F-1 score of $0.52$. We open-source the dataset, annotation methodology, and
annotators' instructions in full length at this https URL to
enable easy build-up work. We believe similar methods can help prevent
disinformation and educate in the realm of media literacy.

介绍 Verifee 数据集，其中分类了四种不同的新闻可信度等级，通过调整多种流行序列到序列语言模型，在信任度分类任务中达到了最佳的 F-1 得分 0.52，并通过开源数据集和注释方法有助于防止虚假报道和提高公众媒体素养。