Expert demonstrations have proven an easy way to indirectly specify complex
tasks. Recent algorithms even support extracting unambiguous formal
specifications, e.g. deterministic finite automata (DFA), from demonstrations.
Unfortunately, these techniques are generally not sample efficient. In this
work, we introduce $L^*LM$, an algorithm for learning DFAs from both
demonstrations and natural language. Due to the expressivity of natural
language, we observe a significant improvement in the data efficiency of
learning DFAs from expert demonstrations. Technically, $L^*LM$ leverages large
language models to answer membership queries about the underlying task. This is
then combined with recent techniques for transforming learning from
demonstrations into a sequence of labeled example learning problems. In our
experiments, we observe the two modalities complement each other, yielding a
powerful few-shot learner.

该研究介绍了一种从专家演示和自然语言中学习确定性有限自动机（DFA）的算法，利用自然语言的表达能力显著提高了从专家演示中学习 DFAs 的数据效率，通过结合大型语言模型和转化学习算法，实现了强大的少样本学习器。

$L^*LM$: 从示例中使用自然语言预测的学习自动机

$L^*LM$: Learning Automata from Examples using Natural Language Oracles

Medicine, by its nature, is a multifaceted domain that requires the synthesis
of information across various modalities. Medical generative vision-language
models (VLMs) make a first step in this direction and promise many exciting
clinical applications. However, existing models typically have to be fine-tuned
on sizeable down-stream datasets, which poses a significant limitation as in
many medical applications data is scarce, necessitating models that are capable
of learning from few examples in real-time. Here we propose Med-Flamingo, a
multimodal few-shot learner adapted to the medical domain. Based on
OpenFlamingo-9B, we continue pre-training on paired and interleaved medical
image-text data from publications and textbooks. Med-Flamingo unlocks few-shot
generative medical visual question answering (VQA) abilities, which we evaluate
on several datasets including a novel challenging open-ended VQA dataset of
visual USMLE-style problems. Furthermore, we conduct the first human evaluation
for generative medical VQA where physicians review the problems and blinded
generations in an interactive app. Med-Flamingo improves performance in
generative medical VQA by up to 20\% in clinician's rating and firstly enables
multimodal medical few-shot adaptations, such as rationale generation. We
release our model, code, and evaluation app under
this https URL

Med-Flamingo 是一种适应于医学领域的多模态少样本学习器，能够通过从出版物和教材中的医学图像 - 文本数据进行预训练，提高生成式医学视觉问答（VQA）的性能，并在医师评估和干预性应用中首次实现了多模态医学少样本适应。

Med-Flamingo: 一种多模态医学少样本学习器

Med-Flamingo: a Multimodal Medical Few-shot Learner

Despite the success of text-to-text pre-trained models in various natural
language generation (NLG) tasks, the generation performance is largely
restricted by the number of labeled data in downstream tasks, particularly in
data-to-text generation tasks. Existing works mostly utilize abundant unlabeled
structured data to conduct unsupervised pre-training for task adaption, which
fail to model the complex relationship between source structured data and
target texts. Thus, we introduce self-training as a better few-shot learner
than task-adaptive pre-training, which explicitly captures this relationship
via pseudo-labeled data generated by the pre-trained model. To alleviate the
side-effect of low-quality pseudo-labeled data during self-training, we propose
a novel method called Curriculum-Based Self-Training (CBST) to effectively
leverage unlabeled data in a rearranged order determined by the difficulty of
text generation. Experimental results show that our method can outperform
fine-tuning and task-adaptive pre-training methods, and achieve
state-of-the-art performance in the few-shot setting of data-to-text
generation.

本研究提出一种基于自学习的数据到文本生成方法，通过前置学习模型生成的伪标签数据，显式地捕捉数据和文本之间的复杂关系，以及一种称为基于课程设置的自学习 (CBST) 的新方法来缓解伪标签数据的质量问题，在少量标记的数据集下显著优于基于现有技术的元认知预训练和微调方法，本方法成为数据到文本生成领域中的最佳方法。