We evaluate the ability of large language models (LLMs) to infer causal
relations from natural language. Compared to traditional natural language
processing and deep learning techniques, LLMs show competitive performance in a
benchmark of pairwise relations without needing (explicit) training samples.
This motivates us to extend our approach to extrapolating causal graphs through
iterated pairwise queries. We perform a preliminary analysis on a benchmark of
biomedical abstracts with ground-truth causal graphs validated by experts. The
results are promising and support the adoption of LLMs for such a crucial step
in causal inference, especially in medical domains, where the amount of
scientific text to analyse might be huge, and the causal statements are often
implicit.

通过大型语言模型 (LLMs) 从自然语言中推断因果关系，并借助成对查询对因果图进行扩展，初始分析在生物医学文摘的基准上进行，结果有希望且支持在因果推断中采用 LLMs，尤其是在科学文本庞大且因果陈述经常隐含的医学领域。

文本的零 - shot 因果图外推通过 LLMs

Zero-shot Causal Graph Extrapolation from Text via LLMs

Though exponentially growing health-related literature has been made
available to a broad audience online, the language of scientific articles can
be difficult for the general public to understand. Therefore, adapting this
expert-level language into plain language versions is necessary for the public
to reliably comprehend the vast health-related literature. Deep Learning
algorithms for automatic adaptation are a possible solution; however, gold
standard datasets are needed for proper evaluation. Proposed datasets thus far
consist of either pairs of comparable professional- and general public-facing
documents or pairs of semantically similar sentences mined from such documents.
This leads to a trade-off between imperfect alignments and small test sets. To
address this issue, we created the Plain Language Adaptation of Biomedical
Abstracts dataset. This dataset is the first manually adapted dataset that is
both document- and sentence-aligned. The dataset contains 750 adapted
abstracts, totaling 7643 sentence pairs. Along with describing the dataset, we
benchmark automatic adaptation on the dataset with state-of-the-art Deep
Learning approaches, setting baselines for future research.

为了让公众可靠理解庞大的医学文献，本研究提出了通过深度学习算法自动适应平民语言版本的解决方案，同时为了保证适应的准确性，首先需要建立一个金标准数据集，而为了解决目前现有的数据集相互不对齐及小试验集的问题，本研究建立了第一个均已手动适配的数据集，并使用最新的深度学习方法进行了基准测试。

医学摘要普通语言适应数据集

A Dataset for Plain Language Adaptation of Biomedical Abstracts

We propose a model for tagging unstructured texts with an arbitrary number of
terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat
this as a special case of sequence-to-sequence learning in which the decoder
begins at the root node of an ontological tree and recursively elects to expand
child nodes as a function of the input text, the current node, and the latent
decoder state. In our experiments the proposed method outperforms
state-of-the-art approaches on the important task of automatically assigning
MeSH terms to biomedical abstracts.

本文提出了一种基于序列到序列学习的模型，用于将非结构化文本与本体树中的任意数量术语进行标记，并在自动分配 MeSH 术语给生物医学摘要的重要任务上，优于现有方法。