Communication of scientific findings to the public is important for keeping
non-experts informed of developments such as life-saving medical treatments.
However, generating readable lay summaries from scientific documents is
challenging, and currently, these summaries suffer from critical factual
errors. One popular intervention for improving factuality is using additional
external knowledge to provide factual grounding. However, it is unclear how
these grounding sources should be retrieved, selected, or integrated, and how
supplementary grounding documents might affect the readability or relevance of
the generated summaries. We develop a simple method for selecting grounding
sources and integrating them with source documents. We then use the BioLaySum
summarization dataset to evaluate the effects of different grounding sources on
summary quality. We found that grounding source documents improves the
relevance and readability of lay summaries but does not improve factuality of
lay summaries. This continues to be true in zero-shot summarization settings
where we hypothesized that grounding might be even more important for factual
lay summaries.

科学发现向公众传播非常重要，但从科学文件生成易读的摘要具有挑战性。本文提出了一种简单的方法来选择基础知识来源并将其与源文件集成。使用 BioLaySum 汇总数据集我们发现，基础文献的添加可以提高摘要的相关性和可读性，但并不能提高摘要的事实准确性。

生物医学简介的可读性、相关性和真实性背景知识基础

Background Knowledge Grounding for Readable, Relevant, and Factual  Biomedical Lay Summaries

Large-language models have recently demonstrated impressive zero-shot
capabilities in a variety of natural language tasks such as summarization,
dialogue generation, and question-answering. Despite many promising
applications in clinical medicine (e.g. medical record documentation, treatment
guideline-lookup), adoption of these models in real-world settings has been
largely limited by their tendency to generate factually incorrect and sometimes
even toxic statements. In this paper we explore the ability of large-language
models to facilitate and streamline medical guidelines and recommendation
referencing: by enabling these model to access external point-of-care tools in
response to physician queries, we demonstrate significantly improved factual
grounding, helpfulness, and safety in a variety of clinical scenarios.

本文研究了大型语言模型在临床医学领域的应用，探讨了通过让模型能够访问外部医疗工具，以响应医生查询的方式来实现医疗指南和建议推荐的能力，并展示了在各种临床场景中显著提高事实准确性、有效性和安全性的结果。

基于医疗临床知识的语言模型：Almanac

Almanac: Knowledge-Grounded Language Models for Clinical Medicine

We present LaMDA: Language Models for Dialog Applications. LaMDA is a family
of Transformer-based neural language models specialized for dialog, which have
up to 137B parameters and are pre-trained on 1.56T words of public dialog data
and web text. While model scaling alone can improve quality, it shows less
improvements on safety and factual grounding. We demonstrate that fine-tuning
with annotated data and enabling the model to consult external knowledge
sources can lead to significant improvements towards the two key challenges of
safety and factual grounding. The first challenge, safety, involves ensuring
that the model's responses are consistent with a set of human values, such as
preventing harmful suggestions and unfair bias. We quantify safety using a
metric based on an illustrative set of human values, and we find that filtering
candidate responses using a LaMDA classifier fine-tuned with a small amount of
crowdworker-annotated data offers a promising approach to improving model
safety. The second challenge, factual grounding, involves enabling the model to
consult external knowledge sources, such as an information retrieval system, a
language translator, and a calculator. We quantify factuality using a
groundedness metric, and we find that our approach enables the model to
generate responses grounded in known sources, rather than responses that merely
sound plausible. Finally, we explore the use of LaMDA in the domains of
education and content recommendations, and analyze their helpfulness and role
consistency.

LaMDA 是一种专门用于对话的基于 Transformer 的神经语言模型，其具有高达 137B 的参数，并使用公共对话数据和 Web 文本预训练。它具有先进的优化技术，能够解决安全性和事实基础等关键挑战，以及能够在教育和内容推荐领域发挥积极作用。