Distorted science communication harms individuals and society as it can lead
to unhealthy behavior change and decrease trust in scientific institutions.
Given the rapidly increasing volume of science communication in recent years, a
fine-grained understanding of how findings from scientific publications are
reported to the general public, and methods to detect distortions from the
original work automatically, are crucial. Prior work focused on individual
aspects of distortions or worked with unpaired data. In this work, we make
three foundational contributions towards addressing this problem: (1)
annotating 1,600 instances of scientific findings from academic papers paired
with corresponding findings as reported in news articles and tweets wrt. four
characteristics: causality, certainty, generality and sensationalism; (2)
establishing baselines for automatically detecting these characteristics; and
(3) analyzing the prevalence of changes in these characteristics in both
human-annotated and large-scale unlabeled data. Our results show that
scientific findings frequently undergo subtle distortions when reported. Tweets
distort findings more often than science news reports. Detecting fine-grained
distortions automatically poses a challenging task. In our experiments,
fine-tuned task-specific models consistently outperform few-shot LLM prompting.

失真的科学传播对个人和社会造成危害，可能导致不健康的行为转变并减少对科学机构的信任。本研究通过对科学文献与新闻报道和推特中相应发现的配对进行细粒度的研究，为自动检测原始工作失真的方法提供了基础贡献，并分析了这些特征的变化在人工注释和大规模无标记数据中的普遍性。结果显示科学发现在报道时经常会发生微妙的失真，其中推特比科学新闻报道更容易扭曲发现。自动检测细粒度失真是一项具有挑战性的任务，实验结果表明，经过精细调整的任务特定模型始终优于少量样本的语言模型提示。

理解科学发现报告中的细粒度畸变

Understanding Fine-grained Distortions in Reports of Scientific Findings

Artificial intelligence (AI) models trained on published scientific findings
have been used to invent valuable materials and targeted therapies, but they
typically ignore the human scientists who continually alter the landscape of
discovery. Here we show that incorporating the distribution of human expertise
by training unsupervised models on simulated inferences cognitively accessible
to experts dramatically improves (up to 400%) AI prediction of future
discoveries beyond those focused on research content alone, especially when
relevant literature is sparse. These models succeed by predicting human
predictions and the scientists who will make them. By tuning human-aware AI to
avoid the crowd, we can generate scientifically promising "alien" hypotheses
unlikely to be imagined or pursued without intervention until the distant
future, which hold promise to punctuate scientific advance beyond questions
currently pursued. Accelerating human discovery or probing its blind spots,
human-aware AI enables us to move toward and beyond the contemporary scientific
frontier.

该研究表明，将人类专业知识的分布纳入考虑，并以此训练无监督的人工智能模型，能够显著提高 AI 对未来发现的预测能力，特别是在相关文献稀缺的情况下，这些模型成功预测出人类预测和可能做出预测的科学家，为科学进步开创新的前沿。