In this paper, we study the effects of incorporating timestamps, such as
document creation dates, into generation systems. Two types of time-aware
prompts are investigated: (1) textual prompts that encode document timestamps
in natural language sentences; and (2) linear prompts that convert timestamps
into continuous vectors. To explore extrapolation to future data points, we
further introduce a new data-to-text generation dataset, TempWikiBio,
containing more than 4 millions of chronologically ordered revisions of
biographical articles from English Wikipedia, each paired with structured
personal profiles. Through data-to-text generation on TempWikiBio, text-to-text
generation on the content transfer dataset, and summarization on XSum, we show
that linear prompts on encoder and textual prompts improve the generation
quality on all datasets. Despite having less performance drop when testing on
data drawn from a later time, linear prompts focus more on non-temporal
information and are less sensitive to the given timestamps, according to human
evaluations and sensitivity analyses. Meanwhile, textual prompts establish the
association between the given timestamps and the output dates, yielding more
factual temporal information in the output.

探讨利用时间戳，如文档创建日期，将时间信息纳入到生成系统中的影响，研究时间感知提示的两种类型，并引入一个新的数据生成数据集 TempWikiBio，通过数据生成、文本传输和摘要三个数据集展示了使用线性提示和文本提示可以提高所有数据集的生成质量，线性提示对非时间信息更加关注，对时间敏感度较低，而文本提示可以产生更符合事实的时间信息。

文本生成的时间感知提示

Time-aware Prompting for Text Generation

For evaluating generation systems, automatic metrics such as BLEU cost
nothing to run but have been shown to correlate poorly with human judgment,
leading to systematic bias against certain model improvements. On the other
hand, averaging human judgments, the unbiased gold standard, is often too
expensive. In this paper, we use control variates to combine automatic metrics
with human evaluation to obtain an unbiased estimator with lower cost than
human evaluation alone. In practice, however, we obtain only a 7-13% cost
reduction on evaluating summarization and open-response question answering
systems. We then prove that our estimator is optimal: there is no unbiased
estimator with lower cost. Our theory further highlights the two fundamental
bottlenecks---the automatic metric and the prompt shown to human
evaluators---both of which need to be improved to obtain greater cost savings.

本文提出使用控制变量方法，结合自动评价指标与人工评价来获取代价较低的无偏估计，在对文摘和开放式问题回答进行评估时，可以实现 7-13% 的代价降低，同时强调了自动评价指标和提示方式是进一步降低代价的关键瓶颈。