While large language models are able to retain vast amounts of world
knowledge seen during pretraining, such knowledge is prone to going out of date
and is nontrivial to update. Furthermore, these models are often used under
temporal misalignment, tasked with answering questions about the present,
despite having only been trained on data collected in the past. To mitigate the
effects of temporal misalignment, we propose fact duration prediction: the task
of predicting how long a given fact will remain true. In our experiments, we
demonstrate how identifying facts that are prone to rapid change can help
models avoid from reciting outdated information and identify which predictions
require seeking out up-to-date knowledge sources. We also show how modeling
fact duration improves calibration for knowledge-intensive tasks, such as
open-retrieval question answering, under temporal misalignment by discarding
volatile facts. Our data and code will be released publicly at
this https URL

本文提出了事实持续时间预测来缓解时间错位带来的影响，在实验中演示了确定容易变化的事实，可以帮助模型避免重复过时信息，同时在知识密集型任务下建模事实持续时间有助于提高校准性。

通过丢弃过时事实来缓解时间不对齐问题

Mitigating Temporal Misalignment by Discarding Outdated Facts

We present the results of the Workshop on Multilingual Information Access
(MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question
answering (QA) systems in 16 typologically diverse languages. In this task, we
adapted two large-scale cross-lingual open-retrieval QA datasets in 14
typologically diverse languages, and newly annotated open-retrieval QA data in
2 underrepresented languages: Tagalog and Tamil. Four teams submitted their
systems. The best system leveraging iteratively mined diverse negative examples
and larger pretrained models achieves 32.2 F1, outperforming our baseline by
4.5 points. The second best system uses entity-aware contextualized
representations for document retrieval, and achieves significant improvements
in Tamil (20.8 F1), whereas most of the other systems yield nearly zero scores.

本文介绍了 MIA 2022 workshop 关于跨语言信息检索中的开放式提取问题回答（QA）系统评估的结果，并评估了 16 种语言中的系统性能表现。通过对 14 种语言的大规模跨语言开放式提取 QA 数据集进行改编以及对 Tagalog 和 Tamil 两种语言进行新的标记，共有 4 支队伍提交了他们的系统，最佳系统借助多样化的负样本和更大的预训练模型实现了 32.2 的 F1 值，而第二名则在 Tamil 语言的检索中取得了显著的进展。