# NewsEdits 2.0: Learning the Intentions Behind Updating News

arXiv: https://arxiv.org/abs/2411.18811 · HTML: https://mlsanity.com/paper/2411.18811/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Builds on (most cited first, 11 of 11)

- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [TinyBERT: Distilling BERT for Natural Language Understanding](https://mlsanity.com/paper/1909.10351/) (2019)
- [RealTime QA: What's the Answer Right Now?](https://mlsanity.com/paper/2207.13332/) (2022)
- [StreamingQA: A Benchmark for Adaptation to New Knowledge over Time in Question Answering Models](https://mlsanity.com/paper/2205.11388/) (2022)
- [Large Language Models Can Learn Temporal Reasoning](https://mlsanity.com/paper/2401.06853/) (2024)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning](https://mlsanity.com/paper/2012.15283/) (2020)
- [Towards Benchmarking and Improving the Temporal Reasoning Capability of Large Language Models](https://mlsanity.com/paper/2306.08952/) (2023)
- [WikiAtomicEdits: A Multilingual Corpus of Wikipedia Edits for Modeling Language and Discourse](https://mlsanity.com/paper/1808.09422/) (2018)
- [Identifying Informational Sources in News Articles](https://mlsanity.com/paper/2305.14904/) (2023)

## Built on by (most cited first, 1 of 1)

- [GVD: Governed Versioning and Deduplication for Document Repositories](https://mlsanity.com/paper/2609.17696/) (2026)

## Abstract (arXiv)

As events progress, news articles often update with new information: if we are not cautious, we risk propagating outdated facts. In this work, we hypothesize that linguistic features indicate factual fluidity, and that we can predict which facts in a news article will update using solely the text of a news article (i.e. not external resources like search engines). We test this hypothesis, first, by isolating fact-updates in large news revisions corpora. News articles may update for many reasons (e.g. factual, stylistic, narrative). We introduce the NewsEdits 2.0 taxonomy, an edit-intentions schema that separates fact updates from stylistic and narrative updates in news writing. We annotate over 9,200 pairs of sentence revisions and train high-scoring ensemble models to apply this schema. Then, taking a large dataset of silver-labeled pairs, we show that we can predict when facts will update in older article drafts with high precision. Finally, to demonstrate the usefulness of these findings, we construct a language model question asking (LLM-QA) abstention task. We wish the LLM to abstain from answering questions when information is likely to become outdated. Using our predictions, we show, LLM absention reaches near oracle levels of accuracy.