# Looking Beyond Sentence-Level Natural Language Inference for Downstream Tasks

arXiv: https://arxiv.org/abs/2009.09099 · HTML: https://mlsanity.com/paper/2009.09099/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2020.

## Built on by (most cited first, 6 of 6)

- [The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey](https://mlsanity.com/paper/2104.14839/) (2021)
- [Can NLI Models Verify QA Systems' Predictions?](https://mlsanity.com/paper/2104.08731/) (2021)
- [Factual Consistency Evaluation for Text Summarization via Counterfactual Estimation](https://mlsanity.com/paper/2108.13134/) (2021)
- [Don't Say What You Don't Know: Improving the Consistency of Abstractive Summarization by Constraining Beam Search](https://mlsanity.com/paper/2203.08436/) (2022)
- [Investigating Crowdsourcing Protocols for Evaluating the Factual Consistency of Summaries](https://mlsanity.com/paper/2109.09195/) (2021)
- [Valuable Hallucinations: Realizable Non-realistic Propositions](https://mlsanity.com/paper/2502.11113/) (2025)

## Abstract (arXiv)

In recent years, the Natural Language Inference (NLI) task has garnered significant attention, with new datasets and models achieving near human-level performance on it. However, the full promise of NLI -- particularly that it learns knowledge that should be generalizable to other downstream NLP tasks -- has not been realized. In this paper, we study this unfulfilled promise from the lens of two downstream tasks: question answering (QA), and text summarization. We conjecture that a key difference between the NLI datasets and these downstream tasks concerns the length of the premise; and that creating new long premise NLI datasets out of existing QA datasets is a promising avenue for training a truly generalizable NLI model. We validate our conjecture by showing competitive results on the task of QA and obtaining the best reported results on the task of Checking Factual Correctness of Summaries.