# Improving the Natural Language Inference robustness to hard dataset by data augmentation and preprocessing

arXiv: https://arxiv.org/abs/2412.07108 · HTML: https://mlsanity.com/paper/2412.07108/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 12 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [Stress Test Evaluation for Natural Language Inference](https://mlsanity.com/paper/1806.00692/) (2018)
- [Shortcut-Stacked Sentence Encoders for Multi-Domain Inference](https://mlsanity.com/paper/1708.02312/) (2017)

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

- [HalluCounter: Reference-free LLM Hallucination Detection in the Wild!](https://mlsanity.com/paper/2503.04615/) (2025)

## Abstract (arXiv)

Natural Language Inference (NLI) is the task of inferring whether the hypothesis can be justified by the given premise. Basically, we classify the hypothesis into three labels(entailment, neutrality and contradiction) given the premise. NLI was well studied by the previous researchers. A number of models, especially the transformer based ones, have achieved significant improvement on these tasks. However, it is reported that these models are suffering when they are dealing with hard datasets. Particularly, they perform much worse when dealing with unseen out-of-distribution premise and hypothesis. They may not understand the semantic content but learn the spurious correlations. In this work, we propose the data augmentation and preprocessing methods to solve the word overlap, numerical reasoning and length mismatch problems. These methods are general methods that do not rely on the distribution of the testing data and they help improve the robustness of the models.