# Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and Approaches

arXiv: https://arxiv.org/abs/1904.01172 · HTML: https://mlsanity.com/paper/1904.01172/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Apr 2019.

## Built on by (most cited first, 30 of 65)

- [Stretching Sentence-pair NLI Models to Reason over Long Documents and Clusters](https://mlsanity.com/paper/2204.07447/) (2022)
- [Training on the Test Task Confounds Evaluation and Emergence](https://mlsanity.com/paper/2407.07890/) (2024)
- [Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges](https://mlsanity.com/paper/2502.12378/) (2025)
- [Inherent Trade-Offs between Diversity and Stability in Multi-Task Benchmarks](https://mlsanity.com/paper/2405.01719/) (2024)
- [It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance](https://mlsanity.com/paper/2305.09022/) (2023)
- [The Defeat of the Winograd Schema Challenge](https://mlsanity.com/paper/2201.02387/) (2022)
- [Reasoning over Vision and Language: Exploring the Benefits of Supplemental Knowledge](https://mlsanity.com/paper/2101.06013/) (2021)
- [Inductive reasoning in humans and large language models](https://mlsanity.com/paper/2306.06548/) (2023)
- [Automated scholarly paper review: Concepts, technologies, and challenges](https://mlsanity.com/paper/2111.07533/) (2021)
- [Is BERT Blind? Exploring the Effect of Vision-and-Language Pretraining on Visual Language Understanding](https://mlsanity.com/paper/2303.12513/) (2023)
- [Assemble Foundation Models for Automatic Code Summarization](https://mlsanity.com/paper/2201.05222/) (2022)
- [Reasoning Capacity in Multi-Agent Systems: Limitations, Challenges and Human-Centered Solutions](https://mlsanity.com/paper/2402.01108/) (2024)
- [Gemini in Reasoning: Unveiling Commonsense in Multimodal Large Language Models](https://mlsanity.com/paper/2312.17661/) (2023)
- [Benchmarking for Biomedical Natural Language Processing Tasks with a Domain Specific ALBERT](https://mlsanity.com/paper/2107.04374/) (2021)
- [A new hope for network model generalization](https://mlsanity.com/paper/2207.05843/) (2022)
- [Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanations](https://mlsanity.com/paper/2305.14618/) (2023)
- [Resolving Word Vagueness with Scenario-guided Adapter for Natural Language Inference](https://mlsanity.com/paper/2405.12434/) (2024)
- [Towards Large-Scale Interpretable Knowledge Graph Reasoning for Dialogue Systems](https://mlsanity.com/paper/2203.10610/) (2022)
- [Exploring the Limitations of Large Language Models in Compositional Relation Reasoning](https://mlsanity.com/paper/2403.02615/) (2024)
- [Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning](https://mlsanity.com/paper/2205.00731/) (2022)
- [CRoW: Benchmarking Commonsense Reasoning in Real-World Tasks](https://mlsanity.com/paper/2310.15239/) (2023)
- [SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning](https://mlsanity.com/paper/2104.08793/) (2021)
- [The Web Can Be Your Oyster for Improving Large Language Models](https://mlsanity.com/paper/2305.10998/) (2023)
- [Conditions for Length Generalization in Learning Reasoning Skills](https://mlsanity.com/paper/2311.16173/) (2023)
- [From Data to Commonsense Reasoning: The Use of Large Language Models for Explainable AI](https://mlsanity.com/paper/2407.03778/) (2024)
- [KMIR: A Benchmark for Evaluating Knowledge Memorization, Identification and Reasoning Abilities of Language Models](https://mlsanity.com/paper/2202.13529/) (2022)
- [Continual Pre-training of MoEs: How robust is your router?](https://mlsanity.com/paper/2503.05029/) (2025)
- [Context-Aware Interaction Network for Question Matching](https://mlsanity.com/paper/2104.08451/) (2021)
- [Natural Language Processing with Commonsense Knowledge: A Survey](https://mlsanity.com/paper/2108.04674/) (2021)
- [SDOH-NLI: a Dataset for Inferring Social Determinants of Health from Clinical Notes](https://mlsanity.com/paper/2310.18431/) (2023)

## Abstract (arXiv)

In the NLP community, recent years have seen a surge of research activities that address machines' ability to perform deep language understanding which goes beyond what is explicitly stated in text, rather relying on reasoning and knowledge of the world. Many benchmark tasks and datasets have been created to support the development and evaluation of such natural language inference ability. As these benchmarks become instrumental and a driving force for the NLP research community, this paper aims to provide an overview of recent benchmarks, relevant knowledge resources, and state-of-the-art learning and inference approaches in order to support a better understanding of this growing field.