# Probing Linguistic Systematicity

arXiv: https://arxiv.org/abs/2005.04315 · HTML: https://mlsanity.com/paper/2005.04315/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 30 of 37)

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- [Compositional Evaluation on Japanese Textual Entailment and Similarity](https://mlsanity.com/paper/2208.04826/) (2022)
- [Do PLMs Know and Understand Ontological Knowledge?](https://mlsanity.com/paper/2309.05936/) (2023)
- [Neuro-symbolic Natural Logic with Introspective Revision for Natural Language Inference](https://mlsanity.com/paper/2203.04857/) (2022)
- [Can Transformers Reason in Fragments of Natural Language?](https://mlsanity.com/paper/2211.05417/) (2022)
- [SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics](https://mlsanity.com/paper/2106.01077/) (2021)
- [Simple Linguistic Inferences of Large Language Models (LLMs): Blind Spots and Blinds](https://mlsanity.com/paper/2305.14785/) (2023)
- [Supporting Context Monotonicity Abstractions in Neural NLI Models](https://mlsanity.com/paper/2105.08008/) (2021)
- [A Latent-Variable Model for Intrinsic Probing](https://mlsanity.com/paper/2201.08214/) (2022)
- [Systematicity, Compositionality and Transitivity of Deep NLP Models: a Metamorphic Testing Perspective](https://mlsanity.com/paper/2204.12316/) (2022)
- [Investigating Transfer Learning in Multilingual Pre-trained Language Models through Chinese Natural Language Inference](https://mlsanity.com/paper/2106.03983/) (2021)
- [Decomposing Natural Logic Inferences in Neural NLI](https://mlsanity.com/paper/2112.08289/) (2021)
- [Understanding Natural Language Understanding Systems. A Critical Analysis](https://mlsanity.com/paper/2303.04229/) (2023)
- [SETI: Systematicity Evaluation of Textual Inference](https://mlsanity.com/paper/2305.15045/) (2023)
- [Exploring Continual Learning of Compositional Generalization in NLI](https://mlsanity.com/paper/2403.04400/) (2024)
- [Scaling Synthetic Logical Reasoning Datasets with Context-Sensitive Declarative Grammars](https://mlsanity.com/paper/2406.11035/) (2024)
- [Situation and Behavior Understanding by Trope Detection on Films](https://mlsanity.com/paper/2101.07632/) (2021)
- [Do Transformers know symbolic rules, and would we know if they did?](https://mlsanity.com/paper/2203.00162/) (2022)
- [Dynamic MOdularized Reasoning for Compositional Structured Explanation Generation](https://mlsanity.com/paper/2309.07624/) (2023)

## Abstract (arXiv)

Recently, there has been much interest in the question of whether deep natural language understanding models exhibit systematicity; generalizing such that units like words make consistent contributions to the meaning of the sentences in which they appear. There is accumulating evidence that neural models often generalize non-systematically. We examined the notion of systematicity from a linguistic perspective, defining a set of probes and a set of metrics to measure systematic behaviour. We also identified ways in which network architectures can generalize non-systematically, and discuss why such forms of generalization may be unsatisfying. As a case study, we performed a series of experiments in the setting of natural language inference (NLI), demonstrating that some NLU systems achieve high overall performance despite being non-systematic.