# SLOG: A Structural Generalization Benchmark for Semantic Parsing

arXiv: https://arxiv.org/abs/2310.15040 · HTML: https://mlsanity.com/paper/2310.15040/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 53 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 26 of 26)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Compositionality decomposed: how do neural networks generalise?](https://mlsanity.com/paper/1908.08351/) (2019)
- [COGS: A Compositional Generalization Challenge Based on Semantic Interpretation](https://mlsanity.com/paper/2010.05465/) (2020)
- [Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/1808.09031/) (2018)
- [Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation](https://mlsanity.com/paper/1905.08205/) (2019)
- [The Devil is in the Detail: Simple Tricks Improve Systematic Generalization of Transformers](https://mlsanity.com/paper/2108.12284/) (2021)
- [What do RNN Language Models Learn about Filler-Gap Dependencies?](https://mlsanity.com/paper/1809.00042/) (2018)
- [Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations](https://mlsanity.com/paper/2104.07478/) (2021)
- [Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing](https://mlsanity.com/paper/2205.12253/) (2022)
- [Learning Algebraic Recombination for Compositional Generalization](https://mlsanity.com/paper/2107.06516/) (2021)
- [Lexicon Learning for Few-Shot Neural Sequence Modeling](https://mlsanity.com/paper/2106.03993/) (2021)
- [Uncontrolled Lexical Exposure Leads to Overestimation of Compositional Generalization in Pretrained Models](https://mlsanity.com/paper/2212.10769/) (2022)
- [Structural generalization is hard for sequence-to-sequence models](https://mlsanity.com/paper/2210.13050/) (2022)
- [ReCOGS: How Incidental Details of a Logical Form Overshadow an Evaluation of Semantic Interpretation](https://mlsanity.com/paper/2303.13716/) (2023)
- [Universal Semantic Parsing](https://mlsanity.com/paper/1702.03196/) (2017)
- [AMR Dependency Parsing with a Typed Semantic Algebra](https://mlsanity.com/paper/1805.11465/) (2018)
- [Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization](https://mlsanity.com/paper/2205.14521/) (2022)
- [Fast semantic parsing with well-typedness guarantees](https://mlsanity.com/paper/2009.07365/) (2020)
- [Can RNNs learn Recursive Nested Subject-Verb Agreements?](https://mlsanity.com/paper/2101.02258/) (2021)
- [Assessing the Capacity of Transformer to Abstract Syntactic Representations: A Contrastive Analysis Based on Long-distance Agreement](https://mlsanity.com/paper/2212.04523/) (2022)
- [Subregular Complexity and Deep Learning](https://mlsanity.com/paper/1705.05940/) (2017)
- [Learning compositional structures for semantic graph parsing](https://mlsanity.com/paper/2106.04398/) (2021)
- [Multi-Element Long Distance Dependencies: Using SPk Languages to Explore the Characteristics of Long-Distance Dependencies](https://mlsanity.com/paper/1907.06048/) (2019)

## Built on by (most cited first, 22 of 22)

- [Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases](https://mlsanity.com/paper/2502.19249/) (2025)
- [Linguistic Generalizations are not Rules: Impacts on Evaluation of LMs](https://mlsanity.com/paper/2502.13195/) (2025)
- [AMR Parsing is Far from Solved: GrAPES, the Granular AMR Parsing Evaluation Suite](https://mlsanity.com/paper/2312.03480/) (2023)
- [Neural Semantic Parsing with Extremely Rich Symbolic Meaning Representations](https://mlsanity.com/paper/2404.12698/) (2024)
- [Analyzing the Inner Workings of Transformers in Compositional Generalization](https://mlsanity.com/paper/2502.15277/) (2025)
- [Compositional Generalization Across Distributional Shifts with Sparse Tree Operations](https://mlsanity.com/paper/2412.14076/) (2024)
- [Behavioural vs. Representational Systematicity in End-to-End Models: An Opinionated Survey](https://mlsanity.com/paper/2506.04461/) (2025)
- [Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions](https://mlsanity.com/paper/2506.11111/) (2025)
- [Strengthening Structural Inductive Biases by Pre-training to Perform Syntactic Transformations](https://mlsanity.com/paper/2407.04543/) (2024)
- [Evaluating Structural Generalization in Neural Machine Translation](https://mlsanity.com/paper/2406.13363/) (2024)
- [Predicting generalization performance with correctness discriminators](https://mlsanity.com/paper/2311.09422/) (2023)
- [Representations as Language: An Information-Theoretic Framework for Interpretability](https://mlsanity.com/paper/2406.02449/) (2024)
- [ZOGRASCOPE: A New Benchmark for Semantic Parsing over Property Graphs](https://mlsanity.com/paper/2503.05268/) (2025)
- [A Theorem-Proving-Based Evaluation of Neural Semantic Parsing](https://mlsanity.com/paper/2510.11225/) (2025)
- [LIEDER: Linguistically-Informed Evaluation for Discourse Entity Recognition](https://mlsanity.com/paper/2403.06301/) (2024)
- [Exploring Compositional Generalization (in COGS/ReCOGS_pos) by Transformers using Restricted Access Sequence Processing (RASP)](https://mlsanity.com/paper/2504.15349/) (2025)
- [Structural Generalization on SLOG without Hand-Written Rules](https://mlsanity.com/paper/2604.26157/) (2026)
- [Recursive Binding on a Budget: Subspace Carving in Order-p Tensor Memories](https://mlsanity.com/paper/2606.11391/) (2026)
- [On the Role of Directionality in Structural Generalization](https://mlsanity.com/paper/2607.02307/) (2026)
- [On the Computational Complexity of Structural Generalization](https://mlsanity.com/paper/2607.19573/) (2026)
- [Compositional Generalization via Structural Identification in a Category-Theoretic Framework](https://mlsanity.com/paper/2608.26465/) (2026)
- [LLMs as Linguistic Chameleons: Decoupling Semantics and Structure for Privacy-Preserving Communication](https://mlsanity.com/paper/2609.23193/) (2026)

## Abstract (arXiv)

The goal of compositional generalization benchmarks is to evaluate how well models generalize to new complex linguistic expressions. Existing benchmarks often focus on lexical generalization, the interpretation of novel lexical items in syntactic structures familiar from training; structural generalization tasks, where a model needs to interpret syntactic structures that are themselves unfamiliar from training, are often underrepresented, resulting in overly optimistic perceptions of how well models can generalize. We introduce SLOG, a semantic parsing dataset that extends COGS (Kim and Linzen, 2020) with 17 structural generalization cases. In our experiments, the generalization accuracy of Transformer models, including pretrained ones, only reaches 40.6%, while a structure-aware parser only achieves 70.8%. These results are far from the near-perfect accuracy existing models achieve on COGS, demonstrating the role of SLOG in foregrounding the large discrepancy between models' lexical and structural generalization capacities.