# Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models

arXiv: https://arxiv.org/abs/2105.03495 · HTML: https://mlsanity.com/paper/2105.03495/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 38 references that are in the map.
- First posted on arXiv 7 May 2021.

## Builds on (most cited first, 17 of 17)

- [Neural Network Acceptability Judgments](https://mlsanity.com/paper/1805.12471/) (2018)
- [Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference](https://mlsanity.com/paper/1902.01007/) (2019)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [BLiMP: The Benchmark of Linguistic Minimal Pairs for English](https://mlsanity.com/paper/1912.00582/) (2019)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [Targeted Syntactic Evaluation of Language Models](https://mlsanity.com/paper/1808.09031/) (2018)
- [A Simple Method for Commonsense Reasoning](https://mlsanity.com/paper/1806.02847/) (2018)
- [A Systematic Assessment of Syntactic Generalization in Neural Language Models](https://mlsanity.com/paper/2005.03692/) (2020)
- [Unsupervised Evaluation of Interactive Dialog with DialoGPT](https://mlsanity.com/paper/2006.12719/) (2020)
- [Dialogue Natural Language Inference](https://mlsanity.com/paper/1811.00671/) (2018)
- [Do Massively Pretrained Language Models Make Better Storytellers?](https://mlsanity.com/paper/1909.10705/) (2019)
- [Pre-training Is (Almost) All You Need: An Application to Commonsense Reasoning](https://mlsanity.com/paper/2004.14074/) (2020)
- [Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations](https://mlsanity.com/paper/1909.00142/) (2019)
- [Discourse Coherence in the Wild: A Dataset, Evaluation and Methods](https://mlsanity.com/paper/1805.04993/) (2018)
- [A Cross-Domain Transferable Neural Coherence Model](https://mlsanity.com/paper/1905.11912/) (2019)
- [A Unified Neural Coherence Model](https://mlsanity.com/paper/1909.00349/) (2019)

## Built on by (most cited first, 15 of 15)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Language Model Behavior: A Comprehensive Survey](https://mlsanity.com/paper/2303.11504/) (2023)
- [A fine-grained comparison of pragmatic language understanding in humans and language models](https://mlsanity.com/paper/2212.06801/) (2022)
- [Prompting is not a substitute for probability measurements in large language models](https://mlsanity.com/paper/2305.13264/) (2023)
- [DiscoScore: Evaluating Text Generation with BERT and Discourse Coherence](https://mlsanity.com/paper/2201.11176/) (2022)
- [On the Limits of Minimal Pairs in Contrastive Evaluation](https://mlsanity.com/paper/2109.07465/) (2021)
- [XCOMPS: A Multilingual Benchmark of Conceptual Minimal Pairs](https://mlsanity.com/paper/2502.19737/) (2025)
- [Unlocking Structure Measuring: Introducing PDD, an Automatic Metric for Positional Discourse Coherence](https://mlsanity.com/paper/2402.10175/) (2024)
- [Large Language Models as Neurolinguistic Subjects: Discrepancy between Performance and Competence](https://mlsanity.com/paper/2411.07533/) (2024)
- [Is It JUST Semantics? A Case Study of Discourse Particle Understanding in LLMs](https://mlsanity.com/paper/2506.04534/) (2025)
- [Joint Modeling of Entities and Discourse Relations for Coherence Assessment](https://mlsanity.com/paper/2509.04182/) (2025)
- [WUGNECTIVES: Novel Entity Inferences of Language Models from Discourse Connectives](https://mlsanity.com/paper/2510.09556/) (2025)
- [Light or Full Verb? A Minimal-Pair Dataset for Probing Phraseological Competence in Language Models](https://mlsanity.com/paper/2606.05087/) (2026)
- [Which course? Discourse! Teaching Discourse and Generation in the Era of LLMs](https://mlsanity.com/paper/2602.02878/) (2026)
- [VakyArth: Evaluating Pragmatic Competence in LLMs across Indic Languages](https://mlsanity.com/paper/2609.01788/) (2026)

## Abstract (arXiv)

Coherent discourse is distinguished from a mere collection of utterances by the satisfaction of a diverse set of constraints, for example choice of expression, logical relation between denoted events, and implicit compatibility with world-knowledge. Do neural language models encode such constraints? We design an extendable set of test suites addressing different aspects of discourse and dialogue coherence. Unlike most previous coherence evaluation studies, we address specific linguistic devices beyond sentence order perturbations, allowing for a more fine-grained analysis of what constitutes coherence and what neural models trained on a language modelling objective do encode. Extending the targeted evaluation paradigm for neural language models (Marvin and Linzen, 2018) to phenomena beyond syntax, we show that this paradigm is equally suited to evaluate linguistic qualities that contribute to the notion of coherence.