---
title: "Exceptions, Instantiations, and Overgeneralization: Insights into"
description: "The authors first propose a theoretical framework, GenerIX. 49 references, cited by 6 papers here."
authors: ["Emily Allaway", "Chandra Bhagavatula", "Jena D. Hwang", "Kathleen McKeown", "Sarah-Jane Leslie"]
year: 2024
venue: "Computational Linguistics"
source: "https://aclanthology.org/2024.cl-4.2/"
doi: "10.1162/coli_a_00530"
licence: "CC BY-NC-ND 4.0 (Computational Linguistics, MIT Press)"
licence_url: "https://creativecommons.org/licenses/by-nc-nd/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/allaway-2024-exceptions-instantiations-overgeneralization-insights-into/"
---
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# Exceptions, Instantiations, and Overgeneralization: Insights into How Language Models Process Generics

**Authors:** [Emily Allaway](https://md.research.elamur.ai/free/en/authors/emily-allaway/), [Chandra Bhagavatula](https://md.research.elamur.ai/free/en/authors/chandra-bhagavatula/), [Jena D. Hwang](https://md.research.elamur.ai/free/en/authors/jena-d-hwang/), [Kathleen McKeown](https://md.research.elamur.ai/free/en/authors/kathleen-mckeown/) and Sarah-Jane Leslie · **Year:** 2024 · **Venue:** [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/) ([Computational Linguistics 2024](https://md.research.elamur.ai/free/en/venues/cl/2024/)) · **Publisher:** [The MIT Press](https://md.research.elamur.ai/free/en/publishers/the-mit-press/) · **Licence:** [CC BY-NC-ND 4.0 (Computational Linguistics, MIT Press)](https://creativecommons.org/licenses/by-nc-nd/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.cl-4.2/) · [PDF](https://aclanthology.org/2024.cl-4.2.pdf) · [DOI 10.1162/coli\_a\_00530](https://doi.org/10.1162/coli_a_00530) · [OpenAlex](https://openalex.org/W4401117634)

**Citations:** 7 citations (OpenAlex, 2026-10-14) · [6](https://md.research.elamur.ai/free/en/papers/allaway-2024-exceptions-instantiations-overgeneralization-insights-into/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary

The paper investigates how large language models (LLMs) reason about generics. Generics are generalizations without an explicit quantifier, such as "birds fly." They remain acceptable despite exceptions (penguins) and extend to specific cases (sparrows). The authors first propose a theoretical framework, GenerIX. It builds on the pragmatic notion of focus and provides logical definitions of several readings of a generic, as well as of its exceptions and instantiations (collectively called exemplars). These definitions are implemented in the ExempliFI system, which automatically generates such exemplars using LLMs. This yields a dataset of approximately 370 thousand exemplars for about 17 thousand generics, part of which was manually verified by humans. The dataset was then used to test two phenomena known from human psychology: overgeneralization (GOG) and property inheritance. The models tend to overgeneralize, as humans do, and exceptions do not fully eliminate this tendency. The models do draw inferences from generics, but when contradictory information appears, they revise them inconsistently. This matters because most natural language reasoning systems are built on LLMs.

## Key points

- **Two resources:** the theoretical framework GenerIX, which determines what counts as an exception depending on focus: in "BIRDS fly," the exceptions are other flyers, such as bats; and the generator ExempliFI, which works with LLMs.
- **Dataset:** ≈370K exemplars for ≈17K generics. They include both knowledge-based examples ("ostriches do not fly") and reasoning-based examples (a bird with a broken wing). Quality was verified by humans on a sample.
- **The generics are taken from the sets** AnimalG, GGTest and GGSmall. For comparison, the authors present a simple intermediate baseline on GPT-3.5-Turbo. Its exceptions rely mostly on knowledge rather than on reasoning.
- **The GOG effect** (Generic OverGeneralization) consists in treating a statement with "all" as a generic compatible with exceptions. In humans who have just thought about an exception, it drops to about 20% of accepted responses, while without such a prompt it is about 50%. This effect was also found in LLMs, and exceptions do not remove it.
- **Property inheritance:** the models infer from a generic to an individual member, but after a counterexample they adjust this inference less consistently than humans do.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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- [Columbia University](https://md.research.elamur.ai/free/en/universities/columbia-university/) — University, New York, United States · 263 works · 57,820 citations
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## Cite

Cite as: Emily Allaway, Chandra Bhagavatula, Jena D. Hwang, Kathleen McKeown and Sarah-Jane Leslie. 2024. Exceptions, Instantiations, and Overgeneralization: Insights into How Language Models Process Generics. Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{allaway2024exceptions,
  title = {{Exceptions, Instantiations, and Overgeneralization: Insights into How Language Models Process Generics}},
  author = {Emily Allaway, Chandra Bhagavatula, Jena D. Hwang, Kathleen McKeown and Sarah-Jane Leslie},
  journal = {Computational Linguistics},
  year = {2024},
  doi = {10.1162/coli\_a\_00530},
  url = {https://doi.org/10.1162/coli_a_00530}
}
```

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*Updated 3 November 2026*