# Can LLMs Detect Ambiguous Plural Reference? An Analysis of Split-Antecedent and Mereological Reference

arXiv: https://arxiv.org/abs/2510.04581 · HTML: https://mlsanity.com/paper/2510.04581/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 33 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [End-to-end Neural Coreference Resolution](https://mlsanity.com/paper/1707.07045/) (2017)
- [Attribution Patching Outperforms Automated Circuit Discovery](https://mlsanity.com/paper/2310.10348/) (2023)
- [We're Afraid Language Models Aren't Modeling Ambiguity](https://mlsanity.com/paper/2304.14399/) (2023)
- [Coreference Resolution through a seq2seq Transition-Based System](https://mlsanity.com/paper/2211.12142/) (2022)
- [Zero and Few-shot Semantic Parsing with Ambiguous Inputs](https://mlsanity.com/paper/2306.00824/) (2023)
- [To Know or Not To Know? Analyzing Self-Consistency of Large Language Models under Ambiguity](https://mlsanity.com/paper/2407.17125/) (2024)
- [Free the Plural: Unrestricted Split-Antecedent Anaphora Resolution](https://mlsanity.com/paper/2011.00245/) (2020)
- [Do Pre-Trained Language Models Detect and Understand Semantic Underspecification? Ask the DUST!](https://mlsanity.com/paper/2402.12486/) (2024)
- [Incremental Sentence Processing Mechanisms in Autoregressive Transformer Language Models](https://mlsanity.com/paper/2412.05353/) (2024)
- [Trick or Neat: Adversarial Ambiguity and Language Model Evaluation](https://mlsanity.com/paper/2506.01205/) (2025)

## Abstract (arXiv)

Our goal is to study how LLMs represent and interpret plural reference in ambiguous and unambiguous contexts. We ask the following research questions: (1) Do LLMs exhibit human-like preferences in representing plural reference? (2) Are LLMs able to detect ambiguity in plural anaphoric expressions and identify possible referents? To address these questions, we design a set of experiments, examining pronoun production using next-token prediction tasks, pronoun interpretation, and ambiguity detection using different prompting strategies. We then assess how comparable LLMs are to humans in formulating and interpreting plural reference. We find that LLMs are sometimes aware of possible referents of ambiguous pronouns. However, they do not always follow human reference when choosing between interpretations, especially when the possible interpretation is not explicitly mentioned. In addition, they struggle to identify ambiguity without direct instruction. Our findings also reveal inconsistencies in the results across different types of experiments.