# Grounded Misunderstandings in Asymmetric Dialogue: A Perspectivist Annotation Scheme for MapTask

arXiv: https://arxiv.org/abs/2511.03718 · HTML: https://mlsanity.com/paper/2511.03718/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [Modeling Context in Referring Expressions](https://mlsanity.com/paper/1608.00272/) (2016)
- [BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack](https://mlsanity.com/paper/2406.10149/) (2024)
- [Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning](https://mlsanity.com/paper/2305.13971/) (2023)
- [Do Vision-Language Models Represent Space and How? Evaluating Spatial Frame of Reference Under Ambiguities](https://mlsanity.com/paper/2410.17385/) (2024)
- [Flexible and Efficient Grammar-Constrained Decoding](https://mlsanity.com/paper/2502.05111/) (2025)
- [Perspective-Aware Reasoning in Vision-Language Models via Mental Imagery Simulation](https://mlsanity.com/paper/2504.17207/) (2025)
- [A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context](https://mlsanity.com/paper/1907.03399/) (2019)
- ["You Are An Expert Linguistic Annotator": Limits of LLMs as Analyzers of Abstract Meaning Representation](https://mlsanity.com/paper/2310.17793/) (2023)
- [Common Ground Tracking in Multimodal Dialogue](https://mlsanity.com/paper/2403.17284/) (2024)
- [LVLMs are Bad at Overhearing Human Referential Communication](https://mlsanity.com/paper/2509.11514/) (2025)
- [Probing LLMs for Multilingual Discourse Generalization Through a Unified Label Set](https://mlsanity.com/paper/2503.10515/) (2025)

## Built on by (most cited first, 2 of 2)

- [Seeing Is Not Sharing: Some Vision-Language Models Overestimate Common Ground in Asymmetric Dialogue](https://mlsanity.com/paper/2606.31719/) (2026)
- [Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition](https://mlsanity.com/paper/2607.14769/) (2026)

## Abstract (arXiv)

Collaborative dialogue relies on participants incrementally establishing common ground, yet in asymmetric settings they may believe they agree while referring to different entities. We introduce a perspectivist annotation scheme for the HCRC MapTask corpus (Anderson et al., 1991) that separately captures speaker and addressee grounded interpretations for each reference expression, enabling us to trace how understanding emerges, diverges, and repairs over time. Using a scheme-constrained LLM annotation pipeline, we obtain 13k annotated reference expressions with reliability estimates and analyze the resulting understanding states. The results show that full misunderstandings are rare once lexical variants are unified, but multiplicity discrepancies systematically induce divergences, revealing how apparent grounding can mask referential misalignment. Our framework provides both a resource and an analytic lens for studying grounded misunderstanding and for evaluating (V)LLMs' capacity to model perspective-dependent grounding in collaborative dialogue.