# Superlatives in Context: Modeling the Implicit Semantics of Superlatives

arXiv: https://arxiv.org/abs/2405.20967 · HTML: https://mlsanity.com/paper/2405.20967/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 39 references that are in the map.
- First posted on arXiv 31 May 2024.

## Builds on (most cited first, 15 of 15)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://mlsanity.com/paper/1710.03957/) (2017)
- [MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines](https://mlsanity.com/paper/2007.12720/) (2020)
- [Break It Down: A Question Understanding Benchmark](https://mlsanity.com/paper/2001.11770/) (2020)
- [We're Afraid Language Models Aren't Modeling Ambiguity](https://mlsanity.com/paper/2304.14399/) (2023)
- [Multi-Sentence Argument Linking](https://mlsanity.com/paper/1911.03766/) (2019)
- [minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Models](https://mlsanity.com/paper/2203.13112/) (2022)
- [Zero and Few-shot Semantic Parsing with Ambiguous Inputs](https://mlsanity.com/paper/2306.00824/) (2023)
- [Weakly Supervised Text-to-SQL Parsing through Question Decomposition](https://mlsanity.com/paper/2112.06311/) (2021)
- ["You Are An Expert Linguistic Annotator": Limits of LLMs as Analyzers of Abstract Meaning Representation](https://mlsanity.com/paper/2310.17793/) (2023)
- [Where's My Head? Definition, Dataset and Models for Numeric Fused-Heads Identification and Resolution](https://mlsanity.com/paper/1905.10886/) (2019)
- [Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks](https://mlsanity.com/paper/2204.10615/) (2022)
- [Revisiting Sentence Union Generation as a Testbed for Text Consolidation](https://mlsanity.com/paper/2305.15605/) (2023)
- [Promptly Predicting Structures: The Return of Inference](https://mlsanity.com/paper/2401.06877/) (2024)

## Built on by (most cited first, 5 of 5)

- [EMBRACE: Shaping Inclusive Opinion Representation by Aligning Implicit Conversations with Social Norms](https://mlsanity.com/paper/2507.20264/) (2025)
- [QueryBandits for Hallucination Mitigation: Exploiting Semantic Features for No-Regret Rewriting](https://mlsanity.com/paper/2508.16697/) (2025)
- [Hint-Augmented Re-ranking: Efficient Product Search using LLM-Based Query Decomposition](https://mlsanity.com/paper/2511.13994/) (2025)
- [XYBench: Can LLMs Respond Pragmatically to Queries with Misconceptions?](https://mlsanity.com/paper/2609.06842/) (2026)
- [No One Size Fits All: QueryBandits for Hallucination Mitigation](https://mlsanity.com/paper/2602.20332/) (2026)

## Abstract (arXiv)

Superlatives are used to single out elements with a maximal/minimal property. Semantically, superlatives perform a set comparison: something (or some things) has the min/max property out of a set. As such, superlatives provide an ideal phenomenon for studying implicit phenomena and discourse restrictions. While this comparison set is often not explicitly defined, its (implicit) restrictions can be inferred from the discourse context the expression appears in. In this work we provide an extensive computational study on the semantics of superlatives. We propose a unified account of superlative semantics which allows us to derive a broad-coverage annotation schema. Using this unified schema we annotated a multi-domain dataset of superlatives and their semantic interpretations. We specifically focus on interpreting implicit or ambiguous superlative expressions, by analyzing how the discourse context restricts the set of interpretations. In a set of experiments we then analyze how well models perform at variations of predicting superlative semantics, with and without context. We show that the fine-grained semantics of superlatives in context can be challenging for contemporary models, including GPT-4.