cardiffnlp/GrainWiC

Coarse-Grained and Fine-Grained WiC Datasets

This is the official dataset repository of WiC is Not WSD: A Study on LLMs and Lexical Ambiguity Resolution, accepted to the AACL 2026 main conference.

About

This repository contains two Word-in-Context (WiC) evaluation datasets. Given two sentences that contain the same target word, the task is to decide whether the word has the same meaning in both (True) or different meanings (False). The two datasets differ in the granularity of the sense distinctions they encode, as illustrated above for the word club:

• Coarse-grained WiC: 2,798 sentence pairs over 20 ambiguous nouns, built from CoarseWSD-20 by pairing sentences with the same target word and comparing their coarse-grained sense labels.
• Fine-grained WiC: 1,185 sentence pairs over 924 target words, newly constructed from WordNet 3.1 noun and verb entries. Each instance also includes the WordNet definition of the target word in each sentence.

Both datasets are evaluation sets and are released as a single test split.

Dataset

The two configs differ in the granularity of the sense distinctions they encode:

Config	Instances	Target words	Distinct senses	Unique sentences	True	False
coarse_grained	2,798	20	53	5,594	1,399	1,399
fine_grained	1,185	924	1,655	2,266	594	591

Both datasets are intended for evaluation only and are provided as a single test split.

Loading

from datasets import load_dataset

coarse = load_dataset("JodieChou/wic-llms", "coarse_grained", split="test")
fine   = load_dataset("JodieChou/wic-llms", "fine_grained", split="test")

coarse_grained

Constructed automatically from CoarseWSD-20 (Loureiro et al., 2021), a coarse-grained WSD dataset covering 20 ambiguous nouns. Sentences containing the same target word are paired and the WiC label is derived by comparing their coarse-grained sense labels: same sense label gives True, otherwise False. The label distribution is exactly balanced.

Field	Type	Description
id	int	Row index (0-based).
word	string	Target word (one of 20 nouns).
target_index	string	Position of the target word in sentence1 and sentence2, as index1-index2. Indices are 0-based and count whitespace-separated tokens, following the original WiC data format.
sentence1	string	First context: a sentence from CoarseWSD-20. Text is lower-cased and pre-tokenised, with tokens separated by single spaces.
sentence2	string	Second context.
label	string	"True" if the target word has the same coarse sense in both sentences, else "False".

Per-word statistics (from the paper, Table 8):

Word	Senses	Pairs	True	False
apple	2	424	212	212
arm	2	70	35	35
bank	2	46	23	23
bass	3	508	254	254
bow	3	106	53	53
chair	2	40	20	20
club	3	96	48	48
crane	2	78	39	39
deck	2	14	7	7
digit	2	10	5	5
hood	3	38	19	19
java	2	824	412	412
mole	5	70	35	35
pitcher	2	26	13	13
pound	2	20	10	10
seal	4	108	54	54
spring	3	182	91	91
square	4	82	41	41
trunk	3	38	19	19
yard	2	18	9	9
Total	53	2,798	1,399	1,399

Example:

{"word": "apple", "target_index": "6-18",
 "sentence1": "the local economy includes forestry , apple orchard and sheep and dairy farming .",
 "sentence2": "symbol is one of the four standard fonts available on all postscript - based printers , starting with apple 's original laserwriter ( 1985 ) .",
 "label": "False"}

fine_grained

A newly constructed WiC-style dataset that follows the construction of the original WiC benchmark (Pilehvar and Camacho-Collados, 2019) but is built from WordNet 3.1 noun and verb entries, with modifications intended to limit the effect of memorisation by language models. Each target word is paired with its WordNet gloss, and sentence pairs are built from the usage examples of the senses. Unlike standard WiC, each instance also carries the sense definition of the target word in each sentence, so the dataset can be used both with and without explicit sense information.

Field	Type	Description
id	int	Row index (0-based).
word	string	Target word (noun or verb lemma).
sentence1	string	First context (WordNet usage example).
sentence2	string	Second context (WordNet usage example).
definition1	string	WordNet definition of the target word's sense in sentence1.
definition2	string	WordNet definition of the target word's sense in sentence2.
label	string	"True" if definition1 and definition2 are the same sense, else "False".

Example:

{"word": "absence",
 "sentence1": "he was surprised by the absence of any explanation",
 "sentence2": "he visited during my absence",
 "definition1": "the state of being absent",
 "definition2": "the time interval during which something or somebody is away",
 "label": "False"}

Files

data/coarse_grained/test.jsonl   # 2,798 rows
data/fine_grained/test.jsonl     # 1,185 rows

Each file is JSON Lines (one JSON object per line, UTF-8). Labels in both configs are the strings "True" / "False".

Notes

• Sentences in coarse_grained are lower-cased and tokenised (punctuation separated by spaces), as in CoarseWSD-20.
• coarse_grained contains one exact duplicate pair (bank, target_index 12-5). It is kept so that the counts match the paper.
• In coarse_grained, target_index points at the target word in 2,797 of 2,798 rows. In one club row (target_index 85-19) the first index is off because of a tokenisation irregularity in the source sentence.
• These are evaluation sets. There is no training split.

Source datasets

• CoarseWSD-20: Daniel Loureiro, Kiamehr Rezaee, Mohammad Taher Pilehvar, and Jose Camacho-Collados. 2021. Analysis and Evaluation of Language Models for Word Sense Disambiguation. Computational Linguistics, 47(2):387–443.
• WordNet 3.1: George A. Miller. 1995. WordNet: A Lexical Database for English. Communications of the ACM; Christiane Fellbaum (ed.). 1998. WordNet: An Electronic Lexical Database. MIT Press.
• WiC (construction procedure): Mohammad Taher Pilehvar and Jose Camacho-Collados. 2019. WiC: the Word-in-Context Dataset for Evaluating Context-Sensitive Meaning Representations. NAACL-HLT 2019.

License and attribution

This dataset is released under CC BY-SA 4.0.

• The coarse_grained sentences and sense labels are derived from CoarseWSD-20 (Loureiro et al., 2021).
• The fine_grained definitions and example sentences come from WordNet 3.1 and are used under the WordNet License. WordNet 3.1 Copyright 2011 by Princeton University. All rights reserved.

Citation

If you use these datasets, please cite the paper:

@inproceedings{zhou2026wicnotwsd,
  title     = {WiC is Not WSD: A Study on LLMs and Lexical Ambiguity Resolution},
  author    = {Zhou, Yi and Rezaee, Kiamehr and Bollegala, Danushka and Pilehvar, Mohammad Taher and Camacho-Collados, Jose},
  booktitle = {Proceedings of the 2026 Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL 2026)},
  year      = {2026},
  url       = {https://arxiv.org/abs/2609.20593}
}

Please also cite CoarseWSD-20 when using the coarse_grained config, and WordNet when using the fine_grained config.