Datasets:
File size: 3,609 Bytes
89feb2b 28d1dc3 2158340 28d1dc3 89feb2b 48142cc 89feb2b 903103c 28d1dc3 903103c 00ee1ee 903103c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | ---
language: en
license: cc-by-4.0
size_categories:
- 10K<n<100K
task_categories:
- text-ranking
- text-retrieval
tags:
- retrieval
- embeddings
- benchmark
dataset_info:
- config_name: default
features:
- name: query-id
dtype: string
- name: corpus-id
dtype: string
- name: score
dtype: int64
splits:
- name: test
num_examples: 2000
- config_name: corpus
features:
- name: _id
dtype: string
- name: title
dtype: string
- name: text
dtype: string
splits:
- name: corpus
num_examples: 50000
- config_name: queries
features:
- name: _id
dtype: string
- name: text
dtype: string
splits:
- name: queries
num_examples: 1000
configs:
- config_name: default
data_files:
- split: test
path: qrels.jsonl
- config_name: corpus
data_files:
- split: corpus
path: corpus.jsonl
- config_name: queries
data_files:
- split: queries
path: queries.jsonl
---
# LIMIT
A retrieval dataset that exposes fundamental theoretical limitations of embedding-based retrieval models. Despite using simple queries like "Who likes Apples?", state-of-the-art embedding models achieve less than 20% recall@100 on LIMIT full and cannot solve LIMIT-small (46 docs).
## Links
- **Paper**: [On the Theoretical Limitations of Embedding-Based Retrieval](https://arxiv.org/abs/2508.21038)
- **Code**: [github.com/google-deepmind/limit](https://github.com/google-deepmind/limit)
- **Full version**: [LIMIT](https://huggingface.co/datasets/orionweller/LIMIT/) (50k documents)
- **Small version**: [LIMIT-small](https://huggingface.co/datasets/orionweller/LIMIT-small/) (46 documents only)
## Sample Usage
You can load the data using the `datasets` library from Huggingface ([LIMIT](https://huggingface.co/datasets/orionweller/LIMIT), [LIMIT-small](https://huggingface.co/datasets/orionweller/LIMIT-small)):
```python
from datasets import load_dataset
ds = load_dataset("orionweller/LIMIT-small", "corpus") # also available: queries, test (contains qrels).
```
## Dataset Details
**Queries** (1,000): Simple questions asking "Who likes [attribute]?"
- Examples: "Who likes Quokkas?", "Who likes Joshua Trees?", "Who likes Disco Music?"
**Corpus** (50k documents): Short biographical texts describing people and their preferences
- Format: "[Name] likes [attribute1] and [attribute2]."
- Example: "Geneva Durben likes Quokkas and Apples."
**Qrels** (2,000): Each query has exactly 2 relevant documents (score=1), creating nearly all possible combinations of 2 documents from the 46 corpus documents (C(46,2) = 1,035 combinations).
### Format
The dataset follows standard MTEB format with three configurations:
- `default`: Query-document relevance judgments (qrels), keys: `corpus-id`, `query-id`, `score` (1 for relevant)
- `queries`: Query texts with IDs , keys: `_id`, `text`
- `corpus`: Document texts with IDs, keys: `_id`, `title` (empty), and `text`
### Purpose
Tests whether embedding models can represent all top-k combinations of relevant documents, based on theoretical results connecting embedding dimension to representational capacity. Despite the simple nature of queries, state-of-the-art models struggle due to fundamental dimensional limitations.
## Citation
```bibtex
@misc{weller2025theoreticallimit,
title={On the Theoretical Limitations of Embedding-Based Retrieval},
author={Orion Weller and Michael Boratko and Iftekhar Naim and Jinhyuk Lee},
year={2025},
eprint={2508.21038},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2508.21038},
}
``` |