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
• Code: github.com/google-deepmind/limit
• Full version: LIMIT (50k documents)
• Small version: LIMIT-small (46 documents only)

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

@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}, 
}