orionweller/LIMIT-small
LIMIT-small 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). Introduction Vector embeddings have been tasked with an ever-increasing set of retrieval tasks over the years, with a nascent rise in using them for reasoning… See the full description on the dataset page: https://huggingface.co/datasets/orionweller/LIMIT-small.
| Author / organization | orionweller |
|---|---|
| License (as declared) | cc-by-4.0 |
| Downloads | 314 (as reported by Hugging Face) |
| Likes | 2 |
| Created | 28 August 2025 |
| Last updated at source | 5 September 2025 |
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- 17 Oct 2026 Description: LIMIT-small 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 → LIMIT-small 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
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