Update README.md

README.md

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 # LIMIT-small
 
-This dataset is an MTEB-compatible version of the [original dataset](https://github.com/google-deepmind/limit). Please see that link for more details.
+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](TODO: add paper link)
+- **Code**: [github.com/google-deepmind/limit](https://github.com/google-deepmind/limit)
+- **Small version**: [LIMIT-small](https://huggingface.co/datasets/orionweller/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** (46 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 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
+@article{weller2025limit,
+  title={On the Theoretical Limitations of Embedding-Based Retrieval},
+  author={Weller, Orion and Boratko, Michael and Naim, Iftekhar and Lee, Jinhyuk},
+  journal={arXiv preprint arXiv:TODO},
+  year={2025}
+}
+```"""