Add paper link, Github link and task categories

README.md

@@ -2,8 +2,12 @@
 language:
 - en
 license: cc-by-sa-4.0
-license_link: https://huggingface.co/datasets/logo-lab/trl-dlte/blob/main/LICENSES.md
+size_categories:
+- 100K<n<10M
 pretty_name: TRL-DLTE
+license_link: https://huggingface.co/datasets/logo-lab/trl-dlte/blob/main/LICENSES.md
+task_categories:
+- other
 tags:
 - tabular
 - data-lake
@@ -12,23 +16,29 @@ tags:
 - benchmark
 - representation-learning
 - trl-bench
-size_categories:
-- 100K<n<10M
 configs:
 - config_name: manifests
   data_files:
-  - {split: train, path: data/manifests/train-*.parquet}
+  - split: train
+    path: data/manifests/train-*.parquet
 - config_name: lake
   data_files:
-  - {split: train, path: data/lake/train-*.parquet}
+  - split: train
+    path: data/lake/train-*.parquet
 - config_name: table_maps
   data_files:
-  - {split: train, path: data/table_maps/train-*.parquet}
+  - split: train
+    path: data/table_maps/train-*.parquet
 ---
 
 # TRL-DLTE
 
-Compositional **Data-Lake Table Enrichment** suite of TRL-Bench. A 47,772-table data lake derived from 1,379 TabFact and WikiTableQuestions parent tables, fragmented at four cumulative noise tiers (clean / schema / cell / hard). Each parent yields a seed query, a union target (additional rows), and a join target (additional columns); the system must recover both targets from the lake by retrieval, column alignment, and row matching.
+Compositional **Data-Lake Table Enrichment** suite of TRL-Bench.
+
+- **Paper:** [TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders](https://huggingface.co/papers/2606.09323)
+- **Code:** [LOGO-CUHKSZ/TRL-Bench](https://github.com/LOGO-CUHKSZ/TRL-Bench)
+
+TRL-DLTE features a 47,772-table data lake derived from 1,379 TabFact and WikiTableQuestions parent tables, fragmented at four cumulative noise tiers (clean / schema / cell / hard). Each parent yields a seed query, a union target (additional rows), and a join target (additional columns); the system must recover both targets from the lake by retrieval, column alignment, and row matching.
 
 This repository ships two configs:
 
@@ -116,6 +126,17 @@ distractors = lake["train"].filter(lambda x: x["kind"] == "distractor")
 union_targets = lake["train"].filter(lambda x: x["kind"] == "target_union")
 ```
 
+## Citation
+
+```bibtex
+@article{pang2026trl,
+  title={TRL-Bench: Standardizing Cross-Paradigm Representation-Level Evaluation of Tabular Encoders},
+  author={Pang, Wei and Jian, Xiangru and Li, Hehan and Yu, Zhixuan and Xue, Alex and Li, Jinyang and Dong, Zhengyuan and Zhao, Xinjian and Xu, Hao and Zhang, Chao and Cheng, Reynold and {\"O}zsu, M. Tamer and Yu, Tianshu},
+  journal={arXiv preprint arXiv:2606.09323},
+  year={2026}
+}
+```
+
 ## License
 
-Umbrella **CC-BY-SA-4.0** (driven by 390 WTQ-derived parents). Per-component licenses in `LICENSES.md`. Manifest records and the fragmentation config are TRL-Bench-derived under CC-BY-4.0; the lake content inherits per-source upstream licenses (TabFact: CC-BY 4.0; WTQ: CC-BY-SA 4.0; CKAN distractors: CC-BY 4.0 record / per-portal upstream).
+Umbrella **CC-BY-SA-4.0** (driven by 390 WTQ-derived parents). Per-component licenses in `LICENSES.md`. Manifest records and the fragmentation config are TRL-Bench-derived under CC-BY-4.0; the lake content inherits per-source upstream licenses (TabFact: CC-BY 4.0; WTQ: CC-BY-SA 4.0; CKAN distractors: CC-BY 4.0 record / per-portal upstream).