update README

README.md

@@ -6,7 +6,7 @@ licenses:
 - The Creative Commons Attribution-Noncommercial 4.0 International License
 multilinguality:
 - monolingual
-pretty_name: FTrace
+pretty_name: FTRACE
 size_categories:
 - 1M<n<10M
 source_datasets:
@@ -17,7 +17,7 @@ task_categories:
 task_ids:
 - masked-language-modeling
 ---
-# Dataset Card for "FTrace"
+# Dataset Card for "FTRACE"
 ## Table of Contents
 - [Dataset Description](#dataset-description)
   - [Dataset Summary](#dataset-summary)
@@ -42,15 +42,15 @@ task_ids:
   - [Citation Information](#citation-information)
   - [Contributions](#contributions)
 ## Dataset Description
-- **Homepage:** 
-- **Repository:** 
-- **Paper:** 
-- **Point of Contact:** 
-- **Size of downloaded dataset files:** 
-- **Size of the generated dataset:** 
-- **Total amount of disk used:** 
+- **Homepage:** https://huggingface.co/datasets/ekinakyurek/FTRACE
+- **Repository:** https://github.com/ekinakyurek/influence
+- **Paper:** https://arxiv.org/pdf/2205.11482.pdf
+- **Point of Contact:** [email protected]
+- **Size of downloaded dataset files:** 113.7 MB
+- **Size of the generated dataset:** 1006.6 MB
+- **Total amount of disk used:** 1120.3 MB
 ### Dataset Summary
-
+FTRACE is a zero-shot infromation retrieval benchmark deviced for tracing a language model’s predictions back to training examples. In the accompanying paper, we evaluate commonly studied influence methods, including gradient-based (TracIn) and embedding-based approaches. The dataset contains two parts. First, factual queries for that we trace the knowledge are extracted from existing LAMA queries (Petroni et al., 2019). Second, Wikidata sentences are extracted from TREx corpus (Elsahar et al., 2018). We annotate the extracted sentences with their stated facts, and these facts can be mathed with the facts in query set. In both parts, we provide (input, target) pairs as a masked language modeling task -- see examples in the below. However, one can use the same data in other formalities for example auto-regressive completion via a processing of `input_pretokenized` and `targets_pretokenized` field. 
 ### Supported Tasks and Leaderboards
 [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
 ### Languages
@@ -148,7 +148,38 @@ The data fields are the same among all splits.
 ### Licensing Information
 The parts of this dataset are available under the [Creative Commons Attribution-ShareAlike License (CC BY-SA 4.0)](https://creativecommons.org/licenses/by-sa/4.0/) and [The Creative Commons Attribution-Noncommercial 4.0 International License](https://github.com/facebookresearch/LAMA/blob/master/LICENSE)
 ### Citation Information
+The main paper should be cited as follow:
+```
+@misc{https://doi.org/10.48550/arxiv.2205.11482,
+  doi = {10.48550/ARXIV.2205.11482},
+  url = {https://arxiv.org/abs/2205.11482},
+  author = {Akyürek, Ekin and Bolukbasi, Tolga and Liu, Frederick and Xiong, Binbin and Tenney, Ian and Andreas, Jacob and Guu, Kelvin},
+  keywords = {Computation and Language (cs.CL), Information Retrieval (cs.IR), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  title = {Tracing Knowledge in Language Models Back to the Training Data},
+  publisher = {arXiv},
+  year = {2022}, 
+}
+```
+
+Please also cite Petroni et al., 2019 for the query set, and (Elsahar et al., 2018) for the abstract set.
+
 ```
-@misc{}
+@inproceedings{petroni2019language,
+  title={Language Models as Knowledge Bases?},
+  author={F. Petroni, T. Rockt{\"{a}}schel, A. H. Miller, P. Lewis, A. Bakhtin, Y. Wu and S. Riedel},
+  booktitle={In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2019},
+  year={2019}
+}
 ```
+
+```
+@inproceedings{elsahar2018t,
+  title={T-rex: A large scale alignment of natural language with knowledge base triples},
+  author={Elsahar, Hady and Vougiouklis, Pavlos and Remaci, Arslen and Gravier, Christophe and Hare, Jonathon and Laforest, Frederique and Simperl, Elena},
+  booktitle={Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
+  year={2018}
+}
+```
+
+
 ### Contributions