Update README.md

README.md

@@ -7,7 +7,7 @@ language_creators:
 - machine-generated
 multilinguality:
 - multilingual
-pretty_name: Fact Completion Benchmark for Text Models
+pretty_name: Polyglot or Not? Fact-Completion Benchmark
 size_categories:
 - 100K<n<1M
 task_categories:
@@ -128,11 +128,21 @@ language:
 
 ### Dataset Summary
 
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+This is the dataset for **Polyglot or Not?: Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models**.
 
-### Supported Tasks and Leaderboards
+### Test Description
 
-[More Information Needed]
+ Given a factual association such as *The capital of France is **Paris***, we determine whether a model adequately "knows" this information with the following test:
+ 
+ * Step **1**: prompt the model to predict the likelihood of the token **Paris** following *The Capital of France is*
+ 
+ * Step **2**: prompt the model to predict the average likelihood of a set of false, counterfactual tokens following the same stem.
+ 
+ If the value from **1** is greater than the value from **2** we conclude that model adequately recalls that fact. Formally, this is an application of the Contrastive Knowledge Assessment proposed in [[1][bib]]. 
+ 
+ For every foundation model of interest (like [LLaMA](https://arxiv.org/abs/2302.13971)), we perform this assessment on a set of facts translated into 20 languages. All told, we score foundation models on 303k fact-completions ([results](https://github.com/daniel-furman/capstone#multilingual-fact-completion-results)). 
+ 
+ We also score monolingual models (like [GPT-2](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)) on English-only fact-completion ([results](https://github.com/daniel-furman/capstone#english-fact-completion-results)).
 
 ### Languages
 
@@ -209,12 +219,11 @@ This dataset card aims to be a base template for new datasets. It has been gener
 ### Citation Information
 
 ```
-@misc{calibragpt,
-  author = {Shreshta Bhat and Daniel Furman and Tim Schott},
-  title = {CalibraGPT: The Search for (Mis)Information in Large Language Models},
-  year = {2023},
+@misc{polyglot_or_not,
+  author = {Daniel Furman and Tim Schott and Shreshta Bhat},
+  title = {Polyglot or Not?: Measuring Multilingual Encyclopedic Knowledge Retrieval from Foundation Language Models},
+  year = {2023}
   publisher = {GitHub},
-  journal = {GitHub repository},
   howpublished = {\url{https://github.com/daniel-furman/Capstone}},
 }
 ```
@@ -243,26 +252,3 @@ This dataset card aims to be a base template for new datasets. It has been gener
 }
 ```
 
-```
-@inproceedings{elsahar-etal-2018-rex,
-    title = "{T}-{RE}x: 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)",
-    month = may,
-    year = "2018",
-    address = "Miyazaki, Japan",
-    publisher = "European Language Resources Association (ELRA)",
-    url = "https://aclanthology.org/L18-1544",
-}
-
-```
-
-### Contributions
-
-[More Information Needed]