Update README.md

README.md

@@ -12,10 +12,10 @@ language:
 Jellyfish-13B is a large language model with 13 billion parameters, designed specifically for data managment and preprocessing tasks, such as entity matching, data imputation, error detection, and schema matching.
 
 We fine-tuned [Open-Orca/OpenOrca-Platypus2-13B](https://huggingface.co/Open-Orca/OpenOrca-Platypus2-13B) using the datasets related to data preprocessing tasks.
-Its performance is competitive, standing up well against prior state-of-the-art algorithms and LLMs such as OpenAI GPT 3.5 and GPT 4 (evaluated by our previous work, https://arxiv.org/abs/2205.09911).
+Its performance is competitive, standing up well against prior state-of-the-art algorithms and LLMs such as OpenAI GPT 3.5 and GPT 4 ([evaluated by our previous work](https://arxiv.org/abs/2308.16361)).
 Note that Jellyfish is only a 13B model and can be run locally for low cost and data security.
 
-|  Task  | Dataset | Non-LLM SoTA | GPT-3.5 | GPT-4 | Jellyfish-13B| Jellyfish-13B-Resoning |
+|  Task  | Dataset | Non-LLM SoTA<sup>1</sup> | GPT-3.5<sup>2</sup> | GPT-4<sup>2</sup> | Jellyfish-13B| Jellyfish-13B-Resoning |
 | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
 | Entity Matching  | Fodors-Zagats  | 100  | 100 | 100 | 100 | 100 |
 | Entity Matching  | Beer           | 94.37| 96.30 | 100 | 93.33 | 100 | 
@@ -24,12 +24,19 @@ Note that Jellyfish is only a 13B model and can be run locally for low cost and
 | Entity Matching  | DBLP-ACM       | 98.99| 96.99 | 97.44 | 97.35 | 95.74 | 
 | Entity Matching  | DBLP-GoogleScholar | 95.60| 76.12 | 91.87 | 92.83 | 89.45 | 
 | Entity Matching  | Amazon-Google  | 75.58| 66.53 | 74.21 | 72.69 | 56.64 | 
-| Imputation       |  Restaurant    | 77.20| 94.19 | 97.67 | 94.19 | 93.02 | 
-| Imputation       |  Buy           | 96.50| 98.46 | 100 | 100 | 100 | 
+| Data Imputation  |  Restaurant    | 77.20| 94.19 | 97.67 | 94.19 | 93.02 | 
+| Data Imputation  |  Buy           | 96.50| 98.46 | 100 | 100 | 100 | 
 | Error Detection  |  Hosptial      | 99.10| 90.74 | 90.74 | 92.21 | 65.66 | 
 | Error Detection  |  Adult         | 94.40| 92.01 | 92.01 | 96.62 | 90.13 | 
-| Schema Matching  |  Sythea        | 38.50| 57.14 | 66.67 | 36.36 | 30.77 | \\
-_Accuracy as the metric for data imputation, and the f1 score for other tasks._
+| Schema Matching  |  Sythea        | 38.50| 57.14 | 66.67 | 36.36 | 30.77 |  
+
+_Accuracy as the metric for data imputation, and the f1 score for other tasks._  
+1.  
+  [Ditto](https://arxiv.org/abs/2004.00584) for Entity Matching  
+  [SMAT](https://www.researchgate.net/publication/353920530_SMAT_An_Attention-Based_Deep_Learning_Solution_to_the_Automation_of_Schema_Matching) for Schema Matching  
+  [HoloDetect](https://arxiv.org/abs/1904.02285) for Error Detection  
+  [HoloClean](https://arxiv.org/abs/1702.00820) for Data Imputation  
+2. [Large Language Models as Data Preprocessors](https://arxiv.org/abs/2308.16361)
 
 We release two versions of Jellyfish: the Jellyfish-13B (the main branch) and Jellyfish-13B-Reasoning.
 As the names suggest, Jellyfish-13B focuses on providing accurate, direct answers.