Jellyfish-8B

Model Details

Jellyfish-8B is a large language model equipped with 8 billion parameters.
We fine-tuned the Meta-Llama-3-8B-Instruct model using the datasets pertinent to data preprocessing tasks. The training data is used a subset of the [Jellyfish-Instruct] (https://huggingface.co/datasets/NECOUDBFM/Jellyfish-Instruct)

More details about the model can be found in the Jellyfish paper.

• Developed by: Haochen Zhang, Yuyang Dong, Chuan Xiao, Masafumi Oyamada
• Contact: dongyuyang@nec.com
• Funded by: NEC Corporation, Osaka University
• Language(s) (NLP): English
• License: Non-Commercial Creative Commons license (CC BY-NC-4.0)
• Finetuned from model: Meta-Llama-3-8B-Instruct

Citation

If you find our work useful, please give us credit by citing:

@article{zhang2023jellyfish,
  title={Jellyfish: A Large Language Model for Data Preprocessing},
  author={Zhang, Haochen and Dong, Yuyang and Xiao, Chuan and Oyamada, Masafumi},
  journal={arXiv preprint arXiv:2312.01678},
  year={2023}
}

Performance on seen tasks

Task	Type	Dataset	Non-LLM SoTA1	GPT-3.52	GPT-42	GPT-4o	Table-GPT	Jellyfish-7B	Jellyfish-8B	Jellyfish-13B
Error Detection	Seen	Adult	99.10	99.10	92.01	83.58	--	77.40	73.74	99.33
Error Detection	Seen	Hospital	94.40	97.80	90.74	44.76	--	94.51	93.40	95.59
Error Detection	Unseen	Flights	81.00	--	83.48	66.01	--	69.15	66.21	82.52
Error Detection	Unseen	Rayyan	79.00	--	81.95	68.53	--	75.07	81.06	90.65
Data Imputation	Seen	Buy	96.50	98.50	100	100	--	98.46	98.46	100
Data Imputation	Seen	Restaurant	77.20	88.40	97.67	90.70	--	89.53	87.21	89.53
Data Imputation	Unseen	Flipkart	68.00	--	89.94	83.20	--	87.14	87.48	81.68
Data Imputation	Unseen	Phone	86.70	--	90.79	86.78	--	86.52	85.68	87.21
Schema Matching	Seen	MIMIC-III	20.00	--	40.00	29.41	--	53.33	45.45	40.00
Schema Matching	Seen	Synthea	38.50	45.20	66.67	6.56	--	55.56	47.06	56.00
Schema Matching	Unseen	CMS	50.00	--	19.35	22.22	--	42.86	38.10	59.29
Entity Matching	Seen	Amazon-Google	75.58	63.50	74.21	70.91	70.10	81.69	81.42	81.34
Entity Matching	Seen	Beer	94.37	100	100	90.32	96.30	100.00	100.00	96.77
Entity Matching	Seen	DBLP-ACM	98.99	96.60	97.44	95.87	93.80	98.65	98.77	98.98
Entity Matching	Seen	DBLP-GoogleScholar	95.70	83.80	91.87	90.45	92.40	94.88	95.03	98.51
Entity Matching	Seen	Fodors-Zagats	100	100	100	93.62	100	100	100	100
Entity Matching	Seen	iTunes-Amazon	97.06	98.20	100	98.18	94.30	96.30	96.30	98.11
Entity Matching	Unseen	Abt-Buy	89.33	--	92.77	78.73	--	86.06	88.84	89.58
Entity Matching	Unseen	Walmart-Amazon	86.89	87.00	90.27	79.19	82.40	84.91	85.24	89.42
Avg			80.44	-	84.17	72.58	-	82.74	81.55	86.02

For GPT-3.5 and GPT-4, we used the few-shot approach on all datasets. However, for Jellyfish models, the few-shot approach is disabled on seen datasets and enabled on unseen datasets.
Accuracy as the metric for data imputation and the F1 score for other tasks.

1. Ditto for Entity Matching
   SMAT for Schema Matching
   HoloDetect for Error Detection seen datasets
   RAHA for Error Detection unseen datasets
   IPM for Data Imputation
2. Large Language Models as Data Preprocessors

Performance on unseen tasks

Column Type Annotation

Dataset	RoBERTa (159 shots)1	GPT-3.51	GPT-4	GPT-4o	Jellyfish-7B	Jellyfish-8B	Jellyfish-13B
SOTAB	79.20	89.47	91.55	65.05	83	76.33	82

Few-shot is disabled for Jellyfish models.

1. Results from Column Type Annotation using ChatGPT

Attribute Value Extraction

Dataset	Stable Beluga 2 70B1	SOLAR 70B1	GPT-3.51	GPT-4 1	GPT-4o	Jellyfish-7B	Jellyfish-8B	Jellyfish-13B
AE-110k	52.10	49.20	61.30	55.50	55.77	56.09	59.55	58.12
OA-Mine	50.80	55.20	62.70	68.90	60.20	51.98	59.22	55.96

Few-shot is disabled for Jellyfish models.

1. Results from Product Attribute Value Extraction using Large Language Models

Prompt Template

<|start_header_id|>system<|end_header_id|>{system message}<|eot_id|>
<|start_header_id|>user<|end_header_id|>{prompt}<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>