metadata
license: cc-by-nc-4.0
language:
- en
Jellyfish-7B
Other versions of Jellyfish:
Jellyfish-8B
Jellyfish-13B
Model Details
Jellyfish-7B is a large language model equipped with 7 billion parameters.
We fine-tuned the mistralai/Mistral-7B-Instruct-v0.2 model using
a subset of the Jellyfish-Instruct
Jellyfish-7B vs GPT-3.5-turbo wining rate by GPT4 evaluation is 56.36%.
More details about the model can be found in the Jellyfish paper.
- Developed by: Haochen Zhang, Yuyang Dong, Chuan Xiao, Masafumi Oyamada
- Contact: [email protected]
- Funded by: NEC Corporation, Osaka University
- Language(s) (NLP): English
- License: Non-Commercial Creative Commons license (CC BY-NC-4.0)
- Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2
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.
- Ditto for Entity Matching
SMAT for Schema Matching
HoloDetect for Error Detection seen datasets
RAHA for Error Detection unseen datasets
IPM for Data Imputation - 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.
- 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.
Prompt Template
{system message}
[INST]:
{prompt} (without the {})
[\INST]]
Prompts
We provide the prompts used for both the model's fine-tuning and inference. You can structure your data according to these prompts.
System Message
You are an AI assistant that follows instruction extremely well.
User will give you a question. Your task is to answer as faithfully as you can.
For Entity Matching
You are tasked with determining whether two records listed below are the same based on the information provided.
Carefully compare the {attribute 1}, {attribute 2}... for each record before making your decision.
Note that missing values (N/A or \"nan\") should not be used as a basis for your decision.
Record A: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}, ...]
Record B: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}, ...]
Are record A and record B the same entity? Choose your answer from: [Yes, No].
For Data Imputation
You are presented with a {keyword} record that is missing a specific attribute: {attribute X}.
Your task is to deduce or infer the value of {attribute X} using the available information in the record.
You may be provided with fields like {attribute 1}, {attribute 2}, ... to help you in the inference.
Record: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}, ...]
Based on the provided record, what would you infer is the value for the missing attribute {attribute X}?
Answer only the value of {attribute X}.
For Data Imputation
You are presented with a {keyword} record that is missing a specific attribute: {attribute X}.
Your task is to deduce or infer the value of {attribute X} using the available information in the record.
You may be provided with fields like {attribute 1}, {attribute 2}, ... to help you in the inference.
Record: [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}, ...]
Based on the provided record, what would you infer is the value for the missing attribute {attribute X}?
Answer only the value of {attribute X}.
For Error Detection
There are two forms of the error detection task. In the first form, a complete record row is provided, and the task is to determine if a specific value is erroneous. In the second form, only the value of a specific attribute is given, and the decision about its correctness is based solely on the attribute's name and value. The subsequent prompt examples pertain to these two forms, respectively.
Your task is to determine if there is an error in the value of a specific attribute within the whole record provided.
The attributes may include {attribute 1}, {attribute 2}, ...
Errors may include, but are not limited to, spelling errors, inconsistencies, or values that don't make sense given the context of the whole record.
Record [{attribute 1}: {attribute 1 value}, {attribute 2}: {attribute 2 value}, ...]
Attribute for Verification: [{attribute X}: {attribute X value}]
Question: Is there an error in the value of {attribute X}? Choose your answer from: [Yes, No].
Your task is to determine if there is an error in the value of a specific attribute.
The attributes may belong to a {keyword} record and could be one of the following: {attribute 1}, {attribute 2}, ...
Errors can include, but are not limited to, spelling errors, inconsistencies, or values that don't make sense for that attribute.
Note: Missing values (N/A or \"nan\") are not considered errors.
Attribute for Verification: [{attribute X}: {attribute X value}]
Question: Is there an error in the value of {attribute X}? Choose your answer from: [Yes, No].
For Schema Matching
Your task is to determine if the two attributes (columns) are semantically equivalent in the context of merging two tables.
Each attribute will be provided by its name and a brief description.
Your goal is to assess if they refer to the same information based on these names and descriptions provided.
Attribute A is [name: {value of name}, description: {value of description}].
Attribute B is [name: {value of name}, description: {value of description}].
Are Attribute A and Attribute B semantically equivalent? Choose your answer from: [Yes, No].
For Column Type Annotation
We follow the prompt in Column Type Annotation using ChatGPT (text+inst+2-step).
For Attribute Value Extraction
We follow the prompt in Product Attribute Value Extraction using Large Language Models (textual, w/o examples).