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Jellyfish-7B

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license: cc-by-nc-4.0
language:
  - en

Jellyfish-7B

PicToModel

Jellyfish model with other sizes are available here:
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 dataset.

For interpretability, the winning rate of Jellyfish-7B against GPT-3.5-turbo (evaluated by GPT-4) 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. 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. HoloDetect for Error Detection seen datasets
    RAHA for Error Detection unseen datasets
    IPM for Data Imputation SMAT for Schema Matching
    Ditto for Entity Matching
  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 

{system message}

[INST]:

{prompt} (without the {})

[\INST]]

Prompts

We provide the prompts used for both 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 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 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 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 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 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).