Series 5/6: Comprehensive Case Study on Extracting Data from W-2, W-8BEN, and W-9 Forms Using LLMs
Detailed Analysis and Sample Data from W9, W8 and W-8BEN Form Extraction
Table of contents
Introduction
In the ever-evolving realm of natural language processing (NLP), the extraction of structured information from unstructured documents is a critical task. This comprehensive case study explores the performance of various Large Language Models (LLMs) on three important document types: W-2, W-8BEN, and W-9 forms. By analyzing the accuracy of models such as ChatGPT 3.5, Gemini 1.0, Gemini Vision 1.0, and different configurations of Llama2, we aim to shed light on their capabilities and limitations in document extraction tasks.
Case Study 1: W-9 Form Extraction
Analysis of LLM Performance on W-9 Forms
the spotlight turns to the W9 form, a critical document in our evaluation of document extraction accuracy. The introduction to the results of the W9 form sets the stage for a detailed analysis of how different Large Language Models (LLMs) performed in extracting key information. From ChatGPT 3.5 to Gemini 1.0, Gemini Vision 1.0, and varying LLAmla2 models, we scrutinize each model's efficacy in capturing essential details from the W9 form. This examination is grounded in a comparison with ground truth values, providing a reliable benchmark for accuracy assessment. Readers will gain insights into the specific nuances and efficiencies demonstrated by each LLM in handling the intricacies of W9 form extraction, contributing to a nuanced understanding of the overall document extraction landscape.
The table in the image you provided presents a detailed analysis of the accuracy of different Large Language Models (LLMs) in extracting specific fields from a W9 form. Here’s a comprehensive explanation:
Key Findings:
Name: All models achieved 100% accuracy.
Business Name: ChatGPT and Gemini reached 100% accuracy, while Llama2 models had varying accuracies (80% for Llama2 70b, 96% for Llama2 13b, and 18% for Llama2 7b).
Address: ChatGPT achieved 100%, with Llama2 models slightly lower (99% for Llama2 70b and 13b, 95% for Llama2 7b).
City, State, and ZIP Code: None of the models performed well on this field, all scoring 0%.
List Account Number(s): ChatGPT and Gemini reached 100%, while Llama2 models had lower scores (16% for Llama2 70b, 18% for Llama2 13b, 88% for Llama2 7b).
Employer Identification Number: All models scored 0%.
Exempt Payee Code: ChatGPT (44%), Gemini (93%), Llama2 70b and 13b (100%), Llama2 7b (0%).
Exemption from FATCA Reporting Code: ChatGPT (44%), Gemini (34%), Llama2 70b (2%), Llama2 13b (94%), Llama2 7b (4%).
Requester’s Name and Address: ChatGPT (100%), Gemini (62%), Llama2 models (0%).
Social Security Number: ChatGPT (100%), Gemini (98%), Llama2 70b and 13b (96%), Llama2 7b (62%).
Overall, the accuracy of the models on the W9 form fields varies. Some models perform well in some fields, while others perform poorly. The best-performing model overall is ChatGPT, with a total accuracy of 68.8%.
Visual Representation: The bar graph below illustrates the comparative accuracy of the LLMs in extracting data from W-9 forms.
The bar graph in the image you provided is a visual representation of the accuracy of different Large Language Models (LLMs) in extracting information from W9 forms. The graph compares five different LLM models: chatgpt, gemini, llama2 70b, llama2 13b, and llama2 7b. These models have been trained with different configurations and capacities, which can lead to differences in their performance.
Accuracy: The y-axis of the graph represents the accuracy of the models in percentage terms. Accuracy is a common metric in machine learning that measures the proportion of correct predictions made by the model out of all predictions.
Performance of the Models:
ChatGPT and Gemini: These models have the highest accuracy, both above 60%. This suggests that they are more reliable for the task of information extraction from W9 forms.
Llama2 70b: This model has slightly lower accuracy, indicating that it may not perform as well as ChatGPT and Gemini for this specific task.
Llama2 13b: This model has an accuracy close to ChatGPT and Gemini, suggesting that it’s also a good choice for this task.
Llama2 7b: This model has the lowest accuracy, below 40%, indicating that it may not be the best choice for this task.
In conclusion, the graph provides a comparative analysis of different LLMs for the task of information extraction from W9 forms. It highlights the importance of choosing the right model for specific tasks to achieve the best performance. However, it’s important to note that the performance of these models can vary depending on the specific task and data they are trained on. Therefore, continual evaluation and comparison of these models are necessary to ensure optimal performance.
Case Study: W-8BEN Form Extraction
Analysis of LLM Performance on W-8BEN Forms
The W-8BEN form, another crucial tax document, was evaluated similarly. The accuracy of various LLMs in extracting their fields was assessed to understand their effectiveness. Our focus turns to the W8 form, delving into the outcomes of document extraction accuracy across different Large Language Models (LLMs). The introduction sets the context for a comprehensive evaluation, highlighting the performance of LLMs such as ChatGPT 3.5, Gemini 1.0, Gemini Vision 1.0, and varying LLAmla2 models in extracting crucial information from W8 forms. Ground truth values act as the reference point, enabling a meticulous comparison of the accuracy achieved by each model. Readers will gain valuable insights into the nuances of W8 form extraction, witnessing how distinct LLMs navigate the complexities of this document type. The discussion encapsulates both successes and challenges encountered, contributing to a holistic understanding of LLM capabilities in handling W8 forms within the document extraction framework.
The table shows the accuracy of different LLM models in extracting data from W-8 BEN forms. The table includes five LLM models: ChatGPT, Gemini (which is me!), Llama 7b, Llama 13b, and Llama 70b. It shows the accuracy for each model on nine different fields in the W-8 BEN form.
Overall, the accuracy across all models is very high, with some models achieving 100% accuracy in several fields. Here’s a breakdown of the accuracy by field:
Key Findings:
Name of Individual Beneficial Owner: All models except ChatGPT achieved 100% (ChatGPT: 90%).
Country of Citizenship: All models achieved 100%.
Permanent Residence Address: Gemini and Llama2 70b achieved 100%, others ranged from 14% to 17%.
Foreign Tax Identifying Number: Llama2 70b achieved 97%, others ranged from 58% to 60%.
Reference Number(s): ChatGPT and Llama2 70b achieved 98%, others ranged from 3% to 6%.
Date of Birth: All models except Llama2 13b and 70b achieved 100% (Llama2 13b: 87%, Llama2 70b: 88%).
City or Town, State or Province: Gemini achieved 100%, others ranged from 60% to 80%.
Country: All models except Llama2 13b achieved 100% (Llama2 13b: 73%).
U.S. Taxpayer Identification Number: ChatGPT and Gemini achieved 96%, others ranged from 60% to 90%.
Overall, the table suggests that LLM models can be very accurate at extracting data from W-8 BEN forms. However, the accuracy does vary depending on the specific model and field. The table doesn’t say anything about how the accuracy was measured or how statistically significant the results are. It’s also important to note that the W-8 BEN form is a relatively simple document, and the accuracy of these models on more complex documents may be lower
Visual Representation: The bar graph below shows the accuracy of LLMs in extracting data from W-8BEN forms.
The bar graph in the image you provided compares the accuracy of different Large Language Models (LLMs) in extracting information from W-8BEN forms. The graph compares five different LLM models: ChatGPT, Gemini, Llama2 70b, Llama2 13b, and Llama2 7b. These models have been trained with different configurations and capacities, which can lead to differences in their performance. The y-axis of the graph represents the accuracy of the models in percentage terms. Accuracy is a common metric in machine learning that measures the proportion of correct predictions made by the model out of all predictions. Here’s a detailed explanation:
Performance of the Models:
ChatGPT and Gemini: These models have the highest accuracy, both above 60%. This suggests that they are more reliable for the task of information extraction from W-8BEN forms.
Llama2 70b: This model has slightly lower accuracy, indicating that it may not perform as well as ChatGPT and Gemini for this specific task.
Llama2 13b: This model has an accuracy close to ChatGPT and Gemini, suggesting that it’s also a good choice for this task.
Llama2 7b: This model has the lowest accuracy, below 40%, indicating that it may not be the best choice for this task.
In conclusion, the graph provides a comparative analysis of different LLMs for the task of information extraction from W-8BEN forms. It highlights the importance of choosing the right model for specific tasks to achieve the best performance. However, it’s important to note that the performance of these models can vary depending on the specific task and data they are trained on. Therefore, continual evaluation and comparison of these models are necessary to ensure optimal performance. Case Study: W-2 Form Extraction
Case Study: W-2 Form Extraction
Analysis of LLM Performance on W-2 Forms
In the evaluation of document extraction accuracy, the focus shifts to the W2 form, a crucial financial document. This section delves into the intricate details of W2 model accuracy, presenting a meticulous examination of the outcomes produced by various Large Language Models (LLMs). The W2 form, known for its comprehensive representation of wage and tax-related information, poses specific challenges for accurate data extraction. Our analysis encompasses a comparative study of LLMs, including ChatGPT 3.5, Gemini 1.0, Gemini Vision 1.0, and LLAmla2 with parameter variations. The ensuing discussion illuminates the strengths and limitations of each model in accurately capturing and extracting relevant information from the W2 form, offering valuable insights for the broader field of document extraction in natural language processing.
Key Findings:
High Accuracy Fields: Employer Identification Number (EIN), Employer Name, Social Security Number (SSN), Medicare Wages, Social Security Wages, State Wages, Total Wages.
Lower Accuracy Fields: Address, Allocated Tips, Dependent Care Benefits, Local Income Tax, Locality Name, State Income Tax.
Model Performance:
Gemini: Generally high accuracy (>80%) across many fields.
ChatGPT: High accuracy in many fields but slightly lower in Address and Medicare Tax.
Llama2 Models: Varied accuracy, with Llama2 70b performing well in some fields but not others.
Overall, the table provides a helpful starting point for understanding how LLMs perform on W-2 data extraction. However, for a more comprehensive evaluation, it would be beneficial to consider the above limitations and look for additional information about the methodology and broader testing on various W-2 forms.
Visual Representation: The bar graph below compares the accuracy of LLMs in extracting data from W-2 forms.
The bar graph in the image you provided compares the accuracy of different Large Language Models (LLMs) in extracting information from W2 forms. The graph compares five different LLM models: chatgpt, gemini, llama2 70b, llama2 13b, and llama2 7b. These models have been trained with different configurations and capacities, which can lead to differences in their performance. The y-axis of the graph represents the accuracy of the models in percentage terms. Accuracy is a common metric in machine learning that measures the proportion of correct predictions made by the model out of all predictions. Here’s a detailed explanation:
Performance of the Models:
ChatGPT and Gemini: These models have the highest accuracy, both above 60%. This suggests that they are more reliable for the task of information extraction from W2 forms.
Llama2 70b: This model has slightly lower accuracy, indicating that it may not perform as well as ChatGPT and Gemini for this specific task.
Llama2 13b: This model has an accuracy close to ChatGPT and Gemini, suggesting that it’s also a good choice for this task.
Llama2 7b: This model has the lowest accuracy, below 40%, indicating that it may not be the best choice for this task.
Conclusion
In conclusion, the graph provides a comparative analysis of different LLMs for the task of information extraction from W2 forms. It highlights the importance of choosing the right model for specific tasks to achieve the best performance. However, it’s important to note that the performance of these models can vary depending on the specific task and data they are trained on. Therefore, continual evaluation and comparison of these models are necessary to ensure optimal performance.
This comprehensive case study demonstrates that LLMs can be highly effective in extracting data from complex forms like W-2, W-8BEN, and W-9. ChatGPT and Gemini models consistently showed high accuracy across various fields, highlighting their reliability for these tasks. However, certain fields, such as addresses and local tax details, proved more challenging for all models, suggesting areas for further refinement and training.
Choosing the right model for specific document extraction tasks is crucial for achieving optimal performance. Continuous evaluation and comparison of these models are necessary to ensure they meet the desired accuracy standards for various applications. As LLM technology advances, we can expect even greater precision and efficiency in handling complex document extraction tasks, driving advancements in NLP and related fields.
By combining the detailed analyses of W-9, W-8BEN, and W-2 forms, this blog post provides a thorough understanding of LLM capabilities and their practical applications in document extraction.
Coming Up Next
Blog Post 6: CONCLUSIONS AND RECOMMENDATIONS
- Title: The Future of Document Data Extraction with LLMs
Content:
Summarize the key findings from the research.
Discuss the potential future developments in LLM-based data extraction.
Conclude with the impact of this research on the field of document data extraction.
For a more detailed exploration of this topic, including methodologies, data sets, and further analysis, please refer to my Master's Thesis and Thesis Presentation.
LinkedIn link - https://www.linkedin.com/in/pramod-gupta-b1027361/