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A Practical Solution To Improve Table Reading For Generative AI

When it comes to understanding tables, a stark contrast exists between the capabilities of humans and AI. While humans can effortlessly understand a table in Excel, generative AI models often stumble in this arena. But why is generative AI not as effective with table reading?

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Published onDecember 21, 2023
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A Practical Solution To Improve Table Reading For Generative AI

When it comes to understanding tables, a stark contrast exists between the capabilities of humans and AI. While humans can effortlessly understand a table in Excel, generative AI models often stumble in this arena. But why is generative AI not as effective with table reading?

The Human Edge in Table Comprehension

Humans possess an exceptional ability to interpret tables, a skill deeply rooted in our cognitive functions. This natural proficiency allows us to swiftly grasp the context and logical connections in a table. Our brains, adept at identifying patterns and meanings, excel in understanding the hierarchical and relational structures in tabular data. This expertise is enhanced by our experience with various data formats and our advanced natural language processing skills.

AI's Struggle with Tabular Data

In contrast to humans, generative AI, which is really good at understanding and creating text, faces big problems when it comes to Excel tables and similar formats. Imagine you're looking at a jigsaw puzzle with all its pieces scattered - that's kind of how AI sees these tables. Each piece of information, like a piece of the puzzle, is separate and doesn't tell the whole story on its own. AI finds it hard to see the bigger picture that comes from putting all these pieces together.

The main issue for AI is that it doesn't quite get the subtle meanings and connections in a table. It's like if you read a sentence, you understand not just the words, but also how they fit together to make a meaning. In tables, information is broken up into small bits (like in different cells), and understanding how one bit relates to another is tough for AI.

For example, in a table showing a list of people's names and their ages, we understand easily that each name is connected to an age next to it. But for AI, making this connection isn't straightforward. It struggles to link separate bits of information in a way that makes sense, especially when the layout of the table changes or if there are missing pieces. This is why AI finds reading tables a lot harder than reading a story or an article.

AI's Proficiency in Linear Text Processing

Generative AI is like a superstar when it comes to dealing with stories, articles, or any writing that goes in a straight line – this is what we call linear text. Imagine you're reading a book; you start at the beginning and go through sentence by sentence until the end. AI models are great at this kind of task. They're like smart readers who can guess what's going to be said next. They do this by really paying attention to the words that came before and understanding how they connect to form meanings.

But, when it comes to tables, it's a whole different game. Tables aren't like stories that flow in one direction. They're more like a grid or a chessboard, where information is spread out in all directions – up, down, left, and right. For AI, which is used to the one-way street of reading text, this is like trying to read a book where the words are scattered on different pages and in different orders.

This is why AI, even though it's great at predicting the next word in a sentence or writing a piece that sounds like a human, struggles with tables. In tables, you don't just read left to right; you also have to understand how information in one row is related to another, or how what's in one column connects with other columns. This jump from linear to spatial, or grid-like, thinking is tough for AI, and that's why it's not as good with tables as it is with regular text.

A Practical Solution: Bridging AI's Gap in Table Processing

Given the challenges AI faces with table formats, a practical solution for users wanting to leverage generative AI for processing Excel files lies in transforming these tables into a format AI understands better – linear text. This approach involves converting the data in the tables into sentences that narratively connect the pieces of information, thereby making it easier for AI to interpret and analyze the data.

Converting Tables into Text

The first step is to turn the data in your Excel tables into text. This doesn't mean just copying and pasting the data as-is. Instead, it involves creating sentences that describe the data in a way that links the cells' content. For example, instead of a row with "John Doe, 30, Engineer," you could write a sentence like "John Doe is a 30-year-old engineer." This format is much more AI-friendly.

Crafting Connections Between Data

After converting the data into sentences, the next step is to establish connections between them. This involves looking at the relationships and contexts in your table and then creating sentences that express these relationships. If your table shows relationships, like a manager and their employees, you could write sentences like "Jane Smith manages a team of five, including John Doe, an engineer."

Utilizing AI for Enhanced Data Processing

Once the data is in a linear, text-based format with clear connections, you can then input it into an AI model. The AI can now process this information more effectively, as it's presented in a manner similar to the narrative content it's trained on. You can ask the AI to summarize the information, draw conclusions, or even answer specific questions about the data.

This method plays to the strengths of both the AI and the human user. While the AI is excellent at processing and generating text-based information, the human user can provide the necessary context and connections that AI currently struggles with. By converting tables into text, you bridge the gap between AI's capabilities and the requirements of table-based data, allowing for more effective data processing and analysis.

Furthermore: Enhancing Generative AI Analysis with Cross-Referencing

Cross-referencing is a powerful tool in data analysis, especially when dealing with complex or extensive Excel tables. It involves linking related pieces of information across different sections or tables, providing a more comprehensive understanding of the data. For generative AI, which excels in text-based processing, incorporating cross-referencing techniques can significantly improve its ability to analyze and interpret table data.

Excel Table Format

Imagine an Excel table where each row contains information about an individual. For instance, one row might have the following cells: "John Doe" in the first cell, "30 years old" in the second cell, and "Engineer" in the third cell. This table presents data in a structured but fragmented manner.

John Doe30 years oldEngineer

Text-Based Conversion

Now, let's convert this Excel table data into a format that's more suitable for AI processing. The row mentioned above would be transformed into a sentence: "John Doe is a 30-year-old engineer." This sentence maintains the essential information from the Excel row but presents it in a linear, narrative style that AI can process more effectively.

Implementing Cross-Referencing

To implement cross-referencing:

  1. Reference in Text: In the text version, alongside each sentence, include a reference to the specific location in the Excel table. For example, the sentence about John Doe would have an added note: "(See Excel Table, Row 4)".

  2. AI Processing: Input these sentences into the AI model. The AI will process the narrative content, and the cross-references guide the AI to correlate this information with specific locations in the Excel table for additional context or detail.

  3. Comprehensive Analysis: By using the narrative sentences and the ability to refer back to the exact row and cell in the Excel table, the AI can perform a more comprehensive analysis. It can understand the context and details of each piece of data more accurately.

Benefits of Cross-Referencing

  • Enhanced Understanding: The AI gets a clearer picture of the data, as it can connect the narrative text with the specific structured data in the Excel table.
  • Detailed Analysis: The cross-referencing allows for a deeper dive into the data, where AI can access specific details from the Excel table as needed.
  • Versatility: This method is flexible, making it useful for various types of data and analysis requirements.

In this updated example, the combination of a narrative, text-based approach with direct references to the structured Excel data enables generative AI to effectively process and analyze information that is originally presented in a tabular format. This method capitalizes on the AI's strengths in text processing while overcoming its limitations in handling table-based data.

The Current State and Future of AI in Table Comprehension

In summary, while generative AI shows remarkable capabilities in linear text interpretation and generation, its effectiveness in comprehending tabular data like Excel tables is limited. This limitation stems from the nature of tabular data and AI's current challenges in understanding non-linear relationships and contexts. As AI technology advances, we may see improvements in this area, but for the present, humans remain uniquely adept at deciphering the complexities of tables.

Table ReadingGenerative AIAI
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