Fixing the Gemini 2.0 Flash Markdown Table Generation Bug

Fixing the Gemini 2.0 Flash Markdown Table Generation Bug

If you’ve been encountering challenges while working with Google’s Gemini 2.0 Flash model, particularly failures when generating Markdown tables, you’re not alone. Many users, especially those involved in PDF data extraction and structured content generation, have experienced random crashes, excessive whitespace, or incomplete tables using Gemini 2.0 Flash—specifically when a temperature setting of 0 is applied.

After extensive testing, solutions have been identified to mitigate this issue, and a theory has been proposed as to why it occurs.

The Markdown Table Bug in Gemini 2.0 Flash

Conditions for the Bug

The Markdown table generation issue has been observed under the following conditions:

• PDF content like financial statements or structured data is in the model’s context.
• Tasks involve complex operations, such as merging multiple tables.
• Output requirements specify GitHub Flavored Markdown (GFM).
• The model’s temperature is set to 0.

What Happens?

When these conditions are met, the model begins to generate a table but often fails midway. This typically occurs after generating the second or third column header. Instead of finishing the table, the model inserts random whitespace and may enter a loop, failing to produce a usable output.

Example Scenario

Consider an attempt to combine tables from Google’s 2023 Financial Statement:

1. Download Google’s 2023 Financial Statement (10-K Report).
2. Add the PDF to the model’s context.
3. Use the prompt: “Try to combine as many tables as possible in a single table of GitHub Flavored Markdown format.”
4. Set temperature = 0 and run the request.

Here’s a snippet of the common crash output:

## Alphabet Inc. - Consolidated Financial Data (2021-2023)

| Description                                      | 2021 (Millions) | 2022 (Millions) | 2023 (Millions) 

After which there is a lot of whitespace, indicating the model’s failure to complete the table.

Pourquoi ça se produit : Problème de prédiction des tokens

Probable Cause

This issue likely stems from a token prediction error:

• Low Temperature Setting: With a temperature of 0, the model always selects the most probable token.
• Space-Sensitivity of Markdown Tables: In Gemini’s training data, tables often have longer headers in the second or third columns, which might confuse token prediction.
• Whitespace Predictions: The model predicts a space as the next token rather than a needed completion token, leading to excessive whitespace and potential looping.

The failure rate increases at the second or third column header as the model predicts spaces instead of completing the row correctly.

Solutions

Solution 1: Modify the System Prompt

To achieve better table generation results, modify the system prompt as follows:

“For tables, please use the basic GFM table syntax and do NOT include any extra whitespace or tabs for alignment.”

This instruction helps prevent excessive spaces, ensuring the Markdown syntax is respected.

Solution 2: Adjust the Temperature

Explicitly setting the temperature to 1 resolves the issue.

• Gemini 1.5 Temperature Range: 0-1
• Gemini 2.0 Temperature Range: 0-2

Since the bug occurs solely at temperature = 0, a setting of 1 helps the model avoid getting stuck in a whitespace loop.

For those needing a temperature setting below 1, anticipate occasional failures until fine-tuning options become available.

Conclusion

• Issue: Markdown tables fail due to token prediction errors at temperature = 0.
• Solution 1: Add system instructions to mitigate whitespace errors.
• Solution 2: Use temperature = 1 to prevent token loops.
• Workaround: Utilize Gemini 2.0 Pro for a more stable output, as it reportedly handles this issue more effectively.

Unless Google retrains the model, these adjustments provide a practical workaround to produce Markdown tables reliably using Gemini 2.0 Flash. 🚀