TAG vs Text2SQL

To understand how TAG (Table-Augmented Generation) and Text2SQL differ internally in handling complex queries, let’s break down the steps each system follows. TAG goes beyond what Text2SQL does by integrating reasoning and language model capabilities to handle more sophisticated queries. Here’s a detailed step-by-step comparison for a complex query.

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Example Complex Query:

“Summarize the reviews of the highest-grossing romance movie from last year.”

This query requires:

• Aggregating data to find the highest-grossing movie.
• Filtering based on genre (“romance”) and time (“last year”).
• Summarizing unstructured text (reviews).
• Reasoning about time periods (“last year”).

Text2SQL Internal Process

Natural Language Parsing:

Text2SQL uses a natural language processing (NLP) model (like a sequence-to-sequence model or a fine-tuned transformer) to parse the input query.

The system breaks down the query into key elements: “highest-grossing,” “romance movie,” and “last year.”

Schema Matching:

Text2SQL maps the elements from the natural language query to the schema.

It identifies that “romance movie” refers to a column (genre) in the movies table, "highest-grossing" refers to the revenue column, and "last year" refers to the release_date column.

SQL Query Generation:

The system generates the SQL query based on the parsed elements. The result will be a SQL query similar to:

SELECT movie_title, review
FROM movies
WHERE genre = 'romance' AND YEAR(release_date) = YEAR(CURDATE()) - 1
ORDER BY revenue DESC
LIMIT 1;

This query fetches the highest-grossing romance movie released last year and retrieves its title and review.

Query Execution:

The generated SQL query is executed on the database to retrieve the result. The output might look like:

movie_title: "The Great Romance"
review: "An emotional rollercoaster that captures the essence of love."

Output:

Text2SQL stops at this point. The result is simply returned as it is retrieved from the database. There’s no post-processing or additional reasoning.

Limitations:

It only retrieves data but doesn’t “summarize” the reviews or handle reasoning tasks like understanding “last year” dynamically.

Text2SQL also doesn’t handle unstructured data processing, such as summarizing reviews.

TAG (Table-Augmented Generation) Internal Process

Natural Language Parsing (Query Synthesis):

TAG first parses the query similarly to Text2SQL, breaking it into key parts: “highest-grossing,” “romance movie,” “last year,” and “summarize the reviews.”

TAG uses an NLP model to understand not only the structure of the query but also the intent (to summarize) and the reasoning (e.g., “last year” is a relative term).

Schema Mapping & Table Selection:

Like Text2SQL, TAG maps the query terms to database tables and columns using schema awareness. It identifies the movies table, genre column, revenue column, and release_date.

However, TAG may use word embeddings or a language model to reason about the query and generate additional logic (e.g., understanding that “summarize the reviews” involves working with unstructured text).

SQL Query Generation:

TAG generates the SQL query to retrieve the relevant structured data from the database. This query might be similar to the one in Text2SQL:

SELECT movie_title, review
FROM movies
WHERE genre = 'romance' AND YEAR(release_date) = YEAR(CURDATE()) - 1
ORDER BY revenue DESC
LIMIT 1;

AG retrieves the highest-grossing romance movie and its reviews, but unlike Text2SQL, this is just the first step.

Query Execution (Fetching Data):

The SQL query is executed, and the structured data (movie title and review) is fetched from the database:

movie_title: "The Great Romance"
review: "An emotional rollercoaster that captures the essence of love. The chemistry between the leads was electric, and the story was captivating."

Answer Generation (LM Post-Processing):

• Now, the TAG system uses a language model (LM) (e.g., GPT) to process the unstructured data (reviews).
• TAG uses the LM to summarize the fetched reviews, as requested by the query. This might involve multiple passes or an iterative process where the model condenses the information in the review field:

Summary: "The reviews highlight the chemistry between the leads and praise the emotional depth of the story."

Reasoning (Handling “Last Year”):

TAG is capable of understanding that “last year” is a dynamic term. While Text2SQL might rely on SQL date functions (e.g., YEAR(CURDATE()) - 1), TAG can handle more complex reasoning about time periods, particularly if the logic is not directly expressible in SQL.

For example, the model can interpret “last year” even if it’s not explicitly stored in the database.

Final Answer Generation:

The TAG system combines both the structured data (e.g., the movie title) and the reasoning/summarization from the language model to form the final natural language response:

The highest-grossing romance movie from last year is "The Great Romance." The reviews praise the chemistry between the leads and the emotional depth of the story.

Technical Components in TAG:

1. Language Models (LM): For summarization, reasoning, and handling unstructured data. LMs like GPT-4 are used to process the results retrieved from the database.
2. Schema-Aware SQL Generation: Like Text2SQL, TAG uses schema-aware NLP models to generate SQL queries, but it augments this with reasoning capabilities.
3. Post-Processing Pipelines: TAG integrates SQL results into post-processing pipelines that use LMs to generate more refined answers.

Final Verdict

Conclusion:

• Text2SQL stops at generating SQL queries and retrieving data, handling simple, structured questions.
• TAG goes further by incorporating a language model to process unstructured data, handle reasoning tasks, and generate complex, human-like responses based on both structured data from the database and external reasoning. This makes TAG more suited to real-world, complex queries.