Structured data is currently considered the cornerstone of the data industry. This type of data forms the backbone of various applications due to its accessibility and reliability, facilitating efficient information organization.
With the emergence of Generative AI (GenAI), Large Language Models (LLMs) have revolutionized how we interact with data by excelling at processing and generating human like texts. From the beginning, unstructured data was the main focus of this technology due to the abundance and accessibility of such data. Through the exclusive focus on unstructured data and the lack of other data types, LLMs often struggle when dealing with structured data. This article begins with outlining the main challenges and how to tackle them using state-of-the-art methods in the domain of GenAI.
Limitations of Large Language Models (LLMs) with Structured Data
LLMs are mainly trained on unstructured data, which explains why they struggle to interpret structured data formats like tables, databases, or even spreadsheets. In other words, LLMs find huge difficulties in grasping the relationships and constraints of the data, which is critical for accurately interpreting its meaning. The ability to recognize this is known as “Schema Understanding”.
Want to dive deeper into the differences between structured and unstructured data? Check out our previous article, “Structured vs. Unstructured Data: Challenges and Opportunities for Language Models”, to gain more insights and understand the broader context of these challenges.
In addition to the lack of Schema Understanding, LLMs require meticulous interpretation to guarantee the required accuracy. In fact, without this precise interpretation, LLMs will generate incorrect or nonsensical responses, due to the misinterpretation of the numerical values, dates, and categorical data.
Furthermore, utilizing structured data can drive LLMs to hallucinate. Although hallucination is a well-known concept in GenAI, the occurrence of such a problem is particularly common with structured data. LLMs can generate plausible responses, despite the fact that they were generated from incorrect conclusions, due to the generalized patterns recognized from large bodies of text. One effective solution for hallucination is the Retrieval Augmented Generation (RAG) technology which provides context awareness to the queries.
You can learn more about RAG systems by downloading our one-pager “AI-Powered Chatbot”
Addressing the Limitations with New Approaches
The most simple solution is to convert the structured data into Natural Language Prompts. This can be done manually but, we can leverage the capabilities of GenAI to understand and generate these texts. To obtain better results, it is also possible to combine the strength of GenAI with prompt engineering techniques to craft prompts that carefully guide the usage and interpretation of structured data by LLMs.
Interested in mastering prompt engineering techniques? Join our specialized workshop to learn how to effectively craft prompts and maximize the potential of LLMs.
Other techniques can also be used to enhance the performance of LLMs with structured data. One technique worth mentioning is the serialization of structured data as inputs. In other words, we will convert the data into a format that LLMs can interpret and use text. Imagine you have a table containing records of employees with columns like “Name,” “Position,” “Salary,” and “Date of Joining”. The serialization of the data will convert the table into the following text “Employee records: Name: John Doe, Position: Manager, Salary: $90,000, Date of Joining: Jan 10, 2020; Name: Jane Smith, Position: Engineer, Salary: $80,000, Date of Joining: Feb 15, 2019.”, which makes it easier for the model to parse and understand. A more complex solution is the integration of a Knowledge Graph (KG), which consists of defining the relationships between the entities of the structured data. Recent studies have shown that the integration of KGs with LLMs improves the understanding of the context, with accuracy increasing to 54% when questions are posed over a Knowledge Graph representation, leading to better output responses.
Conclusion
Although LLMs were primarily thought to work with unstructured data, recent developments have introduced well-designed approaches and methods to improve the usage of structured data with LLMs. Furthermore, integrating structured data and combining it with unstructured data enhances the accuracy and consistency of the responses in addition to improving the reliability and trust of AI systems, especially in the professional field.
