In recent years, Large Language Models (LLMs) have made tremendous progress in language understanding, with companies leveraging their capabilities to access an unprecedented amount of knowledge.
These models, such as OpenAI's GPT-4, have the ability to generate human-like text and can be trained on massive amounts of data to create a knowledge base. This development has opened up new possibilities for organizations to customize their own language models, which ae being sought out for their short-term benefits.
In the short-term, companies will be able to use these models to create their own language models on proprietary documents and databases. This means they can use their own data to create models that are specific to their business needs.
This will enable them to access and understand large amounts of data much faster than ever before, without the need for human intervention.
Usage and limits of the conversational experience
Following recent trends, while adding a chatbot to query a company’s pesonal knowledge base might be interesting, it does have some very significant drawbacks.
One of the main issues with chatbots is the lack of reliability in the information generated by the AI. This is because chatbots use a statistical approach to generate responses, which can result in incorrect information being provided.
Additionally, there is no way to verify the accuracy of the information generated by the AI, making it difficult to trust the information provided.
Another issue with chatbots is that they lack explainability. In other words, there is no way to understand how the AI arrived at a particular conclusion or answer.
This makes it difficult for businesses to understand how the AI is making decisions and eventually troubleshoot when problems arise.
For analysts, it's important to have a deep understanding of the source of any information and its legitimacy before making any decision based on that information.
Chatbots may not have the ability to evaluate the credibility and reliability of the sources they are using, which can lead to inaccurate or biased information being presented.
Chatbots may not be able to handle complex queries or scenarios that require human judgement and expertise. Analysts are often required to make decisions based on a combination of structured and unstructured data, as well as their own knowledge and experience.
Chatbots may not have the ability to integrate this level of complexity into their responses.
Data privacy is a critical concern when analysts send sensitive information to foreign cloud providers. When analysts send critical information such as financial or strategic data to external servers, they may be putting that information at risk of being accessed or compromised by unauthorized individuals or organizations.
The specific case of analysts
Despite these issues, LLM have become popular for white-collar workers looking to augment their productivity, to generate content more easily, or access any source of information.
However, there is a particular group of professionals who would not benefit from this type of user experience – analysts.
Analysts need to add a layer of expertise and decision-making based on top of many points of structured knowledge. Conversational interfaces are not designed to help them grasp this information and properly perform their job.
Analysts require structured data, such as tables, charts, and graphs, to visualize and analyze information effectively. Chatbots cannot provide this level of detail, nor can they provide the analysis required to make informed decisions.
While LLMs provide organizations with access to an enormous amount of knowledge, the current use of chatbots to query this knowledge base has some significant limitations. Businesses must find ways to verify the accuracy of information generated by the AI and ensure that it is explainable.
Furthermore, chatbots are not suitable for professionals such as analysts, who require structured data to analyze information effectively. It is essential to consider these limitations and work towards developing better interfaces that provide value to all users.
Want to learn how to build a private ChatGPT using open-source technology?
The Rise of Knowledge Graphs is Empowering Analysts
To address the limitations of chatbots and provide analysts with a better user experience, Knowledge Graphs have emerged as a powerful solution.
Knowledge Graphs are data structures that organize knowledge in a way that can be easily understood and queried by humans and machines alike. They can represent entities, concepts, and their relationships, and provide a clear and structured view of data.
Here are some ways that analysts in any industry (financial, medical, intelligence, etc.) can leverage Knowledge Graphs to make decisions on large amounts of text data —
Entity extraction and linking: Knowledge Graphs can extract entities from text — such as people, organizations, locations, and events — and link them together based on their relationships. - This can help analysts identify patterns, connections, and trends that may not be obvious from individual pieces of text.
Entity clustering and classification: Knowledge Graphs can cluster and classify entities based on their attributes, such as sentiment, location, or relevance to a specific topic. - This can help analysts quickly identify relevant information and filter out noise.
Topic modeling and analysis: Knowledge Graphs can use natural language processing (NLP) techniques to identify topics and themes within text data, and map them to specific entities and concepts. - This can help analysts understand the context and significance of information, and identify emerging trends and issues.
Network analysis and visualization: Knowledge Graphs can provide a visual representation of the relationships between entities, concepts, and themes, allowing analysts to identify clusters, subgroups, and key players. - This can help analysts understand the structure and dynamics of complex information, and identify potential threats and opportunities.
Predictive analytics and forecasting: Knowledge Graphs can use machine learning algorithms to predict future trends and events based on historical data and current information. - This can help analysts anticipate potential risks and opportunities, and take proactive measures to mitigate or exploit them.
By leveraging Knowledge Graphs, analysts can access, query, and validate point of information to make the smartest decisions possible. This makes it easier to identify patterns, trends, and insights.
Additionally, they can enable us to understand the relationships between data points, which can help them make better decisions based on the context of the data.
Meet Lettria: A no-code platform to create Knowledge Graphs from text
Lettria is a no-code platform that turns any textual document into an enriched Knowledge Graph with no effort. It leverages natural language processing (NLP) and machine learning (ML) to extract entities, concepts, and relationships from text and transform it into a structured and enriched Knowledge Graph.
Using Lettria, organizations can easily create and manage their own Knowledge Graphs, even if they have little or no technical expertise. The platform provides a user-friendly interface that allows users to upload documents, extract knowledge, and visualize the results in a clear and structured manner.
By hosting your own knowledge graph on your own servers with Lettria, you have complete control over your data and can ensure the privacy and security of your information. You can also easily integrate your knowledge graph with your existing data systems and tools, allowing you to leverage your data in new and powerful ways.