ChatGPT, a huge language model developed by OpenAI, has revolutionized the area of natural language generation by its ability to generate human-like text. However, like any machine learning model, it has its limitations.
One of the limitations of ChatGPT is its lack of understanding of the context and background knowledge of the text it generates.
For example, if asked to write about a specific topic, it may generate text that is grammatically correct but lacks the depth and nuance of an expert in that field.
Another limitation is its inability to reason and make logical connections between different concepts. While it can generate text that flows logically, it lacks the ability to make inferences and draw conclusions based on the information it has been provided.
One way to overcome these limitations is by training the ChatGPT language model with data from Knowledge Graphs.
A knowledge graph is a type of database that stores and organizes information in a way that reflects the relationships between different pieces of data. This allows for more accurate and intuitive representation of real-world concepts and their connections, as compared to traditional relational databases and allows easy access and querying.
By incorporating knowledge graph data into the OpenAI language model, the model can access a wealth of background information and context, allowing it to generate text with a greater depth of understanding. In addition, knowledge graphs can also provide the language model with a way to reason and make logical connections between concepts.
By linking concepts together in a structured way the language model can use this information to make inferences and draw conclusions, adding a level of intelligence to its text generation.
Furthermore, knowledge graph can provide accurate information to the OpenAI model, which can then incorporate that information in its text generation. It can provide specific and accurate information about any subject, making the text generated more informative and useful.
In conclusion, while ChatGPT is a powerful language model, it has limitations in terms of context, background knowledge and reasoning. Combining it with data from knowledge graphs can help overcome these limitations, resulting in a more intelligent and informative text generation system.
A Conversational AI platform based on Knowledge Graphs – as the Onlim platform – offers several benefits when connecting the platform to ChatGPT language models
1. Greater depth of understanding
By incorporating knowledge graph data into ChatGPT, the model can access a wealth of background information and context, allowing it to generate text with a greater depth of understanding and context.
2. Improved reasoning and inference
Knowledge graphs provide a structured way of linking concepts together, allowing ChatGPT to make inferences and draw conclusions based on the information it has been provided, thus improving the reasoning capabilities of the model.
3. Increased accuracy
Knowledge graphs can provide accurate information about any specific subject, which ChatGPT can incorporate into its text generation, resulting in more informative and accurate output.
By using knowledge graph, chatbot providers can create personalized chatbot experiences for their customers, as the chatbot can use the user’s browsing history, interests, and demographic data to provide personalized recommendations and responses.
Knowledge Graphs can store vast amount of data, which allows chatbot providers to scale their chatbot to handle large numbers of users and queries. In contrast, chatbot providers who focus on natural language understanding may not provide the same level of depth, reasoning, and accuracy. Additionally, they may have limitations in terms of scalability and personalization.
We at Onlim have already connected the OpenAI language models with our platform and are currently implementing various use cases for our clients.
Don´t hesitate to contact us directly for a short conversation.
In the whitepaper “More Knowledge For Chatbots And Voice Assistants” you will learn how meaningful conversations between humans and machines are made possible in automated customer communication through so-called Knowledge Graphs.