Have you wondered how Conversational AI tools effectively understand and process human language? How can they know the context of any topic and respond so well? How can they identify sentiment so accurately that it makes people believe they are sentient? One of the main reasons for that is Knowledge-Intensive (KI) in Natural Language Processing (NLP). KI allows us to ask Conversational AI tools a wide range of questions and get factual responses. Yes, AI doesn’t have 100% accuracy yet, but look at all it has accomplished. AI tools that use NLP are closer and closer to fully accurate responses in fairly complex topics. Let’s dive deeper into one of NLP’s main pillars, Knowledge-Intensive.
Knowledge-Intensive and Natural Language Processing
The questions Conversational AI and Generative AI can answer range from Quantum Physics to coding and even how to make an espresso. AI models require access to a vast ocean of data to do such impressive things. They also use sophisticated algorithms that leverage that data to solve difficult problems. Natural Language Processing (NLP) is a field of AI responsible for machines' understanding, answering questions, and solving problems in human language. Thanks to NLP, machines can also translate languages and recognize speech. That said, KI is a field in NLP that uses extensive knowledge sources to enhance processes. KI allows for refined solutions, enabling access to a massive archive of information.
Think of KI as why AI tools can respond to questions and solve problems without searching the web for answers. Some of the structured knowledge sources KI uses are in the form of ontologies and graphs. KI aims to address some of the major challenges of AI models, including ambiguity resolution. KI approaches this issue by improving inference and reasoning using logical frameworks. Besides, KI helps ensure the ethical use of the data and updates the information for better answers. If KI is still unclear, imagine it like an encyclopedia NLP uses to teach AI tools to speak in human language.
What Tasks Does Knowledge-Intensity Solve?
1. Fact-Checking
KI implements a robust approach to validate the information it uses to answer user questions. It analyzes text from massive datasets to identify and extract specific statements and claims that need verification. KI is so powerful that it can even evaluate bias, source reputation, and reliability and compare multiple sources of information. Needless to say, fact-checking is one of the most important aspects of AI models, and we should thank KI for that.
2. ODQA
Open Domain Question Answering (ODQA) is one of the main reasons AI models can answer a wide range of questions effectively. ODQA aims to use an extensive knowledge of information to answer complex questions even when there isn’t an explicit response. One popular way of implementing ODQA is by using the retriever-reader approach. In essence, a retriever narrows down the search by selecting relevant documents that are likely to have the answer to the user’s question. The retriever uses powerful techniques such as keyword matching, document retrieval, and dense vectors. Then, the reader digs deeper into the retrieved documents and generates a response.
3. Entity Linking
KI NLP is responsible for identifying entities in textual data and linking them to relevant sources. Consider entities as meaningful key information elements, including people, items, organizations, locations, etc. The goal is to eliminate ambiguous mentions by connecting them to a trustworthy knowledge base.
4. Text Classification
Text classification is a Supervised Learning technique used to understand massive unstructured data collections. The data used to train AI models has labels or categories that allow the model to learn the relationship between the text input and the desired output. Labels and categories help the model learn how to recognize patterns and make accurate predictions. Text classification involves labeling topics such as science, politics, sports, etc. Text classification also has to do with identifying the polarity of the text, including positive, negative, and neutral. It’s worth mentioning that text classification also plays a pivotal role in understanding the user query’s purpose.
Retrieval-Augmented Generation For KI NLP
As mentioned above, AI, or more specifically, Large Language Models (LLMs), still make mistakes sometimes. A cutting-edge framework to enhance the accuracy of KI NLP is Retrieval Augmented Generation (RAG). RAG aims to address two major problems users have when interacting with LLMs. Not having a reputable source to validate the answer to the prompt or having outdated information. RAG forces the LLM to consult reputable sources of information quickly to validate its response to the user.
That includes answers to questions the LLM may already know. Thus, if there have been recent discoveries related to the question, the LLM can combine them with its existing knowledge before giving an output that may be outdated. The goal is to provide users with the most updated, best-possible answer. RAG, backed up by Meta (Facebook), is one of the most modern approaches to get the most out of LLMs. RAG leverages KI NLP to help avoid the annoying “hallucinations” some AI models still make. By using external knowledge in the training process, KI NLP improves in all the four tasks mentioned above. That’s why RAG is so powerful.
Final Thoughts
We’re rapidly moving to a future where AI models can solve more and more complex problems for us. Their responses get surprisingly closer daily to the exact output users expect. While their journey to perfection is far from over, they’ve helped us reach stellar productivity. The effectiveness of AI models largely depends on the data used to train them. That’s why KI is a major factor behind the success of Conversational AI. Since the data used to train AI models is such a major factor, we aim to combine it with the complexities of your business to give you more relevant answers. Considering the individual aspects of your company allows Funes to increase the Collective Intelligence of your team, leading to potential opportunities for growth.
