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Most of us use NLP business applications every day without even knowing it. Spell-checkers, online search, translators, voice assistants—almost all of these include natural language processing technology. Here is a brief breakdown of various NLP tasks performed by modern NLP software.
Named Entity Recognition
Named entity recognition is the task that implies identification entities in a sentence (like a person, organization, date, location, time, etc.), and their classification into categories.
Part-of-Speech Tagging
Part-of-speech tagging is the task that involves marking up words in a sentence as nouns, verbs, adjectives, adverbs, and other descriptors.
Summarization
Summarization is the task that includes text shortening by identifying the important parts and creating a summary. There are two approaches to text summarization:
Sentiment Analysis
Sentiment analysis is the task that implies a broad range of subjective analysis to identify positive or negative feelings in a sentence, the sentiment of a customer review, judging mood via written text or voice analysis, and other similar tasks.
Text Classification
Text classification is the task that involves assigning tags/categories to text according to the content. Text classifiers can be used to structure, organize, and categorize any text.
Language Modeling
Language modeling is the NLP task that includes predicting the next word/character in a text/document. Language models might be used for:
Optical Character Recognition Machine Translation
Image Captioning
Text Summarization
Handwriting Recognition
Spelling Correction
A large language model is a type of deep learning model trained on massive data sets to help computers imitate human language. This leads to text generation, recognition, prediction, translation, and summarization capabilities. Deep learning models operate similarly to the human brain, processing information to become better at recognizing patterns and making predictions so that they can learn and think the same way we do.
What differentiates deep learning models from standard machine learning models is that deep learning uses far more data points, relies on less human intervention to learn, and has a more complex infrastructure that requires greater computational power. Because of this, large language models are costly and aren’t as widespread as other machine learning models.
With the power of artificial intelligence and deep learning, large language models can perform a wide range of tasks and support different types of applications, whether for internal use or to improve customer experiences. Take a look at ten ways businesses can utilize large language models now.
Chatbots and virtual assistants use large language models to provide quality service to customers. LLM chatbots are capable of providing assistance with troubleshooting and answering common questions. These chatbots can even analyze sentiment within the text to respond more effectively to customers and use predictive analytics to identify potential issues that the customer may be experiencing quickly.
A notable feature of large language models is their text-generation capabilities. After going through massive amounts of training data, LLMs can understand languages and context around the words, making developing written material possible and comparable to text written by humans.
Businesses can use large language models to sift through job applicant information and identify the candidates best suited for the job. Not only does this help with identifying quality candidates, but it also makes the entire process far more efficient. Using LLMs in the hiring process can also improve workplace diversity as it eliminates unconscious bias.
Along with the content creation capabilities of large language models, a specific circumstance where this is beneficial is for developing targeted marketing campaigns. LLMs make it possible for you to identify trends and better understand your target audience, leading to opportunities to develop more personalized advertisements and product recommendations.
You can use large language models to develop social media posts and come up with unique captions to go along with posts that include visual content. Large language models can analyze social media content to understand how to create material that people are more likely to engage with.
Large language models can understand the relationships between words in order to classify text that shares the same sentiment or meaning. By taking text and sorting it into predetermined categories, it's possible for you to organize information from different types of documents and more effectively utilize unstructured data.
Large language model translation capabilities help businesses expand their reach globally to new markets where potential customers speak another language. You can use LLMs to translate various materials, such as website content, marketing materials, product information, social media content, customer service resources, and even legal agreements.
Large language models are revolutionizing fraud detection, improving the efficiency of determining whether or not a transaction is potentially fraudulent, predicting customer transactions to block transactions deemed fraudulent, and assessing the risk level present. LLMs can quickly spot suspicious patterns and protect your business by analyzing huge amounts of data.
Large language models help contribute to supply chain management practices thanks to their analytics and predictive capabilities. With LLMs, you can gather insight to manage inventory, find vendors, and analyze the market to understand demand levels better.
During product development, large language models support several stages, beginning with the ideation phase and throughout the production process, identifying opportunities for automation and even contributing to decisions such as what production materials you should use. LLMs are also useful for performing testing and exploratory data analysis during the research stage of product development.
What is RAG?
RAG, or Retrieval Augmented Generation, is a method introduced by Meta AI researchers that combines an information retrieval component with a text generator model to address knowledge-intensive tasks. Its internal knowledge can be modified efficiently without needing to retrain the entire model.
What are the use cases for RAG?
There are many different use cases for RAG. The most common ones are:
Question and answer chatbots: Incorporating LLMs with chatbots allows them to automatically derive more accurate answers from company documents and knowledge bases. Chatbots are used to automate customer support and website lead follow-up to answer questions and resolve issues quickly.
Search augmentation: Incorporating LLMs with search engines that augment search results with LLM-generated answers can better answer informational queries and make it easier for users to find the information they need to do their jobs.
Knowledge engine — ask questions on your data (e.g., HR, compliance documents): Company data can be used as context for LLMs and allow employees to get answers to their questions easily, including HR questions related to benefits and policies and security and compliance questions.
What are the benefits of RAG?
The RAG approach has a number of key benefits, including:
Providing up-to-date and accurate responses: RAG ensures that the response of an LLM is not based solely on static, stale training data. Rather, the model uses up-to-date external data sources to provide responses.
Reducing inaccurate responses, or hallucinations: By grounding the LLM model's output on relevant, external knowledge, RAG attempts to mitigate the risk of responding with incorrect or fabricated information (also known as hallucinations). Outputs can include citations of original sources, allowing human verification.
Providing domain-specific, relevant responses: Using RAG, the LLM will be able to provide contextually relevant responses tailored to an organization's proprietary or domain-specific data.
Being efficient and cost-effective: Compared to other approaches to customizing LLMs with domain-specific data, RAG is simple and cost-effective. Organizations can deploy RAG without needing to customize the model. This is especially beneficial when models need to be updated frequently with new data.
AI consulting
ChatBot assistants
Computer vision
Financial scoring
NLP, LLM and RAG
Miltech
Prompt engineering
Scientific research
Sports betting (iGaming)
Tabular data and time series
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