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Today, artificial intelligence and machine learning have made rapid inroads into mainstream consumer technology across all sectors. From self-driving cars to investment advisory services, we are discovering new use cases almost daily. But over the past couple of months, the loudest voice and perhaps the biggest mainstream AI breakthroughs have been centered around large language model research. In simple terms, the use of natural language-enabled AI systems is bringing machines and humans closer as interactions between the two become more natural.

The Prominence of Large Language Model Research

ChatGPT is the latest culmination of developments in the field of large language model research. In January 2023, nearly 100 million people were using ChatGPT for various tasks. From students to marketers, the ability of such large language model (LLM) platforms to discover and represent data in fine-tuned conversational language has become a major source of content generation. Google is fast catching up with a similar platform called Google Bard. And there are more – Amazon has Bedrock and Titan, Meta’s LLaMA.

Whether it’s ChatGPT or any other LLM, the underlying concepts of large language model research are making headlines across industries. This is being dubbed the next big thing after the internet and could very well transform entire industries like customer support, education, healthcare, and many more.

Large language model research is not a new phenomenon. It has been around for a while and has powered some of the most exciting AI breakthroughs we have seen in the past couple of years.

Let us explore the key areas that demonstrate massive potential by the accelerated adoption of large language model research.

Sentiment Analysis

Plain English text can often be confusing from a sentimental perspective. It will be incredibly hard for traditional language models to gauge whether a given piece of content has a negative or positive sentiment tied to it. Large language models, however, are equipped with enough knowledge to read between the lines and understand the true sentiment behind a given text or statement. This allows enterprises to connect with the actual emotional sentiment that their target customers are experiencing on a deeper level by analyzing their textual feedback. This, in turn, allows them to identify problem patterns, gauge brand sentiments, and determine the most suitable responses to public interactions on digital channels like social media. Brands getting incredibly good at real-time reactions to social media posts or messages are benefiting from this development in large language model research focused on sentiment analysis.

Chat Conversations

Today, nearly every consumer business in almost all domains has automated chat support services available on their website, app, and official social media pages. While these conversations via chat support were handled by human agents in the past, the situation has changed drastically.

Today, AI-powered conversational chatbots have taken the customer support industry by storm. In addition to being available 24/7 and having the ability to provide real human-like responses with large language models, chatbots also enable staff to focus on other critical business tasks. Studies have previously shown how nearly 64% of employees have agreed to have more time to focus on key job tasks thanks to AI chatbots handling their load of customer support.

Personal Assistants

As part of the chat interface, LLMs can be used for understanding the intents of the users and executing actual tasks based on the responses. This is enabling enterprises to offer personal assistant-like services to their customers where the customer can interact with the chatbot to avail services, e.g., placing a trade, transferring money, booking a seat at a restaurant —the possibilities are endless. This is transformative because the user doesn’t need to understand the complex nuances and workflows of software. They can continue providing natural instructions to digital personal assistants.

Translations

Today, language is not as significant a barrier as it used to be. People travel to different countries or seek information from websites created in different languages. Nearly everyone uses a translation app that converts foreign languages into their desired language. One of the most popular choices in this category, the Google Translate App, has been installed on over 1 billion Android devices, according to its Google Play Store page.

Translation services are yet another major use case for large language model research. The ability of translation apps to recognize even minor nuances or variations in languages that have no linkage to traditional English alphabets is quite stunning. This has been made possible thanks to years of investments and the training of large language models.

Text Summarization

The most advanced version of text summarization is what you see today with generative AI solutions like ChatGPT. The AI system swiftly processes millions of data records and summarizes the exact insight needed for end users. Using large language model research, it can understand the exact information from a stockpile of documents and web content spread across the internet and extract a rich summary from it seamlessly. This saves time and effort considerably.

The instruction handling ability of LLMs allows us to summarize a single document in multiple ways for different purposes, e.g., a research report on generative AI can be summarized for the CEO, addressing the market potential and opportunities, while the same report can be summarized for a technical architect, including the important technical aspects mentioned in the report.

The interesting part is that every piece of feedback generated for the summarization is used by the system to learn more about how to make the next iteration of the summary even better than its predecessor.

Log Analytics

Several software applications generate very long log files containing tons of software activity information. The failures are captured in these log files as message traces, and the developers must go through the log files to understand the issue, its root cause, its impact, and its solution. LLMs can be used to monitor the log files and capture the anomalies or failures recorded in these log files. LLMs can also generate reports for developers with a clear summary of the issue, root cause, solution, etc.

Data Analytics

Enterprise software has large databases and dashboards built on top of them. They provide business users with a high-level understanding of the business. However, business users may sometimes need more information that is not readily available on the dashboard. In such cases, ChatGPT-based solutions can peruse the database to query and fetch the information for them.

Autonomous Code Generation

Large language model research is not just meant to enable textual intelligence but also handles highly technical topics like coding. Developers can use AI tools to generate code for specific application features or functions. This is the next level of low-code and no-code technologies that are already transforming the coding community.

Large language model research allows AI systems to recognize prompts from developers and deliver a reliable and working piece of code that can be put to the test and subsequently deployed.

The Bottom Line

Large language model research is pioneering new innovations at a very rapid pace. However, enterprises need to be aware that the efficiency of such AI models is dependent on how well they're supplied with the right volume of accurate data from multiple business systems. This is why having a data-driven operational strategy across the business is critical.

Once enough data is generated and managed for accuracy, it becomes easier for AI systems to learn and deliver superior experiences for business. Setting up an efficient data management strategy with the right tools and expertise requires the guidance of an experienced technology partner like Xoriant.

Check out our related PDF: Big Data Analytics Services

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