Should Conversational AI Rely on Large Language Models?

Would AI perform better with a “more is better” mentality? Natural language processing (NLP) accuracy relies on countless bytes of data to reach rudimentary conversational levels. From grammar to semantics, there’s more nuance to AI conversational models than simply downloading dictionaries into data sets.

Large language models (LLMs) are conversational AI chatbots taking the world by storm, like ChatGPT. Are products like ChatGPT the role model for the future of conversational AI — which relies on LLMs — or is their popularity providing insight into what humans could do better?

What Is an Example of Conversational AI?

BERT and ChatGPT are the world’s most well-known conversational AIs. LLMs pull information from data input like books and ever-updating sources like social media or websites. In conjunction with advanced NLP, it attempts to construct responses syntactically and tonally, crafting sentences with as much accurate data as possible while reading like a human.

Chatbot designers have begun questioning if previous conversational AI models that didn’t rely on LLMs should even make it into the next phase of chatbots. Why should humanity continue experimenting with antiquated designs when LLMs create surprisingly authentic and believable responses?

Humans must rely on LLMs to catalyze conversational AI growth. Their adaptability and scalability are incomparable to prior technologies, despite their shortcomings.

How Can Language Models Change?

LLMs aren’t perfect — they’re too early in development to be without most flaws. Hallucinations plague LLMs, providing users with inconsistent or outright inaccurate responses that sound convincing as much as 41% of the time. Why is this such an issue if these models are the peak of modern conversational AI?

Sounding like a human makes data gaps even more problematic because no data set can access every piece of knowledge. It may construct a sentence the LLM perceives as sensible because the information is correct in specific contexts. It can’t discern when it isn’t while trying to communicate in humanlike ways 100% of the time. The construction of the determination could be a jumble of data that sounds reassuring but has no backing.

Hallucinations could be a product of poor oversight and data curation. Concept drift, overfitting and underfitting are all issues that result in incorrect responses from even the most mature conversational AI. When the learning environment for the AI supports making connections to anomalies or irrelevant data that would prevent discerning new data, you could ask LLMs the same question twice and get two different answers.

How Do Large Language Models Influence Communication?

Never before LLMs did the layperson have access to such immense and powerful conversational AI. The advent of OpenAI was a necessary technological shift, as humans needed to play with the tool to increase its performance. Over 75% of consumers believe in AI’s ability to become more human, which shows how seriously people interact with the tools.

Because LLMs find patterns and relationships when analyzing language, it allows humans to understand how communications impact knowledge. If ChatGPT looked at websites to gather an answer, how could your phrasing influence the output? How do LLMs replicate humanity’s priorities with language and communication, especially in digital landscapes? How conversational AI talks to people requires everyone — from computer scientists to students — to reflect on how the world speaks in person and online.

More user contributions equal more information it can use to expand its capabilities, also known as human-in-the-loop processing. People help identify outdated information and improve delivery. Companies and individuals not using LLMs previously can now experiment with how they can simplify lives and streamline operations. In essence, these resources were free advertisements for AI expansion and adoption.

Honing AI Language Models

The future of conversational AI may rely on LLMs as a stepping stone or reveal the next stage in development. Despite accuracy and controversy, it has already landed a significant cultural impact worldwide, enlightening everyone with access to the future. Perhaps AI shouldn’t rely on LLMs forever, but it’s inarguable that it’s needed now to keep the momentum for positive progress.