The pros and cons of AI language models

What do you think of the OpenAI ChatGPT app and AI language models? There’s lots going on: GPT-3, GPT-4, Google Bard, Bert, DeepMind, PaLM; and let’s not forget Meta Llama.

Yay or nay? Amazing artificial intelligence technology or spooky world-order threat?

Maybe a little of both. But whatever your views on the proliferation of AI applications, the large language models, or LLMs, that are powering OpenAI’s ChatGPT and similar tools are unquestionably out of the bottle.

These artificial neural networks have gotten the world’s undivided attention for their uncanny ability to process and generate commonsense, coherent text. Built on deep-learning algorithms, these AI systems — the larger of which Stanford University has dubbed foundation models — give life to a multitude of modern applications, including chatbots (conversational AI), content generation, and language translation.

With a recent survey finding that 66% of technology industry leaders believe large-language-model AI will have a positive effect on their companies’ financial performance, now’s a good time to learn more about how this technology could impact your business.

The caveat is that while AI language models offer huge potential for general-purpose optimization in a variety of domain-specific use cases, along with the sunny projections, there are notable drawbacks to the ways large language models work their machine-learning-driven magic.

For LLMs and regular-sized models alike, here’s a look at the good, the bad, and the ughy.

The pros of AI language models

The benefits of rolling out large-scale language models include:

Deeper levels of comprehension

AI language models utilize natural language processing — NLP tasks — and natural language understanding (NLU) to excel in comprehending and interpreting human language, enabling more-intuitive interactions between humans and machines. Unlike earlier chatbots and automated systems that relied on rigid scripts and keyword matching, these models can better “understand” the context, sentiment, and intent. And this semantic understanding goes a long way to empower customer-support chatbots, virtual assistants, and search engines to do their best work.

For example:

  • In ecommerce: when an online shopper has a question about an item for Siri or Google Assistant, an AI language model can dissect the query, looking at the context and providing a relevant, accurate response
  • In healthcare: an LLM can amass data that lets doctors make more accurate diagnoses of conditions such as cancer

Saved time

AI language models are capable of producing most everything text related, from quick suggestions and recommendations to charming poetry to lengthy essays. As a result, marketers and salespeople, journalists, and even employees who aren’t directly tasked with writing (not to mention students) are using AI language models to streamline work processes and create professional content.

To streamline product marketing, for example, AI language models can instantly generate outlines and draft material for blog posts. The human time and effort saved can then be channeled into personalizing and editing the content to reflect the brand voice.

AI language models are also adept at summarizing lengthy documents such as reports. This makes them indispensable tools for market researchers, as they cut the time needed to read hundreds of pages to identify key points and insights.

Enhanced efficiency and accuracy

Traditional methods of text processing and analysis can be tedious and prone to errors, especially when dealing with massive datasets. By contrast, with their deep-learning algorithms, AI language models can work through and analyze datasets at unprecedented speeds, reducing or eliminating a ton of manual work.

Take the scenario of a business needing to read thousands of customer reviews to identify common complaints, issues, and areas where they’re doing well. Manually eyeballing this trove of data is likely to be time-consuming and exhausting, and any conclusions drawn may be compromised by human biases.

By contrast, large language models can check out and analyze gargantuan amounts of textual data, then extract highly accurate insights and trends, and all in an instant. This translates to businesses being able to gather data and respond to customers in a timely fashion, not to mention being able to avoid subjecting employees to daunting projects, and, of course, probably needing fewer people on staff.

Elimination of language barriers

Wouldn’t it be nice if there were no language barriers? Thanks to AI, we’re not far off: multilingual AI language models provide real-time translation services, facilitating communication and collaboration across linguistic backgrounds. For example, let’s say you manage a big ecommerce platform and need to conduct a virtual meeting among employees who speak considerably more languages than just English. Teamed with speech recognition technology, AI language models can instantly convert participants’ comments, which likely improves both inclusivity and efficiency.

More-personalized online experiences

AI language models have ushered in a computer-science era that transforms the online experience of everything from selecting a movie to download to conducting a search for a restaurant. By collecting data and analyzing user behavior, preferences, and historical details, these models can offer personally tailored product recommendations and experiences.

In ecommerce, for example, AI language models power product recommendation engines that suggest potentially enticing items to shoppers based on their browsing and purchasing history. Aligning recommendations with each person’s preferences increases the likelihood of conversion and customer satisfaction.

These are a few of the benefits of state-of-the-art language models. Let’s switch now to disadvantages to balance out this discussion:

The cons of AI language models

It’s safe to say that the rise of AI language models has radically shaken up the way people interact with technology, and in a good way. However, it’s only fair to consider the drawbacks as well. These include:

Lack of contextual awareness

Despite their eye-opening abilities, AI language models are beset by certain language-comprehension challenges. They aren’t human, obviously, nor are they necessarily well trained, which means they can make some pretty unfortunate mistakes. This is particularly problematic when it comes to nuanced and ambiguous language. Misinterpretation can then lead to inaccurate and misleading responses, negatively impacting the user experience and eroding trust in the information-provision process.

For instance, when someone asks their virtual voice assistant a question that involves multiple layers of context, an AI language model may misinterpret their intent or provide an oversimplified answer. This lack of contextual awareness and first-rate functionality can hinder the ability of AI language models to serve as truly reliable sources of information and assistance.

One glimmer of good news: AI researchers are focused on how contextual understanding can be improved through application of better natural language processing techniques and training methods.

Potentially damaging bias

AI language models are trained on vast datasets, often comprising text from the Internet and books, among other sources. Such datasets can contain inherent biases, which can thereby lead language models to perpetuate gender and racial stereotypes, which can invariably lead to discrimination. If an AI language model is trained on a dataset that’s home to sexist material, for example, it may generate sexist responses.

How rampant is this problem? Researchers have discovered bias in up to 38.6% of “facts” generated by AI. That’s understandably concerning, all the more so in areas such as news and education, where reputations rely on providing the absolute truth. Language models spreading bias that can perpetuate outdated beliefs and unfairness is a thorny reality. While researchers and developers are working on strategies to reduce bias during language-model pretraining and provide clear guidelines for fine-tuning models, the nature of the ways language models work means it won’t be easy to entirely root out this problem.

Harm caused by misinformation

AI language models have shown susceptibility to malicious manipulation, such as with generation of fake and misleading content. For example, people can use AI language models to generate fake news (including photos), reviews, and social media posts. In this AI day and age, it’s increasingly difficult to discern genuine information from fraudulent content. Plus, with such easy dissemination of information, such as on social media, the consequences can include swaying everything from public opinion to influencing election outcomes.

The possibility of rapid generation of fake and potentially harmful content also poses challenges for people who manage content moderation on online platforms. Efforts are being made to develop AI-based content-filtering systems, but harmful content may still bypass detection. There’s no easy fix for this situation, either.

Less-secure data

For getting up to speed, AI language models rely on the intake of voluminous amounts of training data, including user-generated content. While this data, along with information used to improve existing AI models, is essential for effective functioning, it raises significant concerns regarding data privacy and security.

One primary concern is the potential for bad actors to gain access to people’s sensitive data. AI language models can inadvertently be exploited. Some user data has already been exposed or mishandled. In response, governments and organizations are adopting stricter data protection regulations and privacy standards. So far, these include the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA).

Lack of transparency

As documented in publications like New Scientist, it can be difficult or impossible to know how and why a language model processes data, makes decisions related to the processing, and comes to its conclusions. To summarize, the possibility that AI could do whatever it wants in an opaque system, without explaining its methods, without human feedback, and with people having no way of following its logic or making sense of its conclusions, is troubling.

Humans losing their jobs

It’s the question on everyone’s minds: “Is AI coming for my job?” In some cases, that’s a definitive yes: for instance, in May 2023, 3,900 job losses in the US were attributed to AI.

Jobs that entail routine and repetitive language-based specific tasks, such as data entry, content generation, and customer support, are understandably susceptible to automation through AI language models. However, this is not just a low-skill-level phenomenon. Job displacement extends to roles that require higher-level language skills. For example, AI language models being employed to help with legal research and document review may reduce the demand for lawyers and paralegals.

A less creative work world

Job losses are slated to be a relatively small part of significantly larger losses. As Charlie Warzel predicts in The Atlantic:

The optimistic argument for these types of productivity tools is always that they unlock human potential and creativity…. But…. Creativity is an inefficient, nonlinear process. The joy and the magic are in the friction. Productivity is, in many ways, its opposite. And AI is, above all else, a fully realized productivity tool with a mandate to eliminate friction wherever possible.

That’s it for the minuses, which can certainly make any thinking person feel unsettled about AI. We can only hope that as generative AI continues to evolve, ongoing research, collaboration, and regulation will play outsized roles in maximizing the benefits and mitigating the risks.

Pros, cons, realities

Shall we get back to the upsides?

If you have an online business, enabling more-intuitive, personalized experiences for your shoppers or subscribers is an enticing prospect. And your competitors are going to harness the latest tools, so those new systems are worth your consideration, too.

How can you jump aboard — perhaps cautiously — the AI-language-model bandwagon?

One idea: boost your search experience effectiveness with the fundamentals of AI language models — natural language processing and natural language understanding — without the downsides, built into Algolia’s cutting-edge AI-powered search API.Whether you have a startup or an established company, we’re betting you can benefit from the power of AI language models. Ping us for details or a demo; let’s get the pros of these models working in your favor!

About the authorCatherine Dee

Catherine Dee

Search and Discovery writer

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