Artificial intelligence, GPT make NLP panic?

If you ask your everyday smartphone voice assistant if it's afraid of ChatGPT, you usually won't receive an interesting or informative answer. So is this a problem?

Natural language processing (NLP) is an area of ​​artificial intelligence research that has given rise to practical applications such as voice assistants and language translation, but now appears to be threatened by large language models (LLMs) such as OpenAI's GPT-4. A recent post on r/Machinelearning Reddit summed up the sentiment, asking if anyone else has “witnessed the panic within NLP organizations at big tech companies?”

Yangfeng Ji, assistant professor of computer science at the University of Virginia, has observed similar obsessions among academics and students, and recently tried to quell these fears by pointing out areas of NLP research for which LLM is inappropriate. "If not panic, then at least mixed emotions," Ji said.

Ji believes in researchers' ability to adopt new methods as they emerge, but OpenAI's recent success with its LLM model has thrown a wrench into that effort. LLMs can accomplish many tasks, but the most successful LLMs do so behind closed doors. OpenAI did not detail the capabilities of GPT-4, OpenAI's most recent LLM model, which developers can only access through an API.

"Personally, what's even worse is that we don't even know if it's just a language model," Ji said. He noted that LLM-powered chatbots, such as ChatGPT, Bing Chat, and Google Bard, produce results that exceed those of LLM. They appear to update their capabilities over time and pull new data from the internet (usually despite the model's claims, when asked, that it lacks this capability). AI models often require training before they can act on new data. "But if it is a software system with LLM as a core component, then these problems can be easily solved," Ji said.

The opacity of LLMs from OpenAI and Google puts researchers in a difficult position. These models significantly outperform past NLP research on many tasks, but how they achieve this can only be guessed at. Ji describes this as a "mysterious performance gap" between closed-source and open-source models.

Despite this, Ji still sees a lot of NLP research space beyond the capabilities of LLM. He noted how LLMs continue to struggle with ethical issues that make them unsuitable for certain organizations. They are also difficult to fine-tune and can produce unexpected results. These issues are unlikely to cause harm when used to brainstorm cake recipes or write emails to friends, but "when people start taking these systems seriously and doing real work with them, they become major obstacles."

The rapid rise of LLM is not just academic. Apple, Microsoft and Amazon have invested billions of dollars in their respective voice assistants, each promising smart, voice-activated assistants that will grow into helpful companions. The effort went unrewarded. Amazon's recent rounds of layoffs include significant cuts to its Alexa team, which reportedly lost $10 billion in 2022. Microsoft CEO Satya Nadella recently called voice assistants "dumb as a rock" and Cortana was all but abandoned. The Google Assistant team is reportedly reorganizing to assist Bud. Only Apple's Siri has endured, although improvements have slowed to a trickle in recent years.

Just as researchers were caught off guard by the power of LLM, technology companies were unprepared for its widespread use. Chatbots powered by LLM, such as ChatGPT, Google Bard, and Bing Chat, can accomplish tasks that voice assistants cannot (such as writing emails from scratch) and do so using language that is more realistic and engaging than preset voices. Assistant provided.

Noah Gift, founder of Pragmatic AI Labs, sees this as a fundamental shift. “For years, the focus of data science has been on tuning hyperparameters, cleaning data, and focusing primarily on research and technology versus business value, as evidenced by sites like Stack Overflow,” Gift said. “In a recent book I wrote, Practical MLOps, I predicted that there would be less and less data science and more and more models built by large organizations, which is happening to a large extent. If you’re working in a company doing NLP "

But don't carve Siri's tombstone just yet. NLP research remains important, even as strategies for implementing it continue to evolve.

Microsoft's rapid shift to artificial intelligence is one example. The company's collaboration with OpenAI has resulted in several GPT-powered product releases, including Github Copilot, Bing Chat, and Microsoft 365 Copilot. Microsoft hasn't announced a new voice assistant yet, but third-party developers have introduced browser plug-ins that shoehorn the functionality into ChatGPT. OpenAI’s official ChatGPT plug-in has been released in limited release, likely opening the floodgates for custom voice assistants — and more.

"I don't believe voice is a dead end at all, and in fact it will improve significantly as new LLMs come into consumer products," Gift said. "The key issue with voice initially may be that these projects are simply inferior to the technology used by OpenAI and other emerging LLM technology providers. I see the use of text and speech LLM creating a huge market for their technology."