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AI Chatbots Will Never Stop Hallucinating
Last summer a federal judge fined a New York City law firm $5,000 after a lawyer used the artificial intelligence tool ChatGPT to draft a brief for a personal injury case. The text was full of falsehoods—including more than six entirely fabricated past cases meant to establish precedent for the personal injury suit. Similar errors are rampant across AI-generated legal outputs, researchers at Stanford University and Yale University found in a recent preprint study of three popular large language models (LLMs). There's a term for when generative AI models produce responses that don't match reality: "hallucination."
Hallucination is usually framed as a technical problem with AI—one that hardworking developers will eventually solve. But many machine-learning experts don't view hallucination as fixable because it stems from LLMs doing exactly what they were developed and trained to do: respond, however they can, to user prompts. The real problem, according to some AI researchers, lies in our collective ideas about what these models are and how we've decided to use them. To mitigate hallucinations, the researchers say, generative AI tools must be paired with fact-checking systems that leave no chatbot unsupervised.
Many conflicts related to AI hallucinations have roots in marketing and hype. Tech companies have portrayed their LLMs as digital Swiss Army knives, capable of solving myriad problems or replacing human work. But applied in the wrong setting, these tools simply fail. Chatbots have offered users incorrect and potentially harmful medical advice, media outlets have published AI-generated articles that included inaccurate financial guidance, and search engines with AI interfaces have invented fake citations. As more people and businesses rely on chatbots for factual information, their tendency to make things up becomes even more apparent and disruptive.
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But today's LLMs were never designed to be purely accurate. They were created to create—to generate—says Subbarao Kambhampati, a computer science professor who researches artificial intelligence at Arizona State University. "The reality is: there's no way to guarantee the factuality of what is generated," he explains, adding that all computer-generated "creativity is hallucination, to some extent."
In a preprint study released in January, three machine-learning researchers at the National University of Singapore presented a proof that hallucination is inevitable in large language models. The proof applies some classic results in learning theory, such as Cantor's diagonalization argument, to demonstrate that that LLMs simply cannot learn all computable functions. In other words, it shows that there will always be solvable problems beyond a model's abilities. "For any LLM, there is a part of the real world that it cannot learn, where it will inevitably hallucinate," wrote study co-authors Ziwei Xu, Sanjay Jain and Mohan Kankanhalli in a joint e-mail to Scientific American.
Although the proof appears to be accurate, Kambhampati says, the argument it makes—that certain difficult problems will always stump computers—is too broad to provide much insight into why specific confabulations happen. And, he continues, the issue is more widespread than the proof shows because LLMs hallucinate even when faced with simple requests.
One main reason AI chatbots routinely hallucinate stems from their fundamental construction, says Dilek Hakkani-Tür, a computer science professor who studies natural language and speech processing at the University of Illinois at Urbana-Champaign. LLMs are basically hyperadvanced autocomplete tools; they are trained to predict what should come next in a sequence such as a string of text. If a model's training data include lots of information on a certain subject, it might produce accurate outputs. But LLMs are built to always produce an answer, even on topics that don't appear in their training data. Hakkani-Tür says this increases the chance errors will emerge.
Adding more factually grounded training data might seem like an obvious solution. But there are practical and physical limits to how much information an LLM can hold, says computer scientist Amr Awadallah, co-founder and CEO of the AI platform Vectara, which tracks hallucination rates among LLMs on a leaderboard. (The lowest hallucination rates among tracked AI models are around 3 to 5 percent.) To achieve their language fluency, these massive models are trained on orders of magnitude more data than they can store—and data compression is the inevitable result. When LLMs cannot "recall everything exactly like it was in their training, they make up stuff and fill in the blanks," Awadallah says. And, he adds, these models already operate at the edge of our computing capacity; trying to avoid hallucinations by making LLMs larger would produce slower models that are more expensive and more environmentally harmful to operate.
Another cause of hallucination is calibration, says Santosh Vempala, a computer science professor at the Georgia Institute of Technology. Calibration is the process by which LLMs are adjusted to favor certain outputs over others (to match the statistics of training data or to generate more realistically human-sounding phrases).* In a preprint paper first released last November, Vempala and a coauthor suggest that any calibrated language model will hallucinate—because accuracy itself is sometimes at odds with text that flows naturally and seems original. Reducing calibration can boost factuality while simultaneously introducing other flaws in LLM-generated text. Uncalibrated models might write formulaically, repeating words and phrases more often than a person would, Vempala says. The problem is that users expect AI chatbots to be both factual and fluid.
Accepting that LLMs may never be able to produce completely accurate outputs means reconsidering when, where and how we deploy these generative tools, Kambhampati says. They are wonderful idea generators, he adds, but they are not independent problem solvers. "You can leverage them by putting them into an architecture with verifiers," he explains—whether that means putting more humans in the loop or using other automated programs.
At Vectara, Awadallah is working on exactly that. His team's leaderboard project is an early proof of concept for a hallucination detector—and detecting hallucinations is the first step to being able to fix them, he says. A future detector might be paired with an automated AI editor that corrects errors before they reach an end user. His company is also working on a hybrid chatbot and news database called AskNews, which combines an LLM with a retrieval engine that picks the most relevant facts from recently published articles to answer a user's question. Awadallah says AskNews provides descriptions of current events that are significantly more accurate than what an LLM alone could produce because the chatbot bases its responses only on the sources dredged up by the database search tool.
Hakkani-Tür, too, is researching factually grounded systems that pair specialized language models with relatively reliable information sources such as corporate documents, verified product reviews, medical literature or Wikipedia posts to boost accuracy. She hopes that—once all the kinks are ironed out—these grounded networks could one day be useful tools for things like health access and educational equity. "I do see the strength of language models as tools for making our lives better, more productive and more fair," she says.
In a future where specialized systems verify LLM outputs, AI tools designed for specific contexts would partially replace today's all-purpose models. Each application of an AI text generator (be it a customer service chatbot, a news summary service or even a legal adviser) would be part of a custom-built architecture that would enable its utility. Meanwhile less- grounded generalist chatbots would be able to respond to anything you ask but with no guarantee of truth. They would continue to be powerful creative partners or sources of inspiration and entertainment—yet not oracles or encyclopedias—exactly as designed.
*Editor's Note (4/5/24): This sentence was edited after posting. It previously stated that mitigating bias in a large language model's output is an example of calibration. That is instead a separate process known as alignment.
Global Healthcare Natural Language Processing Industry Is Expected To Grow At A Sturdy CAGR Of 18.0% To Reach US$ 18.5 Billion By 2033FMI
Global Healthcare Natural Language Processing Industry The Global Healthcare Natural Language Processing Industry is surging. Expected to reach US$3.5 billion by the end of 2023, it's projected to nearly quadruple in size by 2033, reaching US$18.5 billion. This impressive growth, fueled by a robust 18% compound annual growth rate (CAGR), reflects the increasing adoption of NLP for tasks like medical record analysis, drug discovery, and virtual assistants. This trend signifies a future where NLP plays a vital role in revolutionizing healthcare efficiency and innovation.
In the realm of NLP, text and voice processing technologies emerge as the market vanguard, commanding a substantial share of approximately 34.7% within the global landscape throughout 2023. This shift is indicative of a paradigmatic transformation in the healthcare industry, driven by cutting-edge innovations in natural language processing.
The majority of large end-user firms in various industries primarily use these language processing technologies to improve their internal and external operations. Furthermore, because the return on investment in technology is not necessarily monetary, most small businesses consider it a dangerous investment.
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Customers' demand for better healthcare services is likely to positively impact the Global Healthcare Natural Language Processing Industry adoption trends. The top companies in the global natural language processing in healthcare and life sciences business are concentrating their efforts on increasing the integration of digital innovations in the healthcare sector. Language processing is an artificial intelligence (AI) subset that promotes human-machine interaction.
Large-scale social media platforms are also using text analytics and natural language processing (NLP) technology to measure and supervise social media activity such as political evaluations and hate speeches. These tools are used by platforms such as Facebook and Twitter to manage published material.
The increased importance of web data for successful marketing and decision-making is expected to drive up demand for information extraction product applications. Mobile chatbots are expected to change the marketing and commerce industries in the coming years.
As per the Global Healthcare Natural Language Processing Industry study by Future Market Insights, because key players are focused on research and development in natural language processing platforms used in the healthcare industry, the global natural language processing (NLP) in the healthcare and life sciences market is predicted to rise significantly.
Regulatory barriers to the language processing deployment and high training costs for NLP models are two main problems limiting the healthcare natural language processing market share's growth.
The Health Insurance Portability and Accountability Act Rules of India state that healthcare data security is the most important element in the healthcare and life sciences industry, citing an increase in cyber-attacks on healthcare organizations and cybercriminals developing increasingly sophisticated tools and methods to attack healthcare natural language processing organizations.
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Global Healthcare Natural Language Processing Industry Key Takeaways
Global Healthcare Natural Language Processing Industry Competitive Landscape
The Global Healthcare Natural Language Processing Industry is fiercely competitive, with several significant competitors vying for more market share. These market leaders have been concentrating on growing their customer bases in international nations, as well as creating new inventive solutions, as well as pursuing agreements and mergers to enhance their market share and profitability.
Major players operating in the global natural language processing in the healthcare and life sciences market include 3M, Cerner Corporation, IBM Corporation, Microsoft Corporation, Hewlett Packard Enterprise Development LP, Health Fidelity, Inc., Centene Corporation, Inovalon, Amazon.Com, Inc., Averbis GmbH, Clinithink, Wave Health Technologies, SparkCognition, Lexalytics, Conversica Inc., Dolbey Systems, Inc., and Alphabet Inc.
Global Healthcare Natural Language Processing Industry Key Players
Global Healthcare Natural Language Processing Industry Key Segments
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More Than Chatbots: AI Trends Driving Conversational Experiences For Customers
As Chief Business Officer, Ivan Ostojić is responsible for strengthening Infobip's critical future and market-shaping functions.
gettyTo date, businesses have used artificial intelligence (AI) to enhance the customer journey in areas such as customer support and content creation. As a result, while customer communications platforms have used AI capabilities such as machine learning and natural language processing, many communications platform as a service (CPAAS) providers have yet to fully integrate AI into their offer. Yet, with businesses and brands realizing AI can transform the customer journey, this is changing.
Gathering PaceThere are growing examples of artificial intelligence (AI) improving customer experience. Take, for example, Octopus Energy's customer service platform. By integrating generative AI, the business can draft more effective email responses, leading to higher customer satisfaction. The AI application reportedly responds to a third of all customer inquiry emails. Likewise, JetBlue's partnership with ASAPP utilizes a generative AI-enabled solution to automate and augment its chat channel, saving significant customer agent time and improving efficiency in handling customer queries. Stitch Fix uses generative AI for creating ad headlines and product descriptions.
ChatbotsPerhaps the area where we have seen the greatest adoption of AI is with chatbots. Unlike traditional chatbots, conversational AI uses natural language processing (NLP) to conduct human-like conversations and can perform complex tasks and refer queries to a human agent when required. A good example would be the chatbot my company developed with Microsoft for LAQO, but there are many others on the market, as well.
The use cases vary from industry to industry. In retail and e-commerce, for example, AI chatbots can improve customer service and loyalty through round-the-clock, multilingual support and lead generation. By leveraging data, a chatbot can provide personalized responses tailored to the customer, context and intent.
Marketing and advertising teams can benefit from AI's personalized product suggestions, boosting customer lifetime value. Healthcare businesses may see streamlined appointment bookings and feedback collection. Finance and banking institutions can leverage AI for information services and fraud prevention, while transportation may use it to facilitate ride-booking and tracking, elevating the user experience.
The integration of conversational AI into these sectors demonstrates its potential to automate and personalize customer interactions, leading to improved service quality and increased operational efficiency.
Reducing Churn And Increasing ConversionsIt could be easy to assume that the benefits of AI are primarily around saving employee time. Yet, AI is revolutionizing how businesses engage with customers by personalizing experiences, predicting behaviors and enhancing service quality, thus reducing churn and increasing conversion rates. It can leverage customer interaction data to tailor content and recommendations to each individual. This technology can also assist in crafting realistic customer personas using large datasets, which can then help businesses understand customer needs and refine marketing strategies.
By employing predictive analytics, AI can identify customers at risk of churn, enabling proactive measures like tailored offers to retain them. Sentiment analysis via AI aids in understanding customer emotions toward the brand by analyzing feedback across various platforms, allowing businesses to address issues and reinforce positive aspects quickly.
CPaaS Market OutlookWith these developments in mind, it is clear that AI can benefit the end-to-end customer journey, and the CPaaS market—cloud-based platforms that facilitate these communications with customers—has a critical role to play in embedding AI into customer communications.
Juniper Research anticipates that AI-powered LLMs, including ChatGPT, will play a pivotal role in distinguishing conversational commerce vendors in 2024. Their forecast indicates that global retail spending through conversational commerce channels will surge to $43 billion by 2028, a substantial increase from the $11.4 billion recorded in 2023. This remarkable growth of over 280% will be fueled by the advent of personalized services facilitated by the integration of AI and LLMs.
Gartner predicts that "around 90% of businesses will be leveraging these tools by 2026, an increase of 30% from 2022." To adapt to the emergence of generative AI and large language models, CPaaS providers are taking a partner approach, whereby they connect their CPaaS toolkit with leading generative AI vendors like Microsoft, Google and Amazon. In the Magic Quadrant for Communications Platform as a Service 2023, Gartner believes that the huge funding commitments to build and maintain generative AI means that CPaaS providers' only realistic option is to partner.
It's evident that businesses continuously need to adapt to changing communication trends driven by advancements in technology. By embedding AI, we can improve the experience for consumers and brands across the customer journey. We can already see the impact on ROI: several fold higher ROI on marketing and sales, higher NPS and 20%-30% of cost savings on average. The companies embracing and embedding this technology are quickly gaining competitive advantage, as are the communications platforms that support them.
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