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User Experience with AI Chatbots
Survey results showing user perception of AI reliability.
Primary Sources
Ask ChatGPT: caveats and mitigations for individual users of AI ...
1 IntroductionSince the emergence of ChatGPT in late 2022, Large Language Model (LLM)-based AI chatbots have gained significant attention [1]. A recent survey reports that 60% of Americans have used ChatGPT and other AI chatbots for information gathering and practical advice across a wide range of areas such as educational help, financial advice, product recommendations, career advice, and legal or mental health consultations. While 70% of users stated that ChatGPT was helpful, 10% reported that following ChatGPT’s guidance actually caused harm to them [2].A recent Pew Research Center report on “Artificial Intelligence in Daily Life” examines the views of both American public and AI experts, and reveals both deep divides and common grounds on AI. Both groups express concerns over the trustworthiness of AI, such as inaccurate information, bias, data misuse and personal impacts [3]. While AI chatbots offer numerous benefits, the question remains: what potential harm may arise from their widespread use?There are already numerous surveys on the trustworthiness of AI systems, addressing topics such as their security and privacy [4], bias [5] and responsible use [6]. However, these surveys have primarily focused on the development and deployment of AI systems, as well as related policy-making [7], rather than on their use in individuals’ daily lives.While previous studies have discussed the potential harms to AI users, to our knowledge, no in-depth risk analysis has been conducted in this field from the perspective of personal use. Such a risk analysis, we expect, could be highly valuable for individual users.In this work, we focus specifically on the experiences and interactions of individual users of LLM-based AI systems, particularly AI chatbots such as ChatGPT. These users engage with AI for various purposes, including education, information gathering, and counseling in areas such as technology, business, and health. They are everyday users with no malicious intent and no involvement in adversarial hacking of these chatbots. This study specifically concentrates on personal use of AI, excluding broader economic and societal concerns related to AI adoption, such as job displacement [8], misuse by malicious actors [9], and the environmental and energy costs associated with AI [10, 11].The primary research questions addressed in this work are: What potential risks and harms could AI chatbots pose to individual users? And what mitigation measures can be implement...
AI Research at UIowa: Refining How Large Language Models Process Audio
Large Language Models Working with Audio Weiran Wang, assistant professor at the University of Iowa, has defined his career by exploring machine learning and speech processing. Google is helping fund his personal research on advancing audio comprehension within Large Language Models. These Large Language Models, or LLMs, are the programs that AI systems use to analyze user inputs. Professor Wang’s research explores audio-based Large Language Models and the “hallucinations” they may suffer. While LLMs are designed to process complex audio inputs and generate human-like responses, they might “hallucinate” confident outputs that are factually incorrect or untethered from the original audio input. For the average user, and even for professionals, these errors can be deceptive. Since the AI presents its “hallucinations” with authority, it can lead users to accept incorrect information without them realizing that this data is imaginary.Reducing HallucinationsAn example of an AI “hallucination” could appear when the LLM processes the sound of a glass breaking. The danger isn’t simply that the AI could mislabel the sound, even if the input is only a single noise. A conversational LLM could try to elaborate by describing a burglar shattering a window or someone knocking a glass over at a party. In these instances, the AI “hears” a story that doesn’t exist. Instead of remaining grounded in factual acoustic data, the model invents contexts and events to explain the sound.Professor Wang’s research focuses on preventing these “phantom” narratives and limiting the AI’s responses to facts present in the audio. By reducing the frequency of these “hallucinations,” Wang’s work aims to increase the reliability of audio-based models. As these systems become more grounded, users and professionals can deploy them to effectively handle more complex tasks.Old Models Versus New ModelsBefore Google funded his current research, Professor Wang’s work centered on specialized audio processing models. Those were designed for narrow, predefined tasks that took an audio input and returned a specific output, based strictly on how researchers trained the model. Professor Wang describes those traditional systems like “black boxes” in terms of their flexibility. Unlike modern LLMs, which users can guide through natural language, those older models have instructions baked into their functionality. Researchers can’t easily alter the models’ behavior or ask them to perform new tasks afterward. ...
AI Processing Limits: When Language Models Hit Barriers
The Architecture of AI Limitations: Understanding When Language Models Reach Processing Boundaries Modern artificial intelligence systems represent sophisticated computational networks designed to process and respond to human queries, yet these systems operate within clearly defined technical parameters that can create unexpected barriers for users. Understanding these limitations provides ...
Verbosity Decreases Accuracy in Large Language Models - Unite.AI
New research finds that forcing Large Language Models to give shorter answers notably improves the accuracy and quality of their answers. Anyone who has tried to stop a chatbot from 'rambling' will recognize the conclusions of new research: forcing AI to give shorter replies makes it more accurate.Investigating the reasons why larger AI chatbots perform worse […]
