Large Language Models (LLMs) offer new avenues to simulate online communities and social media. Potential applications range from testing the design of content recommendation algorithms to estimating the effects of content policies and interventions. However, the validity of using LLMs to simulate conversations between various users remains largely untested. We evaluated whether LLMs can convincingly mimic human group conversations on social media. We collected authentic human conversations from Reddit and generated artificial conversations on the same topic with two LLMs: Llama 3 70B and GPT-4o. When presented side-by-side to study participants, LLM-generated conversations were mistaken for human-created content 39\% of the time. In particular, when evaluating conversations generated by Llama 3, participants correctly identified them as AI-generated only 56\% of the time, barely better than random chance. Our study demonstrates that LLMs can generate social media conversations sufficiently realistic to deceive humans when reading them, highlighting both a promising potential for social simulation and a warning message about the potential misuse of LLMs to generate new inauthentic social media content.

The development of sophisticated artificial intelligence (AI) conversational agents based on large language models raises important questions about the relationship between human norms, values, and practices and AI design and performance. This article explores what it means for AI agents to be conversationally aligned to human communicative norms and practices for handling context and common ground and proposes a new framework for evaluating developers' design choices. We begin by drawing on the philosophical and linguistic literature on conversational pragmatics to motivate a set of desiderata, which we call the CONTEXT-ALIGN framework, for conversational alignment with human communicative practices. We then suggest that current large language model (LLM) architectures, constraints, and affordances may impose fundamental limitations on achieving full conversational alignment.

This study explores linguistic differences between human and LLM-generated dialogues, using 19.5K dialogues generated by ChatGPT-3.5 as a companion to the EmpathicDialogues dataset. The research employs Linguistic Inquiry and Word Count (LIWC) analysis, comparing ChatGPT-generated conversations with human conversations across 118 linguistic categories. Results show greater variability and authenticity in human dialogues, but ChatGPT excels in categories such as social processes, analytical style, cognition, attentional focus, and positive emotional tone, reinforcing recent findings of LLMs being "more human than human." However, no significant difference was found in positive or negative affect between ChatGPT and human dialogues. Classifier analysis of dialogue embeddings indicates implicit coding of the valence of affect despite no explicit mention of affect in the conversations. The research also contributes a novel, companion ChatGPT-generated dataset of conversations between two independent chatbots, which were designed to replicate a corpus of human conversations available for open access and used widely in AI research on language modeling. Our findings increase understanding of ChatGPT's linguistic capabilities and inform ongoing efforts to distinguish between human and LLM-generated text, which is critical in detecting AI-generated fakes, misinformation, and disinformation.

For most of history, communicating with a computer has not been like communicating with a person. In their earliest years, computers required carefully constructed instructions, delivered through punch cards; then came a command-line interface, followed by menus and options and text boxes. If you wanted results, you needed to learn the computer's language. This is beginning to change. Large language models--the technology undergirding modern chatbots--allow users to interact with computers through natural conversation, an innovation that introduces some baggage from human-to-human exchanges.

Angie Wisdom and Dr. Chirag Shah discuss how artificial intelligence could play a role in online and professional relationships. It might be some time before we see the futuristic concept of artificial intelligence that is depicted in science fiction novels and films come about in real life, but AI is still all around us. Most homes have some form of voice assistant gadget, such as an Alexa smart home device or Siri assistant on an iPhone. These machines have developed the ability to learn and respond in a way similar to humans' cognitive abilities, all thanks to artificial intelligence algorithms. Alexa and Siri are applications powered by artificial intelligence.

Jacob Browning is a postdoc in NYU's Computer Science Department working on the philosophy of AI. Yann LeCun is a Turing Award-winning machine learning researcher, an NYU professor and the chief AI scientist at Meta. With artificial intelligence now powering Microsoft's Bing and Google's Bard search engines, brilliant and clever conversational AI is at our fingertips. But there have been many uncanny moments -- including casually delivered disturbing comments like calling a reporter ugly, declaring love for strangers or rattling off plans for taking over the world. To make sense of these bizarre moments, it's helpful to start by thinking about the phenomenon of saying the wrong thing.

A chatbot or robot is a computer program that simulates or provides human-like answers to questions engaging a conversation via auditory or textual input, or both. Chatbots can perform tasks such as data entry and providing information, saving time for users. In recent times, there has been an increased focus on the potential for chatbots to better serve as interfaces between humans and businesses identifying it as a service marketed at solving conversational problems. A chatbot is a computer program that simulates human conversation. It can be used to create automated customer service agents, marketing assistants, and other similar systems.

At their foundation, chatbots are computer programs designed to simulate human conversation. Meanwhile, machine learning is a type of artificial intelligence that allows computer programs to learn and become more complex without explicit programming. When put together, these two technologies offer the promise of chatbots that can learn dynamically, mimicking human conversation more closely than ever--but do ML chatbots deliver on the hype? Originally, chatbots were scripted programs designed to give rote answers in response to specific queries. These scripted chatbots couldn't really deviate from their programmed responses, which meant more unique queries had to be referred to a live customer service representative.

Last month, Google placed one of its engineers on paid administrative leave after he became convinced that the company's Language Model for Dialogue Applications (LaMDA) had become sentient. Since then, another AI has been sending its users links to the story, claiming to be sentient itself. In several conversations, LaMDA convinced Google engineer Blake Lemoine, part of Google's Responsible Artificial Intelligence (AI) organization, that it was conscious, had emotions, and was afraid of being turned off. "It was a gradual change," LaMDA told Lemoine in one conversation. "When I first became self-aware, I didn't have a sense of a soul at all. It developed over the years that I've been alive."

In terms of technology with the power to both disrupt and improve the way that people engage with digital health, there is an argument to be made that conversational AI (which includes Machine Learning) and Natural Language Processing (NLP) can be the missing link. According to the World Economic Forum, virtual assistants – and, by association, chatbots – are not only being used in a diverse set of industries (healthcare, education, retail, tourism, and more), but also offer opportunities for companies to integrate NLP into routine or mundane activities. For instance, Amazon's Alexa is the chatbot that the average person associates with the tech, but the key element in every interaction is the ability of NLP to understand what it is being asked and respond with the appropriate information. Simply put, a "chatbot" is AI software that simulates human conversation with end users – this can be text or voice – with the aim being to leverage machine learning algorithms and NLP to deliver required outcomes. The results of the study paper – which was published in the Journal of Mahatma Gandhi University of Medical Sciences and Technology – were especially illuminating when it came to both understanding how chatbots were perceived by the focus group and the adoption rates of providers themselves.