In the rapidly evolving field of text generation, the demand for more precise control mechanisms has become increasingly apparent. To address this need, we present a novel methodology, LIFI, which offers a lightweight approach with fine-grained control for controlled text generation. Unlike previous studies that train pre-trained language models to follow discrete, categorical, and exclusive control codes, LIFI learns controlled text generation under the guidance of continuous, relative, and nonexclusive control codes. These fine-grained codes are automatically derived from an attribute classifier, initially trained with a small amount of labeled data and subsequently employed to label abundant unlabeled data, thus garnering more extensive supervision signals. Moreover, to achieve efficient control, we incorporate the fine-grained control codes with adapters, a parameter- and compute-efficient way to steer a pre-trained language model. We evaluate LIFI on two conventional tasks -- sentiment control and topic control -- and one newly proposed task -- stylistic novel writing. Comprehensive experimental results validate the effectiveness of our proposed methods, demonstrating substantial performance improvements over existing baselines.

Large language models (LLMs) powered conversational search systems have already been used by hundreds of millions of people, and are believed to bring many benefits over conventional search. However, while decades of research and public discourse interrogated the risk of search systems in increasing selective exposure and creating echo chambers -- limiting exposure to diverse opinions and leading to opinion polarization, little is known about such a risk of LLM-powered conversational search. We conduct two experiments to investigate: 1) whether and how LLM-powered conversational search increases selective exposure compared to conventional search; 2) whether and how LLMs with opinion biases that either reinforce or challenge the user's view change the effect. Overall, we found that participants engaged in more biased information querying with LLM-powered conversational search, and an opinionated LLM reinforcing their views exacerbated this bias. These results present critical implications for the development of LLMs and conversational search systems, and the policy governing these technologies.

We propose the Data Contamination Quiz (DCQ), a simple and effective approach to detect data contamination in large language models (LLMs) and estimate the amount of it. Specifically, we frame data contamination detection as a series of multiple-choice questions and devise a quiz format wherein three perturbed versions of each dataset instance are created. These changes only include word-level perturbations. The generated perturbed versions, along with the original instance, form the options in the DCQ, with an extra option accommodating the possibility that none of the provided choices is correct. Given that the only distinguishing signal among the choices is the exact wording relative to the original instance, an LLM, when tasked with identifying the original instance from the choices, gravitates towards the original one if it has been exposed to it in its pre-training phase--a trait intrinsic to LLMs. Tested over several datasets with GPT-4/3.5, our findings--while fully lacking access to LLMs' pre-training data and internal parameters--suggest that DCQ uncovers greater contamination levels compared to existing detection methods and proficiently bypasses more safety filters, especially those set to avoid generating copyrighted contents.

Researchers have slammed US officials for not rolling out stricter AI rules before popstar Taylor Swift became victim of deepfakes. Images showing the four-time Grammy winner in a series of sexual acts while dressed in Kansas City Chief memorabilia and in the stadium - and the pornography share - was viewed 47 million times online before being removed. A professor at George Washington University Law School said if proper legislation was'passed years ago' Swift and others would not have experienced such abuse. 'We are too little, too late at this point,' said Mary Anne Franks. 'It's not just going to be the 14-year-old girl or Taylor Swift. It's going to be politicians.

Last week, The Browser Company, a startup that makes the Arc web browser, released a slick new iPhone app called Arc Search. Instead of displaying links, its brand new "Browse for Me" feature reads the first handful of pages and summarizes them into a single, custom-built, Arc-formatted web page using large language models from OpenAI and others. If a user does click through to any of the actual pages, Arc Search blocks ads, cookies and trackers by default. Arc's efforts to reimagine web browsing have received near-universal acclaim. But over the last few days, "Browse for Me" earned The Browser Company its first online backlash.

Kara Frederick, tech director at the Heritage Foundation, discusses the need for regulations on artificial intelligence as lawmakers and tech titans discuss the potential risks. The way people search for information online could soon be changing as artificial intelligence continues to advance, and with it a new company could dethrone what has long been the king of online searching. "It's certainly conceivable that AI could ultimately replace search, especially if AI can learn what its user wants and deliver more relevant responses," Jon Schweppe, the Policy Director of the American Principles Project, told Fox News Digital while cautioning that there are still a lot of unknowns with the technology. "We are in the nascent stages of the AI revolution and it's still not clear that these companies know how to monetize it." The comments come as new search product called Perplexity has quickly become one of the most talked about platforms in technology, with an AI-driven search function that rivals or even bests traditional search platforms such as Google and Bing, according to a report from the New York Times.

In this paper, we present the current progress of the project Verif.ai, an open-source scientific generative question-answering system with referenced and verified answers. The components of the system are (1) an information retrieval system combining semantic and lexical search techniques over scientific papers (PubMed), (2) a fine-tuned generative model (Mistral 7B) taking top answers and generating answers with references to the papers from which the claim was derived, and (3) a verification engine that cross-checks the generated claim and the abstract or paper from which the claim was derived, verifying whether there may have been any hallucinations in generating the claim. We are reinforcing the generative model by providing the abstract in context, but in addition, an independent set of methods and models are verifying the answer and checking for hallucinations. Therefore, we believe that by using our method, we can make scientists more productive, while building trust in the use of generative language models in scientific environments, where hallucinations and misinformation cannot be tolerated.

Throughout history, humanity's enduring captivation with violence has found expression across diverse artistic mediums and literary creations, frequently acting as a reflective mirror that echoes societal concerns while offering a cathartic release. However, the emergence of AI has introduced a fresh perspective to this relationship, eliciting ethical quandaries concerning the algorithmic creation of violent visual content. This study focuses on the "Gore Diffusion LoRA Model," a newly devised AI framework capable of generating highly realistic images depicting intense violence and bloodshed. The paper scrutinizes the technical intricacies of this model, its plausible applications, and the ethical considerations entwined with its utilization. The Gore Diffusion LoRA Model uses Latent Diffusion models, a form of AI proficient in fabricating top-notch images from latent noise vectors [HJAS20].

Many people are interested in ChatGPT since it has become a prominent AIGC model that provides high-quality responses in various contexts, such as software development and maintenance. Misuse of ChatGPT might cause significant issues, particularly in public safety and education, despite its immense potential. The majority of researchers choose to publish their work on Arxiv. The effectiveness and originality of future work depend on the ability to detect AI components in such contributions. To address this need, this study will analyze a method that can see purposely manufactured content that academic organizations use to post on Arxiv. For this study, a dataset was created using physics, mathematics, and computer science articles. Using the newly built dataset, the following step is to put originality.ai through its paces. The statistical analysis shows that Originality.ai is very accurate, with a rate of 98%.

The rapid dissemination of misinformation through social media increased the importance of automated fact-checking. Furthermore, studies on what deep neural models pay attention to when making predictions have increased in recent years. While significant progress has been made in this field, it has not yet reached a level of reasoning comparable to human reasoning. To address these gaps, we propose a multi-task explainable neural model for misinformation detection. Specifically, this work formulates an explanation generation process of the model's veracity prediction as a text summarization problem. Additionally, the performance of the proposed model is discussed on publicly available datasets and the findings are evaluated with related studies.