The speech-to-song illusion is a robust psychological phenomenon whereby a spoken sentence sounds increasingly more musical as it is repeated. Despite decades of research, a complete formal account of this transformation is still lacking, and some of its nuanced characteristics, namely, that certain phrases appear to transform while others do not, is not well understood. Here we provide a formal account of this phenomenon, by recasting it as a statistical inference whereby a rational agent attempts to decide whether a sequence of utterances is more likely to have been produced in a song or speech. Using this approach and analyzing song and speech corpora, we further introduce a novel prose-to-lyrics illusion that is purely text-based. In this illusion, simply duplicating written sentences makes them appear more like song lyrics. We provide robust evidence for this new illusion in both human participants and large language models.

Tuning pretrained language models for dialogue generation has been a prevalent paradigm for building capable dialogue agents. Yet, traditional tuning narrowly views dialogue generation as resembling other language generation tasks, ignoring the role disparities between two speakers and the multi-round interactive process that dialogues ought to be. Such a manner leads to unsatisfactory chat consistency of the built agent. In this work, we emphasize the interactive, communicative nature of dialogue and argue that it is more feasible to model the speaker roles of agent and user separately, enabling the agent to adhere to its role consistently. We propose an efficient Multi-round Interactive Dialogue Tuning (Midi-Tuning) framework. It models the agent and user individually with two adapters built upon large language models, where they utilize utterances round by round in alternating order and are tuned via a round-level memory caching mechanism. Extensive experiments demonstrate that, our framework performs superior to traditional fine-tuning and harbors the tremendous potential for improving dialogue consistency.

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].