The notoriously challenging book is being re-released for its 30th anniversary. Its fandom is annoying, sure--but at least they read. The host had been grilling Wallace, ostensibly invited on to discuss his own literary and journalistic output, on range of topics: tennis, teaching, why women don't like Westerns, depression, and, yes, Anthony Minghella's Academy Award-winning epic war drama, which had by the time the interview aired already become a punch line . Watching the interview, it's clear Wallace, who died by suicide in 2008, bristles at being pressed to purvey rank punditry on the popular culture at large like some kind of dancing monkey. But the exercise revealed how Rose, and large swaths of American intellectual culture circa the late-1990s, thought of Wallace.

We present ROBOTO2, an open-source, web-based platform for large language model (LLM)-assisted risk of bias (ROB) assessment of clinical trials. ROBOTO2 streamlines the traditionally labor-intensive ROB v2 (ROB2) annotation process via an interactive interface that combines PDF parsing, retrieval-augmented LLM prompting, and human-in-the-loop review. Users can upload clinical trial reports, receive preliminary answers and supporting evidence for ROB2 signaling questions, and provide real-time feedback or corrections to system suggestions. ROBOTO2 is publicly available at https://roboto2.vercel.app/, with code and data released to foster reproducibility and adoption. We construct and release a dataset of 521 pediatric clinical trial reports (8954 signaling questions with 1202 evidence passages), annotated using both manually and LLM-assisted methods, serving as a benchmark and enabling future research. Using this dataset, we benchmark ROB2 performance for 4 LLMs and provide an analysis into current model capabilities and ongoing challenges in automating this critical aspect of systematic review.

Taylor Sheridan, the most overextended man in television, has done it again. Landman, according to the internal metrics at Paramount, is the most watched original show the streamer has ever had. Remember, Yellowstone proper is on Peacock.) The West Texas–set story, which stars Billy Bob Thornton as Tommy Norris, an all-purpose problem solver for a fictional oil company owned by Monty Miller (Jon Hamm), has also developed a bit more of a critical halo than Sheridan's other TV ventures, popping up on best-of-2024 lists, edging into mainstream discourse via podcasts that typically cover more-prestige fare, and retaining a score of 80 percent on Rotten Tomatoes. And the week before Landman wrapped up, this past Sunday night, its lead actor, Billy Bob Thornton, attended the Golden Globes as a nominee for his role in the series.

While the rise of large language models (LLMs) has created rich new opportunities to learn about digital technology, many on the margins of this technology struggle to gain and maintain competency due to lexical or conceptual barriers that prevent them from asking appropriate questions. Although there have been many efforts to understand factuality of LLM-created content and ability of LLMs to answer questions, it is not well understood how unclear or nonstandard language queries affect the model outputs. We propose the creation of a dataset that captures questions of digital newcomers and outsiders, utilizing data we have compiled from a decade's worth of one-on-one tutoring. In this paper we lay out our planned efforts and some potential uses of this dataset.

Etruscan is an ancient language spoken in Italy from the 7th century BC to the 1st century AD. There are no native speakers of the language at the present day, and its resources are scarce, as there exist only around 12,000 known inscriptions. To the best of our knowledge, there are no publicly available Etruscan corpora for natural language processing. Therefore, we propose a dataset for machine translation from Etruscan to English, which contains 2891 translated examples from existing academic sources. Some examples are extracted manually, while others are acquired in an automatic way. Along with the dataset, we benchmark different machine translation models observing that it is possible to achieve a BLEU score of 10.1 with a small transformer model. Releasing the dataset can help enable future research on this language, similar languages or other languages with scarce resources.

A popular approach to unveiling the black box of neural NLP models is to leverage saliency methods, which assign scalar importance scores to each input component. A common practice for evaluating whether an interpretability method is faithful has been to use evaluation-by-agreement -- if multiple methods agree on an explanation, its credibility increases. However, recent work has found that saliency methods exhibit weak rank correlations even when applied to the same model instance and advocated for the use of alternative diagnostic methods. In our work, we demonstrate that rank correlation is not a good fit for evaluating agreement and argue that Pearson-$r$ is a better-suited alternative. We further show that regularization techniques that increase faithfulness of attention explanations also increase agreement between saliency methods. By connecting our findings to instance categories based on training dynamics, we show that the agreement of saliency method explanations is very low for easy-to-learn instances. Finally, we connect the improvement in agreement across instance categories to local representation space statistics of instances, paving the way for work on analyzing which intrinsic model properties improve their predisposition to interpretability methods.

Bots like ChatGPT may not be able to pull off the next big Microsoft server worm or Colonial Pipeline ransomware super-infection but they may help criminal gangs and nation-state hackers develop some attacks against IT, according to Rob Joyce, director of the NSA's Cybersecurity Directorate. Joyce, speaking at CrowdStrike's Government Summit Tuesday, said he doesn't expect to see -- at least not "in the near term" -- AI used "for automated attacks that will rip through systems at speeds that are unfathomable today." Machine learning and its chatbot offspring are "the tools that are going to flow and increase the pace of the threat," Joyce claimed. "It's not going to generate the threat itself." Miscreants can use ML software to develop more authentic-seeming phishing lures and craft better ransom notes, while also scanning larger volumes of data for sensitive info they can monetize, he offered.

The AI software Stable Diffusion has a remarkable ability to turn text into images. When I asked the software to draw "Mickey Mouse in front of a McDonald's sign," for example, it generated the picture you see above. Stable Diffusion can do this because it was trained on hundreds of millions of example images harvested from across the web. Some of these images were in the public domain or had been published under permissive licenses such as Creative Commons. Many others were not--and the world's artists and photographers aren't happy about it.

We present TrialsSummarizer, a system that aims to automatically summarize evidence presented in the set of randomized controlled trials most relevant to a given query. Building on prior work, the system retrieves trial publications matching a query specifying a combination of condition, intervention(s), and outcome(s), and ranks these according to sample size and estimated study quality. The top-k such studies are passed through a neural multi-document summarization system, yielding a synopsis of these trials. We consider two architectures: A standard sequence-to-sequence model based on BART and a multi-headed architecture intended to provide greater transparency to end-users. Both models produce fluent and relevant summaries of evidence retrieved for queries, but their tendency to introduce unsupported statements render them inappropriate for use in this domain at present. The proposed architecture may help users verify outputs allowing users to trace generated tokens back to inputs.

These image-generating AI models are trained on vast data sets consisting of images with text descriptions that have been scraped from the internet. The latest generation of the technology works by taking images in the data set and changing one pixel at a time until the original image is nothing but a collection of random pixels. The AI model then reverses the process to make the pixelated mess into a new image. The paper is the first time researchers have managed to prove that these AI models memorize images in their training sets, says Ryan Webster, a PhD student at the University of Caen Normandy in France, who has studied privacy in other image generation models but was not involved in the research. This could have implications for startups wanting to use generative AI models in health care, because it shows that these systems risk leaking sensitive private information.