Large language models (LLMs) are increasingly utilized by researchers across a wide range of domains, and qualitative social science is no exception; however, this adoption faces persistent challenges, including interpretive bias, low reliability, and weak auditability. We introduce a framework that situates LLM usage along two dimensions, interpretive depth and autonomy, thereby offering a straightforward way to classify LLM applications in qualitative research and to derive practical design recommendations. We present the state of the literature with respect to these two dimensions, based on all published social science papers available on Web of Science that use LLMs as a tool and not strictly as the subject of study. Rather than granting models expansive freedom, our approach encourages researchers to decompose tasks into manageable segments, much as they would when delegating work to capable undergraduate research assistants. By maintaining low levels of autonomy and selectively increasing interpretive depth only where warranted and under supervision, one can plausibly reap the benefits of LLMs while preserving transparency and reliability.

Scientists have uncovered hidden patterns in the Bible that challenge ancient beliefs about its origins. Using artificial intelligence, they discovered'fingerprints' in text throughout the Old Testament, suggesting multiple people wrote the stories. The traditional Jewish and Christian understanding is that Moses wrote the first five books of the Old Testament, including stories about creation, Noah's flood and the Ark of the Covenant. The new study found three distinct writing styles with distinct vocabulary, tone and focus areas, suggesting multiple authors and sources contributed to the books over time. Researchers used AI analyzed for 50 chapters across five books, uncovering inconsistencies in language and content, repeated stories, shifts in tone and internal contradictions.

Legal reform can be challenging in light of the volume, complexity, and interdependence of laws, codes, and records. One salient example of this challenge is the effort to restrict and remove racially restrictive covenants, clauses in property deeds that historically barred individuals of specific races from purchasing homes. Despite the Supreme Court holding such racial covenants unenforceable in 1948, they persist in property records across the United States. Many jurisdictions have moved to identify and strike these provisions, including California, which mandated in 2021 that all counties implement such a process. Yet the scale can be overwhelming, with Santa Clara County (SCC) alone having over 24 million property deed documents, making purely manual review infeasible. We present a novel approach to addressing this pressing issue, developed through a partnership with the SCC Clerk-Recorder's Office. First, we leverage an open large language model, finetuned to detect racial covenants with high precision and recall. We estimate that this system reduces manual efforts by 86,500 person hours and costs less than 2% of the cost for a comparable off-the-shelf closed model. Second, we illustrate the County's integration of this model into responsible operational practice, including legal review and the creation of a historical registry, and release our model to assist the hundreds of jurisdictions engaged in similar efforts. Finally, our results reveal distinct periods of utilization of racial covenants, sharp geographic clustering, and the disproportionate role of a small number of developers in maintaining housing discrimination. We estimate that by 1950, one in four properties across the County were subject to racial covenants.

Chiasmus, a debated literary device in Biblical texts, has captivated mystics while sparking ongoing scholarly discussion. In this paper, we introduce the first computational approach to systematically detect chiasmus within Biblical passages. Our method leverages neural embeddings to capture lexical and semantic patterns associated with chiasmus, applied at multiple levels of textual granularity (half-verses, verses). We also involve expert annotators to review a subset of the detected patterns. Despite its computational efficiency, our method achieves robust results, with high inter-annotator agreement and system precision@k of 0.80 at the verse level and 0.60 at the half-verse level. We further provide a qualitative analysis of the distribution of detected chiasmi, along with selected examples that highlight the effectiveness of our approach.

A sci-fi series has taken the number one spot on IMDB following the release of its second season - despite the show's'disappointing' debut in 2022. The first season of the video game adaptation was deemed a'one-hit' wonder' by viewers who felt the story was written by a'high schooler' and the graphics were'low budget CGI.' But Halo season two, released this month, now sits at number one in IDMB's list of top sci-fi TV series. The Paramount series has 7.2 stars and more than 81,000 votes - overtaking popular shows like Netflix's Stranger Things and Black Mirror. Halo also has an 89 percent on Rotten Tomatoes - a jump from season one's 61 percent rating.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. In an age of deepfakes and post-truth, as artificial intelligence rose and Elon Musk turned Twitter into X, the Merriam-Webster word of the year for 2023 is "authentic." Lookups for the word are routinely heavy on the dictionary company's site but were boosted to new heights throughout the year, editor at large Peter Sokolowski told The Associated Press in an exclusive interview. "We see in 2023 a kind of crisis of authenticity," he said ahead of Monday's announcement of this year's word.

Developing safe and useful general-purpose AI systems will require us to make progress on scalable oversight: the problem of supervising systems that potentially outperform us on most skills relevant to the task at hand. Empirical work on this problem is not straightforward, since we do not yet have systems that broadly exceed our abilities. This paper discusses one of the major ways we think about this problem, with a focus on ways it can be studied empirically. We first present an experimental design centered on tasks for which human specialists succeed but unaided humans and current general AI systems fail. We then present a proof-of-concept experiment meant to demonstrate a key feature of this experimental design and show its viability with two question-answering tasks: MMLU and time-limited QuALITY. On these tasks, we find that human participants who interact with an unreliable large-language-model dialog assistant through chat -- a trivial baseline strategy for scalable oversight -- substantially outperform both the model alone and their own unaided performance. These results are an encouraging sign that scalable oversight will be tractable to study with present models and bolster recent findings that large language models can productively assist humans with difficult tasks.

Quite how Halo hasn't made it to the screen, small or big, before this is an enigma almost as nebulous as the long-running first person shooter video game's crowded mythos. Luminaries such as Steven Spielberg, Peter Jackson and District 9's Neill Blomkamp have all been involved in trying to get a film based on the explosive exploits of Masterchief across the line for the best part of two decades, yet to no avail. Even this big-budget – it reputedly cost more than $200m and looks like gold – TV series starring Pablo Schreiber as the genetically engineered soldier-hero of the United Nations Space Command (UNSC) has been held up for two years by Covid. Never mind, it's here now, and fans of the games who just want to see their nightly battles with giant space monsters played out on the TV screen will no doubt be more than content with Kyle Killen and Steven Kane's adventurous if somewhat insipid reimagining. Unfortunately, those of us who don't recognise every re-enacted power-up bleep and helmet-cam vision of destruction will probably find ourselves wondering, much of the time, quite what is going on.

Question: I've owned and lived in my very small Los Angeles condo complex for 16 years and am president of the association. Many of my neighbors are established professionals, including a couple of financial advisors across the hall. They have lived in the building for more than 40 years and have no plans to move. But they have serious drinking problems and never-ending blowout fights. At all hours of the day and night they scream at each other and throw and slam stuff inside their unit.

At the site of an ancient city on the West Bank, archaeologists are hunting for evidence of the tabernacle that once housed the Ark of the Covenant. Associates for Biblical Research, a consortium of individuals and universities, recently completed four weeks of excavation in Shiloh with the goal of eventually locating the tabernacle. Dr. Scott Stripling, director of excavations at Shiloh and provost at The Bible Seminary in Houston, Texas, told Fox News that the site could offer up vital clues. "We have just begun the process of accumulating evidence but we're confident that the tabernacle rested at Shiloh," he said, adding that that the tabernacle was located at Shiloh for about 350 years. "The tabernacle was set up at Shiloh in 1400 B.C. - Joshua 18:1 mentions it."