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Recent memoirs by the Justices reveal how a new vision of restraint has led to radical outcomes. To understand how grudging Amy Coney Barrett's new book is when it comes to revealing personal details, consider that one of the family members the Supreme Court Justice most often refers to is a great-grandmother who died five years before she was born. On Barrett's desk at home, she recounts in " Listening to the Law," she keeps a photograph of her great-grandmother's one-story house, where, as a widow during the Great Depression, she raised some of her thirteen children and took in other needy relatives. "Looking at the photo reminds me of a woman who stretched herself beyond all reasonable capacity," Barrett explains. "I'm not sure that I'll be able to manage my life with the same grace that she had. But she motivates me to keep trying." For Barrett, the mother of seven children, that effort entails setting her alarm for 5 "Our kids get up at six thirty during the school year, so I start early if I want to accomplish anything on my own to-do list," she writes. This is what passes for disclosure from Barrett; she measures out the details of her life with coffee spoons, careful not to spill.

In the indictment, prosecutors claim Tyler Robinson planned Kirk's killing in advance, citing rooftop surveillance, engraved bullets, and a written note as they seek the death penalty. A TV monitor displays a picture of Tyler Robinson, a suspect in the killing of Charlie Kirk in Orem, Utah. Utah County prosecutors on Tuesday charged Tyler Robinson in the shooting death of conservative activist Charlie Kirk at Utah Valley University, a murder officials say was politically motivated. They intend to seek the death penalty. Utah County Attorney Jeff Gray announced the indictment at a midday news conference, listing charges of aggravated murder, felony discharge of a firearm causing serious bodily injury, and commission of a violent offense in the presence of a child.

Argument mining (AM) is an interdisciplinary research field that integrates insights from logic, philosophy, linguistics, rhetoric, law, psychology, and computer science. It involves the automatic identification and extraction of argumentative components, such as premises and claims, and the detection of relationships between them, such as support, attack, or neutrality. Recently, the field has advanced significantly, especially with the advent of large language models (LLMs), which have enhanced the efficiency of analyzing and extracting argument semantics compared to traditional methods and other deep learning models. There are many benchmarks for testing and verifying the quality of LLM, but there is still a lack of research and results on the operation of these models in publicly available argument classification databases. This paper presents a study of a selection of LLM's, using diverse datasets such as Args.me and UKP. The models tested include versions of GPT, Llama, and DeepSeek, along with reasoning-enhanced variants incorporating the Chain-of-Thoughts algorithm. The results indicate that ChatGPT-4o outperforms the others in the argument classification benchmarks. In case of models incorporated with reasoning capabilities, the Deepseek-R1 shows its superiority. However, despite their superiority, GPT-4o and Deepseek-R1 still make errors. The most common errors are discussed for all models. To our knowledge, the presented work is the first broader analysis of the mentioned datasets using LLM and prompt algorithms. The work also shows some weaknesses of known prompt algorithms in argument analysis, while indicating directions for their improvement. The added value of the work is the in-depth analysis of the available argument datasets and the demonstration of their shortcomings.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Bryan Kohberger is accused of committing an unspeakably evil act, stabbing to death four University of Idaho students in their off-campus home in November 2022. The killings were brutal, and as soon as Kohberger was arrested, some members of the victims' families demanded that he should be executed if he is convicted. Kohberger is due to stand trial in August. In the run-up to that trial, his defense lawyers have filed a flurry of motions challenging various aspects of the prosecution's case. Filing such motions is standard in death cases, though in Kohberger's case, the defense and prosecution have done much of that work in secret.

This study explores how the Large Language Models (LLMs) adjust linguistic features to create personalized persuasive outputs. While research showed that LLMs personalize outputs, a gap remains in understanding the linguistic features of their persuasive capabilities. We identified 13 linguistic features crucial for influencing personalities across different levels of the Big Five model of personality. We analyzed how prompts with personality trait information influenced the output of 19 LLMs across five model families. The findings show that models use more anxiety-related words for neuroticism, increase achievement-related words for conscientiousness, and employ fewer cognitive processes words for openness to experience. Some model families excel at adapting language for openness to experience, others for conscientiousness, while only one model adapts language for neuroticism. Our findings show how LLMs tailor responses based on personality cues in prompts, indicating their potential to create persuasive content affecting the mind and well-being of the recipients.

Large language models (LLMs) have exhibited remarkable performance across various tasks in natural language processing. Nevertheless, challenges still arise when these tasks demand domain-specific expertise and advanced analytical skills, such as conducting research surveys on a designated topic. In this research, we develop ResearchArena, a benchmark that measures LLM agents' ability to conduct academic surveys, an initial step of academic research process. Specifically, we deconstructs the surveying process into three stages 1) information discovery: locating relevant papers, 2) information selection: assessing papers' importance to the topic, and 3) information organization: organizing papers into meaningful structures. In particular, we establish an offline environment comprising 12.0M full-text academic papers and 7.9K survey papers, which evaluates agents' ability to locate supporting materials for composing the survey on a topic, rank the located papers based on their impact, and organize these into a hierarchical knowledge mind-map. With this benchmark, we conduct preliminary evaluations of existing techniques and find that all LLM-based methods under-performing when compared to basic keyword-based retrieval techniques, highlighting substantial opportunities for future research.

We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues. Existing benchmarks and measures focus on gender and racial biases. However, political bias exists in LLMs and can lead to polarization and other harms in downstream applications. In order to provide transparency to users, we advocate that there should be fine-grained and explainable measures of political biases generated by LLMs. Our proposed measure looks at different political issues such as reproductive rights and climate change, at both the content (the substance of the generation) and the style (the lexical polarity) of such bias. We measured the political bias in eleven open-sourced LLMs and showed that our proposed framework is easily scalable to other topics and is explainable.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Idaho legislature passed a bill this week to carry out the death penalty for sex crimes against children younger than 12. Another bill permitting prosecutors to bring sexual exploitation charges against producers of child pornography using artificial intelligence (AI) also passed the assembly in the same session. HB 515 would amend Idaho's current statute that carries a life sentence for "lewd conduct with a minor" below the age of 16. If the child is under 12, if the act is "especially heinous, atrocious or cruel, manifesting exceptional depravity," then prosecutors would seek the death penalty.

Jurors take an oath to make rulings without bias or prejudice, but a new study suggests that promise is broken when the death penalty is on the table. Researchers from Columbia University on Thursday revealed that the shape of defendants' facial features affects whether they are sentenced to death or given life in prison. Hundreds of mugshots of Florida inmates who were convicted of murder were shown to a mock jury in the experiment. Certain facial features – such as downturned lips and heavy eyebrows – were judged to be more untrustworthy and more likely to be sentenced to death. Hundreds of mugshots of Florida inmates who were convicted of murder were shown to a mock jury in the experiment.