Powerful moment Charlie Kirk's widow Erika holds hands with Usha Vance on his final journey on Air Force Two REVEALED: The truth about the'vanishing plane' five miles from Charlie Kirk's assassination... as private jet owner is unmasked Charlie Kirk's incredible welcome to young gay man who wants to join his conservative movement And the armed militia mystery. FBI terror hunter blows the lid on search for Charlie Kirk's assassin... and the vital clue cops are desperate for Kristin Chenoweth fans surprised over her grieving comment on Charlie Kirk's final video about abortion Charlotte Tilbury reveals the secrets behind the Dallas Cowboys cheerleaders' flawless look Go inside the killing that has rocked America - on Daily Mail's podcast The Assassination of Charlie Kirk Charlie Kirk's gesture to my son tells you everything about the man: JILLIAN MICHAELS on her unlikely camaraderie with the conservative giant Joe Rogan is speechless as he learns of Charlie Kirk's assassination on his podcast McDonald's fans disgusted by what customer thinks is'parasite' found in Filet-O-Fish Walkers and hikers have an exciting opportunity to find meteorite fragments that scattered over Scotland this summer, scientists say. The bright meteor was witnessed by some Scots as it streaked across the sky in the early hours of Thursday July 3. It is believed to have exploded over northern Scotland, with the'fall zone' straddling Loch Treig in Lochaber, Highland. The aerial event was captured on some cameras and shared on social media, showing a big yellow spark soaring through the dark sky.

Teenager who lost his legs in crash will'never forgive' driver 38 minutes agoShareSaveKen Banks and Louise HosieBBC Scotland NewsShareSaveBBC Adam Golebiewski had a double amputation after the crash last year A teenager who lost his lower legs in a crash says he "will never forgive" the drink-driver at the wheel. Young footballer Adam Golebiewski, 18, had been a passenger in Arran Paterson's car in Macduff, Aberdeenshire, in September last year. Paterson, 19, admitted dangerous driving, being over the drink-drive limit and driving without insurance at Aberdeen Sheriff Court. Adam walked into court unaided on prosthetic legs following intensive rehabilitation. He said: "I want to try to enjoy life again and stay positive."

Fact tracing seeks to identify specific training examples that serve as the knowledge source for a given query. Existing approaches to fact tracing rely on assessing the similarity between each training sample and the query along a certain dimension, such as lexical similarity, gradient, or embedding space. However, these methods fall short of effectively distinguishing between samples that are merely relevant and those that actually provide supportive evidence for the information sought by the query. This limitation often results in suboptimal effectiveness. Moreover, these approaches necessitate the examination of the similarity of individual training points for each query, imposing significant computational demands and creating a substantial barrier for practical applications. This paper introduces FASTTRACK, a novel approach that harnesses the capabilities of Large Language Models (LLMs) to validate supportive evidence for queries and at the same time clusters the training database towards a reduced extent for LLMs to trace facts. Our experiments show that FASTTRACK substantially outperforms existing methods in both accuracy and efficiency, achieving more than 100\% improvement in F1 score over the state-of-the-art methods while being X33 faster than \texttt{TracIn}.

After a year in which industry was knocked off its axis by the coming of age of artificial intelligence and the transition to an online world continued apace, new businesses are emerging and old industries reinventing themselves to adapt. Here, we look at five companies making the most of these turbulent times. It's been a difficult year for the operators of electric scooters, bikes and mopeds: most notably in Paris, where its e-scooter rental scheme was shut down by city authorities after a popular vote. One big player, Tier, nominated here a year ago as a company to watch, also lost its business in London when trial licences were renewed. Increasingly, in the crowded streets of the UK, rental ebikes are looking a better bet than the e-scooter: a more familiar mode of transport for occasional users, feeling safer and with the bonus of sitting rather than standing.

The widely studied task of Natural Language Inference (NLI) requires a system to recognize whether one piece of text is textually entailed by another, i.e. whether the entirety of its meaning can be inferred from the other. In current NLI datasets and models, textual entailment relations are typically defined on the sentence- or paragraph-level. However, even a simple sentence often contains multiple propositions, i.e. distinct units of meaning conveyed by the sentence. As these propositions can carry different truth values in the context of a given premise, we argue for the need to recognize the textual entailment relation of each proposition in a sentence individually. We propose PropSegmEnt, a corpus of over 45K propositions annotated by expert human raters. Our dataset structure resembles the tasks of (1) segmenting sentences within a document to the set of propositions, and (2) classifying the entailment relation of each proposition with respect to a different yet topically-aligned document, i.e. documents describing the same event or entity. We establish strong baselines for the segmentation and entailment tasks. Through case studies on summary hallucination detection and document-level NLI, we demonstrate that our conceptual framework is potentially useful for understanding and explaining the compositionality of NLI labels.

ChatGPT has revealed some scathing stereotypes of UK residents in a merciless study of what clichés exist in every county. The cutting-edge bot labeled Yorkshiremen as'rude' while Londoners were slammed for their arrogance in the nationwide analysis. The truly insulting results came after MailOnline asked ChatGPT to expose what'negative stereotypes' exist of people from our nation. While the bot insisted that it did not condone stereotypes, it offered a list of those associated with each place when prompted. On the whole, residents of the UK were deemed to have bad teeth while being overly polite and obsessed with the Royal Family.

Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebImageText, and LAION have propelled recent dramatic progress in AI. Large neural models trained on such datasets produce impressive results and top many of today's benchmarks. A notable omission within this family of large-scale datasets is 3D data. Despite considerable interest and potential applications in 3D vision, datasets of high-fidelity 3D models continue to be mid-sized with limited diversity of object categories. Addressing this gap, we present Objaverse 1.0, a large dataset of objects with 800K+ (and growing) 3D models with descriptive captions, tags, and animations. Objaverse improves upon present day 3D repositories in terms of scale, number of categories, and in the visual diversity of instances within a category. We demonstrate the large potential of Objaverse via four diverse applications: training generative 3D models, improving tail category segmentation on the LVIS benchmark, training open-vocabulary object-navigation models for Embodied AI, and creating a new benchmark for robustness analysis of vision models. Objaverse can open new directions for research and enable new applications across the field of AI.

Text generation is of great importance to many natural language processing applications. However, maximization-based decoding methods (e.g., beam search) of neural language models often lead to degenerate solutions--the generated text is unnatural and contains undesirable repetitions. Existing approaches introduce stochasticity via sampling or modify training objectives to decrease the probabilities of certain tokens (e.g., unlikelihood training). However, they often lead to solutions that lack coherence. In this work, we show that an underlying reason for model degeneration is the anisotropic distribution of token representations. We present a contrastive solution: (i) SimCTG, a contrastive training objective to calibrate the model's representation space, and (ii) a decoding method--contrastive search--to encourage diversity while maintaining coherence in the generated text. Extensive experiments and analyses on three benchmarks from two languages demonstrate that our proposed approach significantly outperforms current state-of-the-art text generation methods as evaluated by both human and automatic metrics.

Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing. By warm-starting from the publicly released checkpoints, NLP practitioners have pushed the state-of-the-art on multiple benchmarks while saving significant amounts of compute time. So far the focus has been mainly on the Natural Language Understanding tasks. In this paper, we demonstrate the efficacy of pre-trained checkpoints for Sequence Generation. We developed a Transformer-based sequence-to-sequence model that is compatible with publicly available pre-trained BERT, GPT-2 and RoBERTa checkpoints and conducted an extensive empirical study on the utility of initializing our model, both encoder and decoder, with these checkpoints. Our models result in new state-of-the-art results on Machine Translation, Text Summarization, Sentence Splitting, and Sentence Fusion.