This study demonstrates that the modern generation of Large Language Models (LLMs, such as GPT-4) suffers from the same out-of-domain (OOD) performance gap observed in prior research on pre-trained Language Models (PLMs, such as BERT). We demonstrate this across two non-topical classification tasks: 1) genre classification and 2) generated text detection. Our results show that when demonstration examples for In-Context Learning (ICL) come from one domain (e.g., travel) and the system is tested on another domain (e.g., history), classification performance declines significantly. To address this, we introduce a method that controls which predictive indicators are used and which are excluded during classification. For the two tasks studied here, this ensures that topical features are omitted, while the model is guided to focus on stylistic rather than content-based attributes. This approach reduces the OOD gap by up to 20 percentage points in a few-shot setup. Straightforward Chain-of-Thought (CoT) methods, used as the baseline, prove insufficient, while our approach consistently enhances domain transfer performance.

We address the challenging task of neural machine translation (NMT) in the entertainment domain, where the objective is to automatically translate a given dialogue from a source language content to a target language. This task has various applications, particularly in automatic dubbing, subtitling, and other content localization tasks, enabling source content to reach a wider audience. Traditional NMT systems typically translate individual sentences in isolation, without facilitating knowledge transfer of crucial elements such as the context and style from previously encountered sentences. In this work, we emphasize the significance of these fundamental aspects in producing pertinent and captivating translations. We demonstrate their significance through several examples and propose a novel framework for entertainment translation, which, to our knowledge, is the first of its kind. Furthermore, we introduce an algorithm to estimate the context and style of the current session and use these estimations to generate a prompt that guides a Large Language Model (LLM) to generate high-quality translations. Our method is both language and LLM-agnostic, making it a general-purpose tool. We demonstrate the effectiveness of our algorithm through various numerical studies and observe significant improvement in the COMET scores over various state-of-the-art LLMs. Moreover, our proposed method consistently outperforms baseline LLMs in terms of win-ratio.

Recent advances in text-conditioned video diffusion have greatly improved video quality. However, these methods offer limited or sometimes no control to users on camera aspects, including dynamic camera motion, zoom, distorted lens and focus shifts. These motion and optical aspects are crucial for adding controllability and cinematic elements to generation frameworks, ultimately resulting in visual content that draws focus, enhances mood, and guides emotions according to filmmakers' controls. In this paper, we aim to close the gap between controllable video generation and camera optics. To achieve this, we propose AKiRa (Augmentation Kit on Rays), a novel augmentation framework that builds and trains a camera adapter with a complex camera model over an existing video generation backbone. It enables fine-tuned control over camera motion as well as complex optical parameters (focal length, distortion, aperture) to achieve cinematic effects such as zoom, fisheye effect, and bokeh. Extensive experiments demonstrate AKiRa's effectiveness in combining and composing camera optics while outperforming all state-of-the-art methods. This work sets a new landmark in controlled and optically enhanced video generation, paving the way for future optical video generation methods.

We show that existing evaluations for fake news detection based on conventional sources, such as claims on fact-checking websites, result in high accuracies over time for LLM-based detectors -- even after their knowledge cutoffs. This suggests that recent popular fake news from such sources can be easily detected due to pre-training and retrieval corpus contamination or increasingly salient shallow patterns. Instead, we argue that a proper fake news detection dataset should test a model's ability to reason factually about the current world by retrieving and reading related evidence. To this end, we develop a novel pipeline that leverages natural language feedback from a RAG-based detector to iteratively modify real-time news into deceptive fake news that challenges LLMs. Our iterative rewrite decreases the binary classification ROC-AUC by an absolute 17.5 percent for a strong RAG-based GPT-4o detector. Our experiments reveal the important role of RAG in both detecting and generating fake news, as retrieval-free LLM detectors are vulnerable to unseen events and adversarial attacks, while feedback from RAG detection helps discover more deceitful patterns in fake news.

Just before midnight on New Year's Eve, 25 years ago, Queen Elizabeth II stepped off a private barge to arrive at London's Millennium Dome for its grand opening ceremony. Dressed in a pumpkin-orange coat, she entered the venue with Prince Philip, taking her place alongside Tony and Cherie Blair and 12,000 guests to celebrate the dawn of a new millennium. At the stroke of midnight, Big Ben began to chime and 40 tonnes of fireworks were launched from 16 barges lined along the river. The crowd joined hands, preparing to sing Auld Lang Syne. For a few long moments, the Queen was neglected – she flapped her arms out like a toddler wanting to be lifted up, before Blair and Philip noticed her, took a hand each, and the singing began. A new century was born. One politician who wasn't in attendance at the glitzy celebration was Paddy Tipping, a Labour MP who spent the night in the Cabinet Office.

The British-Canadian computer scientist dubbed the'Godfather of AI' has shortened the odds of artificial intelligence (AI) wiping out humans over the next 30 years, warning the technology could one day'take control'. Professor Geoffrey Hinton said we need to be'very careful' and'very thoughtful' about the development of AI which he says is'potentially very dangerous'. He had previously said there was a 10 per cent chance of the technology causing the extinction of the human race - but now predicts that figure to be '10 per cent to 20 per cent', because of the rapid pace at which AI is developing. Speaking on BBC Radio 4's Today programme, Professor Hinton said: 'You see, we've never had to deal with things more intelligent than ourselves before.' He continued: 'And how many examples do you know of a more intelligent thing being controlled by a less intelligent thing?

Preferences within a group of people are not uniform but follow a distribution. While existing alignment methods like Direct Preference Optimization (DPO) attempt to steer models to reflect human preferences, they struggle to capture the distributional pluralistic preferences within a group. These methods often skew toward dominant preferences, overlooking the diversity of opinions, especially when conflicting preferences arise. To address this issue, we propose Group Distribution Preference Optimization (GDPO), a novel framework that aligns language models with the distribution of preferences within a group by incorporating the concept of beliefs that shape individual preferences. GDPO calibrates a language model using statistical estimation of the group's belief distribution and aligns the model with belief-conditioned preferences, offering a more inclusive alignment framework than traditional methods. In experiments using both synthetic controllable opinion generation and real-world movie review datasets, we show that DPO fails to align with the targeted belief distributions, while GDPO consistently reduces this alignment gap during training. Moreover, our evaluation metrics demonstrate that GDPO outperforms existing approaches in aligning with group distributional preferences, marking a significant advance in pluralistic alignment.

The content of visual and audio scenes is multi-faceted such that a video can be paired with various audio and vice-versa. Thereby, in video-to-audio generation task, it is imperative to introduce steering approaches for controlling the generated audio. While Video-to-Audio generation is a well-established generative task, existing methods lack such controllability. In this work, we propose VATT, a multi-modal generative framework that takes a video and an optional text prompt as input, and generates audio and optional textual description of the audio. Such a framework has two advantages: i) Video-to-Audio generation process can be refined and controlled via text which complements the context of visual information, and ii) The model can suggest what audio to generate for the video by generating audio captions. VATT consists of two key modules: VATT Converter, a LLM that is fine-tuned for instructions and includes a projection layer that maps video features to the LLM vector space; and VATT Audio, a transformer that generates audio tokens from visual frames and from optional text prompt using iterative parallel decoding. The audio tokens are converted to a waveform by pretrained neural codec. Experiments show that when VATT is compared to existing video-to-audio generation methods in objective metrics, it achieves competitive performance when the audio caption is not provided. When the audio caption is provided as a prompt, VATT achieves even more refined performance (lowest KLD score of 1.41). Furthermore, subjective studies show that VATT Audio has been chosen as preferred generated audio than audio generated by existing methods. VATT enables controllable video-to-audio generation through text as well as suggesting text prompts for videos through audio captions, unlocking novel applications such as text-guided video-to-audio generation and video-to-audio captioning.

If you or someone you know is having thoughts of suicide, please contact the Suicide & Crisis Lifeline at 988 or 1-800-273-TALK (8255). Balaji's death on November 26 was ruled a suicide, and Fox News Digital previously reported that the San Francisco Police Department found no evidence of foul play. But the 26-year-old's mother is urging police to reopen their investigation, saying it "doesn't look like a normal situation." Bereaved mother Poornima Ramarao told Business Insider that a private autopsy commissioned by Balaji's family and completed in early December produced concerning results. Now, they are working with an attorney to urge the department to conduct a "proper investigation."

The British-Canadian computer scientist often touted as a "godfather" of artificial intelligence has shortened the odds of AI wiping out humanity over the next three decades, warning the pace of change in the technology is "much faster" than expected. Prof Geoffrey Hinton, who this year was awarded the Nobel prize in physics for his work in AI, said there was a "10% to 20%" chance that AI would lead to human extinction within the next three decades. Previously Hinton had said there was a 10% chance of the technology triggering a catastrophic outcome for humanity. Asked on BBC Radio 4's Today programme if he had changed his analysis of a potential AI apocalypse and the one in 10 chance of it happening, he said: "Not really, 10% to 20%." Hinton's estimate prompted Today's guest editor, the former chancellor Sajid Javid, to say "you're going up", to which Hinton replied: "If anything. You see, we've never had to deal with things more intelligent than ourselves before."