In today's media landscape, where news outlets play a pivotal role in shaping public opinion, it is imperative to address the issue of sentiment manipulation within news text. News writers often inject their own biases and emotional language, which can distort the objectivity of reporting. This paper introduces a novel approach to tackle this problem by reducing the polarity of latent sentiments in news content. Drawing inspiration from adversarial attack-based sentence perturbation techniques and a prompt based method using ChatGPT, we employ transformation constraints to modify sentences while preserving their core semantics. Using three perturbation methods: replacement, insertion, and deletion coupled with a context-aware masked language model, we aim to maximize the desired sentiment score for targeted news aspects through a beam search algorithm. Our experiments and human evaluations demonstrate the effectiveness of these two models in achieving reduced sentiment polarity with minimal modifications while maintaining textual similarity, fluency, and grammatical correctness. Comparative analysis confirms the competitive performance of the adversarial attack based perturbation methods and prompt-based methods, offering a promising solution to foster more objective news reporting and combat emotional language bias in the media.

Anthropomorphism, or the attribution of human-like characteristics to non-human entities, has shaped conversations about the impacts and possibilities of technology. We present AnthroScore, an automatic metric of implicit anthropomorphism in language. We use a masked language model to quantify how non-human entities are implicitly framed as human by the surrounding context. We show that AnthroScore corresponds with human judgments of anthropomorphism and dimensions of anthropomorphism described in social science literature. Motivated by concerns of misleading anthropomorphism in computer science discourse, we use AnthroScore to analyze 15 years of research papers and downstream news articles. In research papers, we find that anthropomorphism has steadily increased over time, and that papers related to language models have the most anthropomorphism. Within ACL papers, temporal increases in anthropomorphism are correlated with key neural advancements. Building upon concerns of scientific misinformation in mass media, we identify higher levels of anthropomorphism in news headlines compared to the research papers they cite. Since AnthroScore is lexicon-free, it can be directly applied to a wide range of text sources.

Conversational recommender systems (CRS) aim to recommend relevant items to users by eliciting user preference through natural language conversation. Prior work often utilizes external knowledge graphs for items' semantic information, a language model for dialogue generation, and a recommendation module for ranking relevant items. This combination of multiple components suffers from a cumbersome training process, and leads to semantic misalignment issues between dialogue generation and item recommendation. In this paper, we represent items in natural language and formulate CRS as a natural language processing task. Accordingly, we leverage the power of pre-trained language models to encode items, understand user intent via conversation, perform item recommendation through semantic matching, and generate dialogues. As a unified model, our PECRS (Parameter-Efficient CRS), can be optimized in a single stage, without relying on non-textual metadata such as a knowledge graph. Experiments on two benchmark CRS datasets, ReDial and INSPIRED, demonstrate the effectiveness of PECRS on recommendation and conversation. Our code is available at: https://github.com/Ravoxsg/efficient_unified_crs.

In this paper, we propose a novel graph neural network-based recommendation model called KGLN, which leverages Knowledge Graph (KG) information to enhance the accuracy and effectiveness of personalized recommendations. We first use a single-layer neural network to merge individual node features in the graph, and then adjust the aggregation weights of neighboring entities by incorporating influence factors. The model evolves from a single layer to multiple layers through iteration, enabling entities to access extensive multi-order associated entity information. The final step involves integrating features of entities and users to produce a recommendation score. The model performance was evaluated by comparing its effects on various aggregation methods and influence factors. In tests over the MovieLen-1M and Book-Crossing datasets, KGLN shows an Area Under the ROC curve (AUC) improvement of 0.3% to 5.9% and 1.1% to 8.2%, respectively, which is better than existing benchmark methods like LibFM, DeepFM, Wide&Deep, and RippleNet.

Although there are some big-name omissions (Netflix, YouTube and Spotify), the headset already supports over a million compatible App Store apps, Apple's first-party offerings and over 600 apps developed specifically for the "spatial computing" device. Here are the notable third-party Vision Pro apps you can install on day one. Microsoft didn't skimp on its entry into the Vision Pro era. Seven of the company's Office apps are available to install on launch day. These include Microsoft Teams, Word, Excel, PowerPoint, Outlook, OneNote and Loop.

Fox News Flash top entertainment and celebrity headlines are here. Lainey Wilson testified in front of congress during a hearing regarding artificial intelligence and intellectual property on Friday. Wilson shared her experience as a "victim" of AI. The hearing began with an example of Johnny Cash's voice used to sing the lyrics of "Barbie Girl" to the tune of "Folsom Prison Blues." Many artists are now seeing their voices used to create music and other content without their consent, according to Wilson.

A Voice of America journalist Wenhao, who specialises in Chinese online disinformation, posted on X that the "biggest US related news on China's internet for the past few days is Texas governor declaring war with the federal government, which did not happen in reality".

When Molly Crabapple touched down in Italy last year for the International Journalism Festival, she expected the usual. The annual conference bills itself as Europe's largest media event, and Crabapple had planned to give a talk about her career as an artist and writer reporting from the front lines of conflict zones. But as she took in some of the panels, she felt herself growing uneasy. Sprinkled among the journalists discussing topics such as the war in Ukraine and the state of podcasting, some of the speakers were promoting the use of generative AI. She overheard someone say that journalists write too much, that much of their work could be automated.

For all the promise of the technology, one use-case for artificial intelligence reared its ugly head last week: non-consensual pornographic images. As millions of users saw abusive A.I. generated images of Taylor Swift proliferate across X, the pitfalls of this technology became clear. If you enjoy this show, please consider signing up for Slate Plus. Slate Plus members get benefits like zero ads on any Slate podcast, bonus episodes of shows like Slow Burn and Dear Prudence--and you'll be supporting the work we do here on What Next TBD. Sign up now at slate.com/whatnextplus to help support our work.

Relation extraction aims at inferring structured human knowledge from textual documents. State-of-the-art methods based on language models commonly have two limitations: (1) they require named entities to be either given as input or infer them, which introduces additional noise, and (2) they require human annotations of documents. As a remedy, we present a novel framework for document-level in-context few-shot relation extraction via pre-trained language models. We achieve crucial benefits in that we eliminate the need for both named entity recognition and human annotation of documents. Unlike existing methods based on fine-tuning, our framework is flexible in that it can be easily updated for a new set of relations without re-training. We evaluate our framework using DocRED, the largest publicly available dataset for document-level relation extraction, and demonstrate that our framework achieves state-of-the-art performance. Finally, we show that our framework actually performs much better than the original labels from the development set of DocRED. To the best of our knowledge, we are the first to reformulate the document-level relation extraction task as a tailored in-context few-shot learning paradigm.