Automatic discourse processing is bottlenecked by data: current discourse formalisms pose highly demanding annotation tasks involving large taxonomies of discourse relations, making them inaccessible to lay annotators. This work instead adopts the linguistic framework of Questions Under Discussion (QUD) for discourse analysis and seeks to derive QUD structures automatically. QUD views each sentence as an answer to a question triggered in prior context; thus, we characterize relationships between sentences as free-form questions, in contrast to exhaustive fine-grained taxonomies. We develop the first-of-its-kind QUD parser that derives a dependency structure of questions over full documents, trained using a large, crowdsourced question-answering dataset DCQA (Ko et al., 2022). Human evaluation results show that QUD dependency parsing is possible for language models trained with this crowdsourced, generalizable annotation scheme. We illustrate how our QUD structure is distinct from RST trees, and demonstrate the utility of QUD analysis in the context of document simplification. Our findings show that QUD parsing is an appealing alternative for automatic discourse processing.

Ensuring that large language models (LMs) are fair, robust and useful requires an understanding of how different modifications to their inputs impact the model's behaviour. In the context of open-text generation tasks, however, such an evaluation is not trivial. For example, when introducing a model with an input text and a perturbed, "contrastive" version of it, meaningful differences in the next-token predictions may not be revealed with standard decoding strategies. With this motivation in mind, we propose Contrastive Input Decoding (CID): a decoding algorithm to generate text given two inputs, where the generated text is likely given one input but unlikely given the other. In this way, the contrastive generations can highlight potentially subtle differences in how the LM output differs for the two inputs in a simple and interpretable manner. We use CID to highlight context-specific biases that are hard to detect with standard decoding strategies and quantify the effect of different input perturbations.

Large generative AI models (LGAIMs), such as ChatGPT, GPT-4 or Stable Diffusion, are rapidly transforming the way we communicate, illustrate, and create. However, AI regulation, in the EU and beyond, has primarily focused on conventional AI models, not LGAIMs. This paper will situate these new generative models in the current debate on trustworthy AI regulation, and ask how the law can be tailored to their capabilities. After laying technical foundations, the legal part of the paper proceeds in four steps, covering (1) direct regulation, (2) data protection, (3) content moderation, and (4) policy proposals. It suggests a novel terminology to capture the AI value chain in LGAIM settings by differentiating between LGAIM developers, deployers, professional and non-professional users, as well as recipients of LGAIM output. We tailor regulatory duties to these different actors along the value chain and suggest strategies to ensure that LGAIMs are trustworthy and deployed for the benefit of society at large. Rules in the AI Act and other direct regulation must match the specificities of pre-trained models. The paper argues for three layers of obligations concerning LGAIMs (minimum standards for all LGAIMs; high-risk obligations for high-risk use cases; collaborations along the AI value chain). In general, regulation should focus on concrete high-risk applications, and not the pre-trained model itself, and should include (i) obligations regarding transparency and (ii) risk management. Non-discrimination provisions (iii) may, however, apply to LGAIM developers. Lastly, (iv) the core of the DSA content moderation rules should be expanded to cover LGAIMs. This includes notice and action mechanisms, and trusted flaggers. In all areas, regulators and lawmakers need to act fast to keep track with the dynamics of ChatGPT et al.

Privacy policies provide individuals with information about their rights and how their personal information is handled. Natural language understanding (NLU) technologies can support individuals and practitioners to understand better privacy practices described in lengthy and complex documents. However, existing efforts that use NLU technologies are limited by processing the language in a way exclusive to a single task focusing on certain privacy practices. To this end, we introduce the Privacy Policy Language Understanding Evaluation (PLUE) benchmark, a multi-task benchmark for evaluating the privacy policy language understanding across various tasks. We also collect a large corpus of privacy policies to enable privacy policy domain-specific language model pre-training. We evaluate several generic pre-trained language models and continue pre-training them on the collected corpus. We demonstrate that domain-specific continual pre-training offers performance improvements across all tasks.

The widespread adoption of Machine Learning systems, especially in more decision-critical applications such as criminal sentencing and bank loans, has led to increased concerns about fairness implications. Algorithms and metrics have been developed to mitigate and measure these discriminations. More recently, works have identified a more challenging form of bias called intersectional bias, which encompasses multiple sensitive attributes, such as race and gender, together. In this survey, we review the state-of-the-art in intersectional fairness. We present a taxonomy for intersectional notions of fairness and mitigation. Finally, we identify the key challenges and provide researchers with guidelines for future directions.

On Thursday, May 11 at 19:30 GMT: Popular AI image generators are able to produce seemingly endless amounts of stunning visual art in just a matter of seconds. So where does the technology leave professional artists? AI image generators are trained on datasets made up of billions of images collected online and generally without the artists' knowledge or approval. Users can then prompt the AI to create new artwork in the style of a specific artist. For many digital artists, the technology represents a threat to their livelihoods.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Authorities around the world are racing to draw up rules for artificial intelligence, including in the European Union, where draft legislation faced a pivotal moment on Thursday. A European Parliament committee voted to strengthen the flagship legislative proposal as it heads toward passage, part of a yearslong effort by Brussels to draw up guardrails for artificial intelligence. Those efforts have taken on more urgency as the rapid advances of chatbots like ChatGPT highlight benefits the emerging technology can bring -- and the new perils it poses.

I have been studying the whole range of issues/opportunities in the commercial roll out of robotics for many years now, and I've spoken at a number of conferences about the best way for us to look at regulating robotics. In the process I've found that my guidelines most closely match the EPSRC Principles of Robotics, although I provide additional focus on potential solutions. And I'm calling it the 5 Laws of Robotics because it's so hard to avoid Asimov's Laws of Robotics in the public perception of what needs to be done. The first most obvious point about these "5 Laws of Robotics" should be that I'm not suggesting actual laws, and neither actually was Asimov with his famous 3 Laws (technically 4 of them). Asimov proposed something that was hardwired or hardcoded into the existence of robots, and of course that didn't work perfectly, which gave him the material for his books.

AI image generators Midjourney and Stable Diffusion trained their models with the works of countless artists without their permission or compensation, artist says. AI-generated images that mimic an artist's style is a form of identity theft and compete with the very creatives whose work was used to train the models, a fine artist suing two artificial intelligence firms told Fox News. AI platforms like Midjourney and Stable Diffusion use text and images from across the internet and other sources to train their machines to create images for their consumers. "Somebody is able to mimic my work because a company let them," Ortiz told Fox News. "It feels like some sort of industrial-level identity theft."

Fox News correspondent Mark Meredith has the latest on ChatGPT on'Special Report.' With AI large language models like ChatGPT being developed around the globe, countries have raced to regulate AI. Some have drafted strict laws on the technology, while others lack regulatory oversight. China and the EU have received particular attention, as they have created detailed, yet divergent, AI regulations. In both, the government plays a large role.