In the latest battle between AI and the media, major Danish newspapers and TV stations are threatening to sue OpenAI unless the company compensates the country's press for allegedly using their content to train its models. "We want remuneration for our work [which] they have used to train their model," says Karen Rønde, CEO of the Danish Press Publications' Collective Management Organization (DPCMO), which represents 99 percent of Danish media outlets, including state broadcaster DR and TV 2. Rønde says the DPCMO plans to sue if a deal is not reached in the next year. Soon after those lawsuits, OpenAI struck a series of licensing deals with major publishers, enabling the company to train its future iterations of ChatGPT on their content. Financial terms for the deals have not been disclosed. Now, Danish media is attempting to force OpenAI to negotiate with them as a collective, an unusual tactic that could provide a model for other small countries if successful.

Apple's developer conference on Monday was about more than infusing its software with the latest artificial intelligence technology, including from ChatGPT. It was also about selling more iPhones. Facing choppy consumer spending and resurgent tech rivals, Apple has looked to AI as a way to invigorate its loyal fan base of more than 1 billion customers and to reverse a sales decline for its biggest-selling product.

Apple has revealed a slew of new artificial intelligence-powered features backed by a partnership with OpenAI, as the iPhone maker battles perceptions that it is falling behind in the race to capitalise on the technology. Apple executives including CEO Tim Cook unveiled "Apple Intelligence" on Monday during a nearly two-hour-long presentation at the company's annual Worldwide Developers Conference in Cupertino, California. "All of this goes beyond artificial intelligence, it's personal intelligence, and it is the next big step for Apple," CEO Tim Cook said. The upgrades include an overhaul of the virtual assistant Siri, which will be capable of hundreds of more tasks with the help of ChatGPT. Apple users will also be able to create their own emojis based on language prompts and generate summaries of emails in the mailbox via the tech giant's in-house technology.

Apple's spin on AI is finally here, and it already seems smarter than Microsoft Copilot and Google Bard. Apple Intelligence focuses on privacy and "personal intelligence," with a bit of an assist from ChatGPT. While we haven't tested it ourselves yet, Apple appears to be avoiding the pitfalls of Microsoft's Recall feature, as well as Google Bard's unfortunate early gaffes. The company isn't trying to capture everything you're doing on your computer, and it's being careful about how it's using larger AI models like ChatGPT. Shortly after the WWDC 2024 keynote ended, Engadget's Cherlynn Low and Devindra Hardawar discussed why they think Apple is taking a more thoughtful approach to AI.

Aspect sentiment quad prediction (ASQP) aims to predict four aspect-based elements, including aspect term, opinion term, aspect category, and sentiment polarity. In practice, unseen aspects, due to distinct data distribution, impose many challenges for a trained neural model. Motivated by this, this work formulates ASQP into the few-shot scenario, which aims for fast adaptation in real applications. Therefore, we first construct a few-shot ASQP dataset (FSQP) that contains richer categories and is more balanced for the few-shot study. Moreover, recent methods extract quads through a generation paradigm, which involves converting the input sentence into a templated target sequence. However, they primarily focus on the utilization of a single template or the consideration of different template orders, thereby overlooking the correlations among various templates. To tackle this issue, we further propose a Broadview Soft Prompting (BvSP) method that aggregates multiple templates with a broader view by taking into account the correlation between the different templates. Specifically, BvSP uses the pre-trained language model to select the most relevant k templates with Jensen-Shannon divergence. BvSP further introduces soft prompts to guide the pre-trained language model using the selected templates. Then, we aggregate the results of multi-templates by voting mechanism. Empirical results demonstrate that BvSP significantly outperforms the stateof-the-art methods under four few-shot settings and other public datasets. Our code and dataset are available at https://github.com/byinhao/BvSP.

This paper presents NgramMarkov, a variant of the Markov constraints. It is dedicated to text generation in constraint programming (CP). It involves a set of n-grams (i.e., sequence of n words) associated with probabilities given by a large language model (LLM). It limits the product of the probabilities of the n-gram of a sentence. The propagator of this constraint can be seen as an extension of the ElementaryMarkov constraint propagator, incorporating the LLM distribution instead of the maximum likelihood estimation of n-grams. It uses a gliding threshold, i.e., it rejects n-grams whose local probabilities are too low, to guarantee balanced solutions. It can also be combined with a "look-ahead" approach to remove n-grams that are very unlikely to lead to acceptable sentences for a fixed-length horizon. This idea is based on the MDDMarkovProcess constraint propagator, but without explicitly using an MDD (Multi-Valued Decision Diagram). The experimental results show that the generated text is valued in a similar way to the LLM perplexity function. Using this new constraint dramatically reduces the number of candidate sentences produced, improves computation times, and allows larger corpora or smaller n-grams to be used. A real-world problem has been solved for the first time using 4-grams instead of 5-grams.

Sexism in online content is a pervasive issue that necessitates effective classification techniques to mitigate its harmful impact. Online platforms often have sexist comments and posts that create a hostile environment, especially for women and minority groups. This content not only spreads harmful stereotypes but also causes emotional harm. Reliable methods are essential to find and remove sexist content, making online spaces safer and more welcoming. Therefore, the sEXism Identification in Social neTworks (EXIST) challenge addresses this issue at CLEF 2024. This study aims to improve sexism identification in bilingual contexts (English and Spanish) by leveraging natural language processing models. The tasks are to determine whether a text is sexist and what the source intention behind it is. We fine-tuned the XLM-RoBERTa model and separately used GPT-3.5 with few-shot learning prompts to classify sexist content. The XLM-RoBERTa model exhibited robust performance in handling complex linguistic structures, while GPT-3.5's few-shot learning capability allowed for rapid adaptation to new data with minimal labeled examples. Our approach using XLM-RoBERTa achieved 4th place in the soft-soft evaluation of Task 1 (sexism identification). For Task 2 (source intention), we achieved 2nd place in the soft-soft evaluation.

Reinforcement learning struggles in the face of long-horizon tasks and sparse goals due to the difficulty in manual reward specification. While existing methods address this by adding intrinsic rewards, they may fail to provide meaningful guidance in long-horizon decision-making tasks with large state and action spaces, lacking purposeful exploration. Inspired by human cognition, we propose a new multi-modal model-based RL approach named Dreaming with Large Language Models (DLLM). DLLM integrates the proposed hinting subgoals from the LLMs into the model rollouts to encourage goal discovery and reaching in challenging tasks. By assigning higher intrinsic rewards to samples that align with the hints outlined by the language model during model rollouts, DLLM guides the agent toward meaningful and efficient exploration. Extensive experiments demonstrate that the DLLM outperforms recent methods in various challenging, sparse-reward environments such as HomeGrid, Crafter, and Minecraft by 27.7\%, 21.1\%, and 9.9\%, respectively.

Programming skills are typically developed through completing various hands-on exercises. Such programming problems can be contextualized to students' interests and cultural backgrounds. Prior research in educational psychology has demonstrated that context personalization of exercises stimulates learners' situational interests and positively affects their engagement. However, creating a varied and comprehensive set of programming exercises for students to practice on is a time-consuming and laborious task for computer science educators. Previous studies have shown that large language models can generate conceptually and contextually relevant programming exercises. Thus, they offer a possibility to automatically produce personalized programming problems to fit students' interests and needs. This article reports on a user study conducted in an elective introductory programming course that included contextually personalized programming exercises created with GPT-4. The quality of the exercises was evaluated by both the students and the authors. Additionally, this work investigated student attitudes towards the created exercises and their engagement with the system. The results demonstrate that the quality of exercises generated with GPT-4 was generally high. What is more, the course participants found them engaging and useful. This suggests that AI-generated programming problems can be a worthwhile addition to introductory programming courses, as they provide students with a practically unlimited pool of practice material tailored to their personal interests and educational needs.

Writing mathematical proofs has been identified as an important [1-3] and yet challenging topic [4] in computing education and mathematics education. A large body of research has shown that timely feedback is crucial to student learning [5, 6]. However, students are largely unable to receive timely feedback on written proofs due to the need to have proofs collected and hand-graded by instructors or teaching assistants. The ability to grade student proofs fully automatically with natural language processing (NLP) alleviates this need by allowing us to give students instant feedback on their proofs to let students iteratively enhance the quality of their proofs. In this paper, we propose a novel set of training methods and models capable of autograding freeform mathematical proofs, a problem at the intersection of mathematical proof education and Automatic Short Answer Grading (ASAG), by using existing NLP models and other machine learning techniques. Our proof autograder enables the development of grading systems that provide instant feedback to students without needing attention from instructors. It can also be deployed in large-scale educational platforms, allowing for more access for students. The main contributions of this paper are: Introducing the first pipeline of machine learning models capable of autograding mathematical proofs with similar accuracy to human graders Quantifying the amount of training data needed to achieve a satisfactory performance from the grading models Publishing an anonymized and labeled mathematical proof dataset that can be used in future model developments [7] Creating a set of autograded problems using the grading pipeline, and performing a user study that answers the following research questions: - Are students able to write better proofs by interacting with the autograder and the feedback it generates?