In The End of the World As We Know It, other writers are telling stories set in the post-apocalyptic world of Stephen King's The Stand One of my most anticipated books of the year is out this month: a collection of short stories set in the post-apocalyptic devastation of Stephen King's The Stand. I love a good end-times story, and King did it so well in this doorstopper of a book, first published in 1978. How will the writers he has invited to develop his "world" fare? Suitably depressed by these visions of the future, I'm then planning to pick myself up with New Scientist columnist Annalee Newitz's cosier take, Automatic Noodle, which comes complete with jolly robots and cooking. From thrillers (Artificial Wisdom) to more literary takes (Helm), Star Wars to the latest from the prolific Adrian Tchaikovsky, let's get reading!

Persona-driven LLMs have emerged as powerful tools in computational social science, yet existing approaches fall at opposite extremes, either relying on costly human-curated data or producing synthetic personas that lack consistency and realism. We introduce SYNTHIA, a dataset of 30,000 backstories derived from 10,000 real social media users from BlueSky open platform across three time windows, bridging this spectrum by grounding synthetic generation in authentic user activity. Our evaluation demonstrates that SYNTHIA achieves competitive performance with state-of-the-art methods in demographic diversity and social survey alignment while significantly outperforming them in narrative consistency. Uniquely, SYNTHIA incorporates temporal dimensionality and provides rich social interaction metadata from the underlying network, enabling new research directions in computational social science and persona-driven language modeling.

This paper presents an iterative approach to performing Scientific Named Entity Recognition (SciNER) using BERT - based models. We leverage transfer learning to fine - tune pre - trained models with a small but high - quality set of manually annotated data. The process is iteratively refined by using the fine - tuned model to auto - annotate a larger dataset, followed by additional rounds of fine - tuning. We evaluated two models, dslim/bert - large - NER and bert - large - cased, and found that bert - large - cased consistently outperformed the former. Our approach demonstrated significant improvements in prediction accuracy and F1 sco res, especially for less common entity classes. Future work could include pre - training with unlabeled data, exploring more powerful encoders like RoBERTa, and expanding the scope of manual annotations. This methodology has broader applications in NLP tasks where access to labeled data is limited.

This paper examines the output of cultural items generated by Chat Generative PreTrained Transformer Pro in response to three structured prompts to translate three anthologies of African poetry. The first prompt was broad, the second focused on poetic structure, and the third prompt emphasized cultural specificity. To support this analysis, four comparative tables were created. The first table presents the results of the cultural items produced after the three prompts, the second categorizes these outputs based on Aixela framework of Proper nouns and Common expressions, the third table summarizes the cultural items generated by human translators, a custom translation engine, and a Large Language Model. The final table outlines the strategies employed by Chat Generative PreTrained Transformer Pro following the culture specific prompt. Compared to the outputs of cultural items from reference human translation and the custom translation engine in prior studies the findings indicate that the culture oriented prompts used with Chat Generative PreTrained Transformer Pro did not yield significant enhancements of cultural items during the translation of African poetry from English to French. Among the fifty four cultural items, the human translation produced thirty three cultural items in repetition, the custom translation engine generated Thirty eight cultural items in repetition while Chat Generative PreTrained Transformer Pro produced forty one cultural items in repetition. The untranslated cultural items revealed inconsistencies in Large language models approach to translating cultural items in African poetry from English to French.

Document similarity assessment plays an important role in various natural language processing (NLP) applications, such as information retrieval, plagiarism detection, recommendation systems, and question answering [11, 19]. For instance, in recommendation systems, document similarity helps personalise suggestions by finding content that closely matches user preference. These tasks rely on accurate measurements of how similar documents are in terms of their structure, content, and meaning, which depends on the way the document is represented computationally. This representation is usually done in vector format and is obtained via document embedding methods. V arious methodologies can be employed to obtain document-level embeddings, and the choice of method directly impacts the accuracy and usefulness of the similarity scores calculated [14, 19].

Large language models (LLMs) are trained from vast repositories of text authored by millions of distinct authors, reflecting an enormous diversity of human traits. While these models bear the potential to be used as approximations of human subjects in behavioral studies, prior efforts have been limited in steering model responses to match individual human users. In this work, we introduce "Anthology", a method for conditioning LLMs to particular virtual personas by harnessing open-ended life narratives, which we refer to as "backstories." We show that our methodology enhances the consistency and reliability of experimental outcomes while ensuring better representation of diverse sub-populations. Across three nationally representative human surveys conducted as part of Pew Research Center's American Trends Panel (ATP), we demonstrate that Anthology achieves up to 18% improvement in matching the response distributions of human respondents and 27% improvement in consistency metrics. Our code and generated backstories are available at https://github.com/CannyLab/anthology.

It was at this point 10 years ago that the future began. Obviously, I am referring to the almost simultaneous launch of the PlayStation 4 and Xbox One consoles in late 2013. These machines ushered in the era of universal high-definition gaming. They brought us into the cloud computing age, allowing games such as Forza Horizon and Titanfall to perform complex maths remotely, freeing up your processor for other tasks. They forged ahead into game streaming, allowing us to play retro games across broadband connections, and recognised the growing importance of sharing gameplay, including functions that made it easier to record and broadcast gaming experiences across social media and Twitch.

The ACL Anthology is an online repository that serves as a comprehensive collection of publications in the field of natural language processing (NLP) and computational linguistics (CL). This paper presents a tool called ``ACL Anthology Helper''. It automates the process of parsing and downloading papers along with their meta-information, which are then stored in a local MySQL database. This allows for efficient management of the local papers using a wide range of operations, including "where," "group," "order," and more. By providing over 20 operations, this tool significantly enhances the retrieval of literature based on specific conditions. Notably, this tool has been successfully utilised in writing a survey paper (Tang et al.,2022a). By introducing the ACL Anthology Helper, we aim to enhance researchers' ability to effectively access and organise literature from the ACL Anthology. This tool offers a convenient solution for researchers seeking to explore the ACL Anthology's vast collection of publications while allowing for more targeted and efficient literature retrieval.

In this paper, we collect an anthology of 100 visual stories from Visual imagery and language have long since complemented each authors who participated in our systematic creative process of improvised other in visual storytelling. From children's picture books to comics story-building based on image sequences. Following close and news articles, this multimedia nexus forms a complementary reading and thematic analysis of our anthology, we present five interplay between imagery and spoken or written language. While themes that characterize the variations found in this creative visual audiences often experience stories and pictures together, visual storytelling process: (1) Narrating What is in Vision vs. Envisioning; images alone can also operate as starting points--sources of creative (2) Dynamically Characterizing Entities/Objects; (3) Sensing inspiration--for authors to write stories [42]. Researchers have Experiential Information About the Scenery; (4) Modulating the found that visual thinking [5, 6] and drawing [3] can prompt storytelling Mood; (5) Encoding Narrative Biases. In understanding the varied from a multitude of perspectives as long as creativity is not ways that people derive stories from images, we offer considerations disturbed in the process [17]. This affirms how creative writing and for collecting story-driven training data to inform automatic visual imagery are interconnected such that stories can be derived story generation. In correspondence with each theme, we envision from images to culminate in creative visual storytelling.

South Korea's CJ OliveNetworks announced its artificial intelligence system has helped create an anthology of poems called 9 i. The Seoul-based system integrator's novel platform, named Oi Writer, has achieved the feat after machine-learning more than 30,000 poems from various genres. With a few keywords, a person can expect the system to generate a draft of a poem almost instantaneously. To get the AI poem generator ready, CJ OliveNetworks worked with nine poets over the past eight months. These nine poets utilized the generator to finalize 45 poems for the anthology, among which eight were by amateur poets from CJ OliveNetworks' research team.