Identifying key product features that influence consumer preferences is essential in the fashion industry. In this study, we introduce a robust methodology to ascertain the most impactful features in fashion product images, utilizing past market sales data. First, we propose the metric called "influence score" to quantitatively assess the importance of product features. Then we develop a forecasting model, the Fashion Demand Predictor (FDP), which integrates Transformer-based models and Random Forest to predict market popularity based on product images. We employ image-editing diffusion models to modify these images and perform an ablation study, which validates the impact of the highest and lowest-scoring features on the model's popularity predictions. Additionally, we further validate these results through surveys that gather human rankings of preferences, confirming the accuracy of the FDP model's predictions and the efficacy of our method in identifying influential features. Notably, products enhanced with "good" features show marked improvements in predicted popularity over their modified counterparts. Our approach develops a fully automated and systematic framework for fashion image analysis that provides valuable guidance for downstream tasks such as fashion product design and marketing strategy development.

Large Language Models (LLMs) exhibit significant persuasion capabilities in one-on-one interactions, but their influence within social networks remains underexplored. This study investigates the potential social impact of LLMs in these environments, where interconnected users and complex opinion dynamics pose unique challenges. In particular, we address the following research question: can LLMs learn to generate meaningful content that maximizes user engagement on social networks? To answer this question, we define a pipeline to guide the LLM-based content generation which employs reinforcement learning with simulated feedback. In our framework, the reward is based on an engagement model borrowed from the literature on opinion dynamics and information propagation. Moreover, we force the text generated by the LLM to be aligned with a given topic and to satisfy a minimum fluency requirement. Using our framework, we analyze the capabilities and limitations of LLMs in tackling the given task, specifically considering the relative positions of the LLM as an agent within the social network and the distribution of opinions in the network on the given topic. Our findings show the full potential of LLMs in creating social engagement. Notable properties of our approach are that the learning procedure is adaptive to the opinion distribution of the underlying network and agnostic to the specifics of the engagement model, which is embedded as a plug-and-play component. In this regard, our approach can be easily refined for more complex engagement tasks and interventions in computational social science. The code used for the experiments is publicly available at https://anonymous.4open.science/r/EDCG/.

Astrophysicist Stephen Hawking told Last Week Tonight's John Oliver a chilling but memorable hypothetical story a decade ago about the potential dangers of AI. The gist is a group of scientists build a superintelligent computer and ask it, "Is there a God?" The computer answers, "There is now" and a bolt of lightning zaps the plug preventing it from being shut down. Let's hope that's not what happened with OpenAI and some missing evidence from the New York Times' plagiarism lawsuit. Wired reported that a court declaration filed by the New York Times on Wednesday says that OpenAI's engineers accidentally erased evidence of the AI's training data that took a long time to research and compile.

This week, the Times alleged that OpenAI's engineers inadvertently erased data the paper's team spent more than 150 hours extracting as potential evidence. OpenAI was able to recover much of the data, but the Times' legal team says it's still missing the original file names and folder structure. According to a declaration filed to the court Wednesday by Jennifer B. Maisel, a lawyer for the newspaper, this means the information "cannot be used to determine where the news plaintiffs' copied articles" may have been incorporated into OpenAI's artificial intelligence models. "We disagree with the characterizations made and will file our response soon," OpenAI spokesperson Jason Deutrom told WIRED in a statement. The New York Times declined to comment.

The first paper describes how OpenAI directs an extensive network of human testers outside the company to vet the behavior of its models before they are released. The second paper presents a new way to automate parts of the testing process, using a large language model like GPT-4 to come up with novel ways to bypass its own guardrails. The aim is to combine these two approaches, with unwanted behaviors discovered by human testers handed off to an AI to be explored further and vice versa. Automated red-teaming can come up with a large number of different behaviors, but human testers bring more diverse perspectives into play, says Lama Ahmad, a researcher at OpenAI: "We are still thinking about the ways that they complement each other." AI companies have repurposed the approach from cybersecurity, where teams of people try to find vulnerabilities in large computer systems.

AI is a technology like no other in human history,

Recent work on GPT [1], BERT [2], and related models has proven immensely successful, not only in direct language modeling tasks but also other domains including translation, question answering, and even code [3] and music [4] generation. In addition to directly performing transfer learning, prompt engineering has emerged as a promising method to leverage the power of large language models trained on diverse types of texts [5, 6]. The general strategy of pretraining large models on easily-gathered unlabeled data using self-supervised tasks and then fine-tuning on more relevant labeled data is especially appealing for many scientific domains where labeled data may be difficult to come by. In materials physics, it is well understood how structure plays a significant role in electrical, thermal, or mechanical properties of a material, and scientists target particular structures as they design new materials for desired applications. For crystals, "structure" typically refers to the basic building unit which is repeated along a periodic lattice to create a bulk crystal, but--particularly for aperiodic or non-crystalline materials--it can also refer to any symmetry or non-random ordering present in the arrangements of particles or atoms. Assessing order and its evolution in three-dimensional structures is a challenging, but critical method for understanding the self-assembly and growth of complex materials; particularly as the scope and magnitude of experiment and simulation data analysis continues to expand, machine learning techniques that are able to leverage large amounts of unlabeled data will become ever more crucial. In this work, we use self-supervised learning (SSL) tasks that can broadly be used to train models for quantifying order and distinguishing assemblies in non-idealized material structures. The choice of SSL for this application was inspired by previous work that has developed SSL tasks for three-dimensional point clouds, which are a natural choice for representing three-dimensional positional data. Thabet et al. [7] formulated self-supervised tasks in terms of a space-filling curve; Sharma and Kaul [8] trained deep networks to model data based on a three-dimensional cover tree; the method proposed in Eckart et al. [9] models simple, soft "patches" of 3D point clouds in order to reconstruct its inputs; and Pang et al. [10] spatially mask

Large Language Models (LLMs) demonstrate exceptional reasoning capabilities, often achieving state-of-the-art performance in various tasks. However, their substantial computational and memory demands, due to billions of parameters, hinder deployment in resource-constrained environments. A promising solution is knowledge distillation, where LLMs transfer reasoning capabilities to Small Language Models (SLMs, $\le$ 1B parameters), enabling wider deployment on low-resource devices. Existing methods primarily focus on generating high-quality reasoning rationales for distillation datasets but often neglect the critical role of data quantity and quality. To address these challenges, we propose a Feedback-Driven Distillation (FDD) framework to enhance SLMs' mathematical reasoning capabilities. In the initialization stage, a distillation dataset is constructed by prompting LLMs to pair mathematical problems with corresponding reasoning rationales. We classify problems into easy and hard categories based on SLM performance. For easy problems, LLMs generate more complex variations, while for hard problems, new questions of similar complexity are synthesized. In addition, we propose a multi-round distillation paradigm to iteratively enrich the distillation datasets, thereby progressively improving the mathematical reasoning abilities of SLMs. Experimental results demonstrate that our method can make SLMs achieve SOTA mathematical reasoning performance.

Answering end user security questions is challenging. While large language models (LLMs) like GPT, LLAMA, and Gemini are far from error-free, they have shown promise in answering a variety of questions outside of security. We studied LLM performance in the area of end user security by qualitatively evaluating 3 popular LLMs on 900 systematically collected end user security questions. While LLMs demonstrate broad generalist ``knowledge'' of end user security information, there are patterns of errors and limitations across LLMs consisting of stale and inaccurate answers, and indirect or unresponsive communication styles, all of which impacts the quality of information received. Based on these patterns, we suggest directions for model improvement and recommend user strategies for interacting with LLMs when seeking assistance with security.

We present two comprehensive benchmarks to evaluate the performance of language models in coding assistance tasks, covering code writing, debugging, code review, and conceptual understanding. Our main contribution includes two curated datasets: StackEval, a large-scale benchmark derived from Stack Overflow questions, and StackUnseen, a dynamic benchmark featuring the most recent Stack Overflow content. These benchmarks offer novel insights into the capabilities and limitations of LLMs, particularly in handling new and emerging content. Additionally, we assess LLMs' proficiency as judges for coding tasks using a curated, human-annotated dataset, exploring their evaluation capabilities and potential biases, including whether they favor their own generated solutions. Our findings underscore the potential of these benchmarks to advance LLM development and application in coding assistance.