Learning under Label Proportions for Text Classification

We present one of the preliminary NLP works under the challenging setup of Learning from Label Proportions (LLP), where the data is provided in an aggregate form called bags and only the proportion of samples in each class as the ground truth. This setup is inline with the desired characteristics of training models under Privacy settings and Weakly supervision. By characterizing some irregularities of the most widely used baseline technique DLLP, we propose a novel formulation that is also robust. This is accompanied with a learnability result that provides a generalization bound under LLP. Combining this formulation with a self-supervised objective, our method achieves better results as compared to the baselines in almost 87% of the experimental configurations which include large scale models for both long and short range texts across multiple metrics.Comment: accepted as long paper in Findings of EMNLP 202

The Jet Composition of GRB 230307A: Poynting-Flux-Dominated Outflow?

The jet composition of GRB plays an important role in understanding the energy dissipation and radiation mechanisms in GRB physics, but it is poorly constrained from the observational data. Recently, an interesting long-duration GRB 230307A with redshift $z=$0.065 has attracted great attention. The lack of detected thermal emission and mini-structure of prompt emission lightcurve of this burst suggest that the outflow is Poynting-flux-dominated and point towards the ICMART model. In this paper, we invoke two independent methods to investigate the jet composition of GRB 230307A. The high magnetization parameter ($\sigma>7$ or ever large) for$R_0=10^{10}$ cm that is used to suppress thermal component, strongly suggests that a significant fraction of the outflow energy is likely in a Poynting flux entrained with the baryonic matter. Moreover, it is found that the radiation efficiency of this burst for typical values $\epsilon_e=0.1$ and $\epsilon_B=0.01$ can reach as high as $~50\%$ which disfavors the internal shock model, but is consistent with ICMART model. Finally, a possible unified picture to produce GRB 230307A originated from a compact star merger is also discussed.Comment: 6 pages, 2 figures, 1 table, updated references, and matched with the published veriso

Is It Possible to Backdoor Face Forgery Detection with Natural Triggers?

Deep neural networks have significantly improved the performance of face forgery detection models in discriminating Artificial Intelligent Generated Content (AIGC). However, their security is significantly threatened by the injection of triggers during model training (i.e., backdoor attacks). Although existing backdoor defenses and manual data selection can mitigate those using human-eye-sensitive triggers, such as patches or adversarial noises, the more challenging natural backdoor triggers remain insufficiently researched. To further investigate natural triggers, we propose a novel analysis-by-synthesis backdoor attack against face forgery detection models, which embeds natural triggers in the latent space. We thoroughly study such backdoor vulnerability from two perspectives: (1) Model Discrimination (Optimization-Based Trigger): we adopt a substitute detection model and find the trigger by minimizing the cross-entropy loss; (2) Data Distribution (Custom Trigger): we manipulate the uncommon facial attributes in the long-tailed distribution to generate poisoned samples without the supervision from detection models. Furthermore, to completely evaluate the detection models towards the latest AIGC, we utilize both state-of-the-art StyleGAN and Stable Diffusion for trigger generation. Finally, these backdoor triggers introduce specific semantic features to the generated poisoned samples (e.g., skin textures and smile), which are more natural and robust. Extensive experiments show that our method is superior from three levels: (1) Attack Success Rate: ours achieves a high attack success rate (over 99%) and incurs a small model accuracy drop (below 0.2%) with a low poisoning rate (less than 3%); (2) Backdoor Defense: ours shows better robust performance when faced with existing backdoor defense methods; (3) Human Inspection: ours is less human-eye-sensitive from a comprehensive user study

STAR: Boosting Low-Resource Event Extraction by Structure-to-Text Data Generation with Large Language Models

Structure prediction tasks such as event extraction require an in-depth understanding of the output structure and sub-task dependencies, thus they still heavily rely on task-specific training data to obtain reasonable performance. Due to the high cost of human annotation, low-resource event extraction, which requires minimal human cost, is urgently needed in real-world information extraction applications. We propose to synthesize data instances given limited seed demonstrations to boost low-resource event extraction performance. We propose STAR, a structure-to-text data generation method that first generates complicated event structures (Y) and then generates input passages (X), all with Large Language Models. We design fine-grained step-by-step instructions and the error cases and quality issues identified through self-reflection can be self-refined. Our experiments indicate that data generated by STAR can significantly improve the low-resource event extraction performance and they are even more effective than human-curated data points in some cases

SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models

Recent advances in large language models (LLMs) have demonstrated notable progress on many mathematical benchmarks. However, most of these benchmarks only feature problems grounded in junior and senior high school subjects, contain only multiple-choice questions, and are confined to a limited scope of elementary arithmetic operations. To address these issues, this paper introduces an expansive benchmark suite SciBench that aims to systematically examine the reasoning capabilities required for complex scientific problem solving. SciBench contains two carefully curated datasets: an open set featuring a range of collegiate-level scientific problems drawn from mathematics, chemistry, and physics textbooks, and a closed set comprising problems from undergraduate-level exams in computer science and mathematics. Based on the two datasets, we conduct an in-depth benchmark study of two representative LLMs with various prompting strategies. The results reveal that current LLMs fall short of delivering satisfactory performance, with an overall score of merely 35.80%. Furthermore, through a detailed user study, we categorize the errors made by LLMs into ten problem-solving abilities. Our analysis indicates that no single prompting strategy significantly outperforms others and some strategies that demonstrate improvements in certain problem-solving skills result in declines in other skills. We envision that SciBench will catalyze further developments in the reasoning abilities of LLMs, thereby ultimately contributing to scientific research and discovery.Comment: Work in progress, 18 page

Anticancer drug nanomicelles formed by self-assembling amphiphilic dendrimer to combat cancer drug resistance

Drug resistance and toxicity constitute challenging hurdles for cancer therapy. The application of nanotechnology for anticancer drug delivery is expected to address these issues and bring new hope for cancer treatment. In this context, we established an original nanomicellar drug delivery system based on an amphiphilic dendrimer (AmDM), which could generate supramolecular micelles to effectively encapsulate the anticancer drug doxorubicin (DOX) with high drug-loading capacity (>40%), thanks to the unique dendritic structure creating large void space for drug accommodation. The resulting AmDM/DOX nanomicelles were able to enhance drug potency and combat doxorubicin resistance in breast cancer models by significantly enhancing cellular uptake while considerably decreasing efflux of the drug. In addition, the AmDM/DOX nanoparticles abolished significantly the toxicity related to the free drug. Collectively, our studies demonstrate that the drug delivery system based on nanomicelles formed with the self-assembling amphiphilic dendrimer constitutes a promising and effective drug carrier in cancer therapy