Reviewerly/exHarmony huggingface dataset:
Decoding News Narratives: A Critical Analysis of Large Language Models  in Framing Bias Detection

This work contributes to the expanding research on the applicability of LLMs in social sciences by examining the performance of GPT-3.5 Turbo, GPT-4, and Flan-T5 models in detecting framing bias in news headlines through zero-shot, few-shot, and explainable prompting methods. A key insight from our evaluation is the notable efficacy of explainable prompting in enhancing the reliability of these models, highlighting the importance of explainable settings for social science research on framing bias. GPT-4, in particular, demonstrated enhanced performance in few-shot scenarios when presented with a range of relevant, in-domain examples. FLAN-T5's poor performance indicates that smaller models may require additional task-specific fine-tuning for identifying framing bias detection. Our study also found that models, particularly GPT-4, often misinterpret emotional language as an indicator of framing bias, underscoring the challenge of distinguishing between reporting genuine emotional expression and intentionally use framing bias in news headlines. We further evaluated the models on two subsets of headlines where the presence or absence of framing bias was either clear-cut or more contested, with the results suggesting that these models' can be useful in flagging potential annotation inaccuracies within existing or new datasets. Finally, the study evaluates the models in real-world conditions ("in the wild"), moving beyond the initial dataset focused on U.S. Gun Violence, assessing the models' performance on framed headlines covering a broad range of topics.
Extensible Embedding: A Flexible Multipler For LLM's Context Length

Large language models (LLMs) call for extension of context to handle many critical applications. However, the existing approaches are prone to expensive costs and inferior quality of context extension. In this work, we propose Extensible Embedding, which realizes high-quality extension of LLM's context with strong flexibility and cost-effectiveness. Extensible embedding stand as an enhancement of typical token embedding, which represents the information for an extensible scope of context instead of a single token. By leveraging such compact input units of higher information density, the LLM can access to a vast scope of context even with a small context window. Extensible embedding is systematically optimized in architecture and training method, which leads to multiple advantages. 1) High flexibility of context extension, which flexibly supports ad-hoc extension of diverse context lengths. 2) Strong sample efficiency of training, which enables the embedding model to be learned in a cost-effective way. 3) Superior compatibility with the existing LLMs, where the extensible embedding can be seamlessly introduced as a plug-in component. Comprehensive evaluations on long-context language modeling and understanding tasks verify extensible embedding as an effective, efficient, flexible, and compatible method to extend the LLM's context.
Momentor: Advancing Video Large Language Model with Fine-Grained  Temporal Reasoning

Large Language Models (LLMs) demonstrate remarkable proficiency in comprehending and handling text-based tasks. Many efforts are being made to transfer these attributes to video modality, which are termed Video-LLMs. However, existing Video-LLMs can only capture the coarse-grained semantics and are unable to effectively handle tasks related to comprehension or localization of specific video segments. In light of these challenges, we propose Momentor, a Video-LLM capable of accomplishing fine-grained temporal understanding tasks. To support the training of Momentor, we design an automatic data generation engine to construct Moment-10M, a large-scale video instruction dataset with segment-level instruction data. We train Momentor on Moment-10M, enabling it to perform segment-level reasoning and localization. Zero-shot evaluations on several tasks demonstrate that Momentor excels in fine-grained temporally grounded comprehension and localization.
Large Language Models Fall Short: Understanding Complex Relationships in  Detective Narratives

Existing datasets for narrative understanding often fail to represent the complexity and uncertainty of relationships in real-life social scenarios. To address this gap, we introduce a new benchmark, Conan, designed for extracting and analysing intricate character relation graphs from detective narratives. Specifically, we designed hierarchical relationship categories and manually extracted and annotated role-oriented relationships from the perspectives of various characters, incorporating both public relationships known to most characters and secret ones known to only a few. Our experiments with advanced Large Language Models (LLMs) like GPT-3.5, GPT-4, and Llama2 reveal their limitations in inferencing complex relationships and handling longer narratives. The combination of the Conan dataset and our pipeline strategy is geared towards understanding the ability of LLMs to comprehend nuanced relational dynamics in narrative contexts.
BlendFilter: Advancing Retrieval-Augmented Large Language Models via  Query Generation Blending and Knowledge Filtering

Retrieval-augmented Large Language Models (LLMs) offer substantial benefits in enhancing performance across knowledge-intensive scenarios. However, these methods often face challenges with complex inputs and encounter difficulties due to noisy knowledge retrieval, notably hindering model effectiveness. To address this issue, we introduce BlendFilter, a novel approach that elevates retrieval-augmented LLMs by integrating query generation blending with knowledge filtering. BlendFilter proposes the blending process through its query generation method, which integrates both external and internal knowledge augmentation with the original query, ensuring comprehensive information gathering. Additionally, our distinctive knowledge filtering module capitalizes on the intrinsic capabilities of the LLM, effectively eliminating extraneous data. We conduct extensive experiments on three open-domain question answering benchmarks, and the findings clearly indicate that our innovative BlendFilter surpasses state-of-the-art baselines significantly.
ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs

With the development of foundation models such as large language models, zero-shot transfer learning has become increasingly significant. This is highlighted by the generative capabilities of NLP models like GPT-4, and the retrieval-based approaches of CV models like CLIP, both of which effectively bridge the gap between seen and unseen data. In the realm of graph learning, the continuous emergence of new graphs and the challenges of human labeling also amplify the necessity for zero-shot transfer learning, driving the exploration of approaches that can generalize across diverse graph data without necessitating dataset-specific and label-specific fine-tuning. In this study, we extend such paradigms to zero-shot transferability in graphs by introducing ZeroG, a new framework tailored to enable cross-dataset generalization. Addressing the inherent challenges such as feature misalignment, mismatched label spaces, and negative transfer, we leverage a language model to encode both node attributes and class semantics, ensuring consistent feature dimensions across datasets. We also propose a prompt-based subgraph sampling module that enriches the semantic information and structure information of extracted subgraphs using prompting nodes and neighborhood aggregation, respectively. We further adopt a lightweight fine-tuning strategy that reduces the risk of overfitting and maintains the zero-shot learning efficacy of the language model. The results underscore the effectiveness of our model in achieving significant cross-dataset zero-shot transferability, opening pathways for the development of graph foundation models. Especially, ZeroG, as a zero-shot method, can even achieve results comparable to those of semi-supervised learning on Pubmed.
PromptKD: Distilling Student-Friendly Knowledge for Generative Language  Models via Prompt Tuning

Recent advancements in large language models (LLMs) have raised concerns about inference costs, increasing the need for research into model compression. While knowledge distillation (KD) is a prominent method for this, research on KD for generative language models like LLMs is relatively sparse, and the approach of distilling student-friendly knowledge, which has shown promising performance in KD for classification models, remains unexplored in generative language models. To explore this approach, we propose PromptKD, a simple yet effective method that utilizes prompt tuning - for the first time in KD - to enable generative language models to transfer student-friendly knowledge. Unlike previous works in classification that require fine-tuning the entire teacher model for extracting student-friendly knowledge, PromptKD achieves similar effects by adding a small number of prompt tokens and tuning only the prompt with student guidance. Extensive experiments on instruction-following datasets using the GPT-2 model family show that PromptKD achieves state-of-the-art performance while adding only 0.0007% of the teacher's parameters as prompts. Further analysis suggests that distilling student-friendly knowledge alleviates exposure bias effectively throughout the entire training process, leading to performance enhancements.
A New Chaos-Based Image Encryption Algorithm Based on Discrete Fourier Transform and Improved Joseph Traversal

To further enhance the security of image encryption, a new chaos-based image encryption algorithm (IEA) based on discrete Fourier transform and Joseph traversal is proposed to encrypt the plain image in both the frequency domain and space domain simultaneously. In the proposed IEA, the logistic map is used to generate the appropriate chaotic sequence, and the improved Joseph traversal is used to scramble the image in different starting positions and variable step sizes. Then, block diffusion is performed at the end. The main finding concerning the proposed IEA is that the combination of discrete Fourier transform and Joseph traversal can enhance the security of the image information, which has been validated by measuring the performance in resisting the common types of attacks.
Target Speech Extraction with Pre-trained Self-supervised Learning  Models

Pre-trained self-supervised learning (SSL) models have achieved remarkable success in various speech tasks. However, their potential in target speech extraction (TSE) has not been fully exploited. TSE aims to extract the speech of a target speaker in a mixture guided by enrollment utterances. We exploit pre-trained SSL models for two purposes within a TSE framework, i.e., to process the input mixture and to derive speaker embeddings from the enrollment. In this paper, we focus on how to effectively use SSL models for TSE. We first introduce a novel TSE downstream task following the SUPERB principles. This simple experiment shows the potential of SSL models for TSE, but extraction performance remains far behind the state-of-the-art. We then extend a powerful TSE architecture by incorporating two SSL-based modules: an Adaptive Input Enhancer (AIE) and a speaker encoder. Specifically, the proposed AIE utilizes intermediate representations from the CNN encoder by adjusting the time resolution of CNN encoder and transformer blocks through progressive upsampling, capturing both fine-grained and hierarchical features. Our method outperforms current TSE systems achieving a SI-SDR improvement of 14.0 dB on LibriMix. Moreover, we can further improve performance by 0.7 dB by fine-tuning the whole model including the SSL model parameters.
Probing Self-supervised Learning Models with Target Speech Extraction

Large-scale pre-trained self-supervised learning (SSL) models have shown remarkable advancements in speech-related tasks. However, the utilization of these models in complex multi-talker scenarios, such as extracting a target speaker in a mixture, is yet to be fully evaluated. In this paper, we introduce target speech extraction (TSE) as a novel downstream task to evaluate the feature extraction capabilities of pre-trained SSL models. TSE uniquely requires both speaker identification and speech separation, distinguishing it from other tasks in the Speech processing Universal PERformance Benchmark (SUPERB) evaluation. Specifically, we propose a TSE downstream model composed of two lightweight task-oriented modules based on the same frozen SSL model. One module functions as a speaker encoder to obtain target speaker information from an enrollment speech, while the other estimates the target speaker's mask to extract its speech from the mixture. Experimental results on the Libri2mix datasets reveal the relevance of the TSE downstream task to probe SSL models, as its performance cannot be simply deduced from other related tasks such as speaker verification and separation.
Analysis of Using Sigmoid Loss for Contrastive Learning

Contrastive learning has emerged as a prominent branch of self-supervised learning for several years. Especially, CLIP, which applies contrastive learning to large sets of captioned images, has garnered significant attention. Recently, SigLIP, a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss. SigLIP achieves the performance comparable to CLIP in a more efficient manner by eliminating the need for a global view. However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored. In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning, in the perspective of the geometric structure of learned embeddings. First, we propose the double-Constant Embedding Model (CCEM), a framework for parameterizing various well-known embedding structures by a single variable. Interestingly, the proposed CCEM is proven to contain the optimal embedding with respect to the sigmoid loss. Second, we mathematically analyze the optimal embedding minimizing the sigmoid loss for contrastive learning. The optimal embedding ranges from simplex equiangular-tight-frame to antipodal structure, depending on the temperature parameter used in the sigmoid loss. Third, our experimental results on synthetic datasets coincide with the theoretical results on the optimal embedding structures.
Quantum Embedding with Transformer for High-dimensional Data

Quantum embedding with transformers is a novel and promising architecture for quantum machine learning to deliver exceptional capability on near-term devices or simulators. The research incorporated a vision transformer (ViT) to advance quantum significantly embedding ability and results for a single qubit classifier with around 3 percent in the median F1 score on the BirdCLEF-2021, a challenging high-dimensional dataset. The study showcases and analyzes empirical evidence that our transformer-based architecture is a highly versatile and practical approach to modern quantum machine learning problems.
Me LLaMA: Foundation Large Language Models for Medical Applications

Recent large language models (LLMs) like ChatGPT and LLaMA have shown great promise in many AI applications. However, their performance on medical tasks is suboptimal and can be further improved by training on large domain-specific datasets. This study introduces Me LLaMA, a medical LLM family including foundation models - Me LLaMA 13/70B and their chat-enhanced versions - Me LLaMA 13/70B-chat, developed through the continual pre-training and instruction tuning of LLaMA2 using large medical data. Our domain-specific data suite for training and evaluation, includes a large-scale continual pre-training dataset with 129B tokens, an instruction tuning dataset with 214k samples, and a medical evaluation benchmark (MIBE) across six tasks with 14 datasets. Our extensive evaluation using MIBE shows that Me LLaMA models surpass existing open-source medical LLMs in zero-shot and few-shot learning and outperform commercial giants like ChatGPT on 6 out of 8 datasets and GPT-4 in 3 out of 8 datasets. In addition, we empirically investigated the catastrophic forgetting problem, and our results show that Me LLaMA models outperform other medical LLMs. Me LLaMA is one of the first and largest open-source foundational LLMs designed for the medical domain, using both biomedical and clinical data. It exhibits superior performance across both general and medical tasks compared to other medical LLMs, rendering it an attractive choice for medical AI applications. All resources are available at: https://github.com/BIDS-Xu-Lab/Me-LLaMA.
Efektivitas Layanan Informasi dengan Format Klasikal Menggunakan Metode Small Group Discussion untuk Meningkatkan Self-Efficacy Akademik Siswa SMA Negeri 10 Padang

Academic self-efficacy is one of the many factors that can affect student learning achievement. Someone who has good academic self-efficacy, will feel that he is always ready and alert in completing his academic tasks without any doubts about his condition. in completing their academic tasks without any doubts about their own condition. This study aims to see the effectiveness of the implementation of information services with a classical format using the small group discussion method to improve students' academic self-efficacy. This research uses a quantitative approach, with the type of research Quasi Experiment design The Non-Equivalent Control Group. The population in this study were Phase E students of SMA Negeri 10 Padang, sampling using purposive sampling method, then obtained samples of Phase E 4 and Phase E 9 classes. The technique used in data collection is by distributing questionnaires, then analyzed using the Wilcoxon Signed Ranks Test, and Mann-Whitney U 2 Independent Sample Test. The results of this study indicate that the implementation of information services with a classical format using the small group discussion method is effective in increasing students' academic self-efficacy. Specifically, the use of this method increases better academic self-efficacy.
Healthcare : A Transformer Network Based Chatbot

Medical chatbots are becoming more and more common as the healthcare industry changes and places more and more focus on automated and remote services. These digital assistants provide cost savings, prompt answers to healthcare inquiries, and access to medical guidance around-the-clock. The effectiveness of these chatbots depends on how well-informed they are about healthcare. Our project's main goal is to employ Transformer network architecture to create a sophisticated chatbot for healthcare. It includes gathering data, choosing a model, recognizing intent and entities, managing conversations, producing responses, integrating knowledge databases, creating user-friendly designs, doing thorough testing, adhering to regulations, and continuously improving. Our objective is to develop a smart conversational agent that can respond to a broad range of healthcare inquiries, provide advice and recommendations tailored to individual diseases, and eventually improve patient satisfaction and healthcare experiences
ELAD: Explanation-Guided Large Language Models Active Distillation

The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences. Traditional distillation methods, which transfer the capabilities of LLMs to smaller models, often fail to determine whether the knowledge has been sufficiently transferred, potentially resulting in high costs or incomplete distillation. In this paper, we propose an Explanation-Guided LLMs Active Distillation (ELAD) framework that employs an active learning strategy to optimize the balance between annotation costs and model performance. To improve efficient sample selection, we introduce an explanation-guided sample selection method that identifies samples challenging its reasoning by exploiting uncertainties in explanation steps. Additionally, we present a customized LLM-annotated explanation revision technique where the teacher model detects and corrects flaws in the student model's reasoning. Our experiments across various reasoning datasets demonstrate that our framework significantly enhances the efficiency of LLM knowledge distillation.
General multi-attack detection for continuous-variable quantum key distribution with local local oscillator

Abstract Continuous-variable quantum key distribution with a local local oscillator(LLO CVQKD) has been extensively researched due to its simplicity and security. For the practical security of the LLO CVQKD system, there are two main attack modes referred to as reference pulse attack and polarization attack presently. However, there is currently no general defense strategy against such attacks and the security of the system needs further investigation. Here, we employ a deep learning framework called generative adversarial networks (GANs) to detect both attacks. We first analyze the data in different cases, derive a feature vector as input to a GAN model, and then show the training and testing process of the GAN model for attack classification. The proposed model has two parts, a discriminator and a generator, both of which employ a convolutional neural network (CNN) to improve accuracy. Simulation results show that the proposed scheme can detect and classify attacks without reducing the secret key rate and the maximum transmission distance. It only establishes a detection model by monitoring features of the pulse without adding additional devices.
A Bibliometric Analysis of Industry-Education Integration Research Based on Web of Science

This study aims to gain a deeper understanding of the research dynamics and trends in industry-education integration through a quantitative analysis of the literature in this field. Eight hundred ninety-five relevant articles were retrieved from the Web of Science Core Collection database, and the research focused on the annual publication trends, prominent authors, institutions and countries, highly cited articles, keywords, and collaborative networks. The study found that the field of industry-education integration has undergone three stages. Secondly, researchers from China and the United States ranked first and second in the number of publications in this field. Thirdly, highly cited articles have a particular influence on industry-education integration, but the overall citation count is relatively limited. Fourthly, keywords such as “cooperative education,” “students,” and “performance” are hot topics in this field. Finally, the analysis of collaborative networks revealed the existence of multiple communities, but the overall scale of collaboration is relatively small, and the influence is limited. This study provides profound insights into the academic research in industry-education integration. It helps scholars, policymakers, and practitioners better understand this field’s development trends.
Supplemental Material: Genetic association between carbonates and gold precipitation mechanisms in the Jinshan deposit, eastern Jiangnan orogen

&lt;p&gt;Thermodynamic database for Geochemist's Workbench (GWB).&lt;/p&gt;