In an era increasingly influenced by artificial intelligence, the detection of fake news is crucial, especially in contexts like election seasons where misinformation can have significant societal impacts. This study evaluates the effectiveness of various LLMs in identifying and filtering fake news content. Utilizing a comparative analysis approach, we tested four large LLMs -- GPT-4, Claude 3 Sonnet, Gemini Pro 1.0, and Mistral Large -- and two smaller LLMs -- Gemma 7B and Mistral 7B. By using fake news dataset samples from Kaggle, this research not only sheds light on the current capabilities and limitations of LLMs in fake news detection but also discusses the implications for developers and policymakers in enhancing AI-driven informational integrity.

The task of Raga Identification is a very popular research problem in Music Information Retrieval. Few studies that have explored this task employed various approaches, such as signal processing, Machine Learning (ML) methods, and more recently Deep Learning (DL) based methods. However, a key question remains unanswered in all of these works: do these ML/DL methods learn and interpret Ragas in a manner similar to human experts? Besides, a significant roadblock in this research is the unavailability of ample supply of rich, labeled datasets, which drives these ML/DL based methods. In this paper, we introduce "Prasarbharti Indian Music" version-1 (PIM-v1), a novel dataset comprising of 191 hours of meticulously labeled Hindustani Classical Music (HCM) recordings, which is the largest labeled dataset for HCM recordings to the best of our knowledge. Our approach involves conducting ablation studies to find the benchmark classification model for Automatic Raga Identification (ARI) using PIM-v1 dataset. We achieve a chunk-wise f1-score of 0.89 for a subset of 12 Raga classes. Subsequently, we employ model explainability techniques to evaluate the classifier's predictions, aiming to ascertain whether they align with human understanding of Ragas or are driven by arbitrary patterns. We validate the correctness of model's predictions by comparing the explanations given by two ExAI models with human expert annotations. Following this, we analyze explanations for individual test examples to understand the role of regions highlighted by explanations in correct or incorrect predictions made by the model.

Meta reinforcement learning sets a distribution over a set of tasks on which the agent can train at will, then is asked to learn an optimal policy for any test task efficiently. In this paper, we consider a finite set of tasks modeled through Markov decision processes with various dynamics. We assume to have endured a long training phase, from which the set of tasks is perfectly recovered, and we focus on regret minimization against the optimal policy in the unknown test task. Under a separation condition that states the existence of a state-action pair revealing a task against another, Chen et al. (2022) show that $O(M^2 \log(H))$ regret can be achieved, where $M, H$ are the number of tasks in the set and test episodes, respectively. In our first contribution, we demonstrate that the latter rate is nearly optimal by developing a novel lower bound for test-time regret minimization under separation, showing that a linear dependence with $M$ is unavoidable. Then, we present a family of stronger yet reasonable assumptions beyond separation, which we call strong identifiability, enabling algorithms achieving fast rates $\log (H)$ and sublinear dependence with $M$ simultaneously. Our paper provides a new understanding of the statistical barriers of test-time regret minimization and when fast rates can be achieved.

While the Self-Attention mechanism in the Transformer model has proven to be effective in many domains, we observe that it is less effective in more diverse settings (e.g. multimodality) due to the varying granularity of each token and the high computational demands of lengthy sequences. To address the challenges, we introduce the Learnable Attention Mask (LAM), strategically designed to globally regulate attention maps and prioritize critical tokens within the sequence. Leveraging the Self-Attention module in a BERT-like transformer network, our approach adeptly captures associations between tokens. The extension of the LAM to a multi-layer version accommodates the varied information aspects embedded at each layer of the Transformer network. Comprehensive experimental validation on various datasets, such as MADv2, QVHighlights, ImageNet 1K, and MSRVTT, demonstrates the efficacy of the LAM, exemplifying its ability to enhance model performance while mitigating redundant computations. This pioneering approach presents a significant advancement in enhancing the understanding of complex scenarios, such as in movie understanding.

In this paper of a research based project, using Bidirectional Long Short-Term Memory (BiLSTM) networks, we provide a novel Fight Scene Detection (FSD) model which can be used for Movie Highlight Generation Systems (MHGS) based on deep learning and Neural Networks . Movies usually have Fight Scenes to keep the audience amazed. For trailer generation, or any other application of Highlight generation, it is very tidious to first identify all such scenes manually and then compile them to generate a highlight serving the purpose. Our proposed FSD system utilises temporal characteristics of the movie scenes and thus is capable to automatically identify fight scenes. Thereby helping in the effective production of captivating movie highlights. We observe that the proposed solution features 93.5% accuracy and is higher than 2D CNN with Hough Forests which being 92% accurate and is significantly higher than 3D CNN which features an accuracy of 65%.

We investigate the construction of latent spaces through self-supervised learning to support semantically meaningful operations. Analogous to operational amplifiers, these "operational latent spaces" (OpLaS) not only demonstrate semantic structure such as clustering but also support common transformational operations with inherent semantic meaning. Some operational latent spaces are found to have arisen "unintentionally" in the progress toward some (other) self-supervised learning objective, in which unintended but still useful properties are discovered among the relationships of points in the space. Other spaces may be constructed "intentionally" by developers stipulating certain kinds of clustering or transformations intended to produce the desired structure. We focus on the intentional creation of operational latent spaces via self-supervised learning, including the introduction of rotation operators via a novel "FiLMR" layer, which can be used to enable ring-like symmetries found in some musical constructions.

Diffusion models (DMs) produce very detailed and high-quality images. Their power results from extensive training on large amounts of data, usually scraped from the internet without proper attribution or consent from content creators. Unfortunately, this practice raises privacy and intellectual property concerns, as DMs can memorize and later reproduce their potentially sensitive or copyrighted training images at inference time. Prior efforts prevent this issue by either changing the input to the diffusion process, thereby preventing the DM from generating memorized samples during inference, or removing the memorized data from training altogether. While those are viable solutions when the DM is developed and deployed in a secure and constantly monitored environment, they hold the risk of adversaries circumventing the safeguards and are not effective when the DM itself is publicly released. To solve the problem, we introduce NeMo, the first method to localize memorization of individual data samples down to the level of neurons in DMs' cross-attention layers. Through our experiments, we make the intriguing finding that in many cases, single neurons are responsible for memorizing particular training samples. By deactivating these memorization neurons, we can avoid the replication of training data at inference time, increase the diversity in the generated outputs, and mitigate the leakage of private and copyrighted data. In this way, our NeMo contributes to a more responsible deployment of DMs.

Large-language Models (LLMs) have been extremely successful at tasks like complex dialogue understanding, reasoning and coding due to their emergent abilities. These emergent abilities have been extended with multi-modality to include image, audio, and video capabilities. Recommender systems, on the other hand, have been critical for information seeking and item discovery needs. Recently, there have been attempts to apply LLMs for recommendations. One difficulty of current attempts is that the underlying LLM is usually not trained on the recommender system data, which largely contains user interaction signals and is often not publicly available. Another difficulty is user interaction signals often have a different pattern from natural language text, and it is currently unclear if the LLM training setup can learn more non-trivial knowledge from interaction signals compared with traditional recommender system methods. Finally, it is difficult to train multiple LLMs for different use-cases, and to retain the original language and reasoning abilities when learning from recommender system data. To address these three limitations, we propose an Item-Language Model (ILM), which is composed of an item encoder to produce text-aligned item representations that encode user interaction signals, and a frozen LLM that can understand those item representations with preserved pretrained knowledge. We conduct extensive experiments which demonstrate both the importance of the language-alignment and of user interaction knowledge in the item encoder.

Recent advancements in hate speech detection (HSD) in Vietnamese have made significant progress, primarily attributed to the emergence of transformer-based pre-trained language models, particularly those built on the BERT architecture. However, the necessity for specialized fine-tuned models has resulted in the complexity and fragmentation of developing a multitasking HSD system. Moreover, most current methodologies focus on fine-tuning general pre-trained models, primarily trained on formal textual datasets like Wikipedia, which may not accurately capture human behavior on online platforms. In this research, we introduce ViHateT5, a T5-based model pre-trained on our proposed large-scale domain-specific dataset named VOZ-HSD. By harnessing the power of a text-to-text architecture, ViHateT5 can tackle multiple tasks using a unified model and achieve state-of-the-art performance across all standard HSD benchmarks in Vietnamese. Our experiments also underscore the significance of label distribution in pre-training data on model efficacy. We provide our experimental materials for research purposes, including the VOZ-HSD dataset, pre-trained checkpoint, the unified HSD-multitask ViHateT5 model, and related source code on GitHub publicly.

For entity recognition - including classification Named Entity Recognition (NER) and Relation and linking - and relation classification problems, Extraction (RE) allow for the extraction and categorization recent ZSL methods (Aly et al., 2021; Ledell Wu, of structured data from unstructured 2020; Chen and Li, 2021) rely on textual descriptions text, which in turn enables not only more accurate of entities or relations. Descriptions provide entity recognition and relationship extraction, but the required information about the semantics of entities also getting data from several unstructured sources, (or relations), which help the models to identify helping to build knowledge graphs and the semantic entity mentions in texts without observing them web. However, these methods usually rely on during training. Works such as (Ledell Wu, 2020; labeled data (usually human-annotated data) for a De Cao et al., 2021) and (Aly et al., 2021) show good performance, usually requiring domain experts how effective it is to use textual descriptions to perform for data acquisition and labeling, which may entity recognition tasks in the zero-shot context.