chore: update confs

Author: actions-user

arxiv.json

@@ -42978,5 +42978,40 @@
         "pub_date": "2025-03-18",
         "summary": "The ever growing realism and quality of generated videos makes it increasingly harder for humans to spot deepfake content, who need to rely more and more on automatic deepfake detectors. However, deepfake detectors are also prone to errors, and their decisions are not explainable, leaving humans vulnerable to deepfake-based fraud and misinformation. To this end, we introduce ExDDV, the first dataset and benchmark for Explainable Deepfake Detection in Video. ExDDV comprises around 5.4K real and deepfake videos that are manually annotated with text descriptions (to explain the artifacts) and clicks (to point out the artifacts). We evaluate a number of vision-language models on ExDDV, performing experiments with various fine-tuning and in-context learning strategies. Our results show that text and click supervision are both required to develop robust explainable models for deepfake videos, which are able to localize and describe the observed artifacts. Our novel dataset and code to reproduce the results are available at https://github.com/vladhondru25/ExDDV.",
         "translated": "随着生成视频的真实感和质量不断提高，人类越来越难以识别深度伪造内容，因此需要更多地依赖自动化的深度伪造检测器。然而，深度伪造检测器也容易出错，并且它们的决策缺乏可解释性，这使得人类在面对基于深度伪造的欺诈和虚假信息时更加脆弱。为此，我们引入了ExDDV，这是首个用于视频中可解释深度伪造检测的数据集和基准。ExDDV包含约5.4K真实和深度伪造视频，这些视频通过人工标注了文本描述（用于解释伪造痕迹）和点击（用于指出伪造痕迹）。我们在ExDDV上评估了多种视觉-语言模型，并进行了多种微调和上下文学习策略的实验。我们的结果表明，文本和点击监督都是开发鲁棒的可解释深度伪造视频模型所必需的，这些模型能够定位并描述观察到的伪造痕迹。我们新颖的数据集和用于复现结果的代码可在https://github.com/vladhondru25/ExDDV获取。"
+    },
+    {
+        "title": "When Pigs Get Sick: Multi-Agent AI for Swine Disease Detection",
+        "url": "http://arxiv.org/abs/2503.15204v1",
+        "pub_date": "2025-03-19",
+        "summary": "Swine disease surveillance is critical to the sustainability of global agriculture, yet its effectiveness is frequently undermined by limited veterinary resources, delayed identification of cases, and variability in diagnostic accuracy. To overcome these barriers, we introduce a novel AI-powered, multi-agent diagnostic system that leverages Retrieval-Augmented Generation (RAG) to deliver timely, evidence-based disease detection and clinical guidance. By automatically classifying user inputs into either Knowledge Retrieval Queries or Symptom-Based Diagnostic Queries, the system ensures targeted information retrieval and facilitates precise diagnostic reasoning. An adaptive questioning protocol systematically collects relevant clinical signs, while a confidence-weighted decision fusion mechanism integrates multiple diagnostic hypotheses to generate robust disease predictions and treatment recommendations. Comprehensive evaluations encompassing query classification, disease diagnosis, and knowledge retrieval demonstrate that the system achieves high accuracy, rapid response times, and consistent reliability. By providing a scalable, AI-driven diagnostic framework, this approach enhances veterinary decision-making, advances sustainable livestock management practices, and contributes substantively to the realization of global food security.",
+        "translated": "猪病监测对全球农业的可持续性至关重要，但其有效性常常因有限的兽医资源、病例识别的延迟以及诊断准确性的不一致而受到削弱。为了克服这些障碍，我们引入了一种新型的基于人工智能的多代理诊断系统，该系统利用检索增强生成（Retrieval-Augmented Generation, RAG）技术，提供及时、基于证据的疾病检测和临床指导。通过自动将用户输入分类为知识检索查询或基于症状的诊断查询，该系统确保了有针对性的信息检索，并促进了精确的诊断推理。自适应提问协议系统地收集相关临床症状，而基于置信度加权的决策融合机制则整合多个诊断假设，以生成稳健的疾病预测和治疗建议。涵盖查询分类、疾病诊断和知识检索的综合评估表明，该系统具有高准确性、快速响应时间和一致的可靠性。通过提供一个可扩展的、人工智能驱动的诊断框架，该方法增强了兽医决策能力，推动了可持续的畜牧管理实践，并为实现全球粮食安全做出了实质性贡献。"
+    },
+    {
+        "title": "Optimizing Retrieval Strategies for Financial Question Answering\n  Documents in Retrieval-Augmented Generation Systems",
+        "url": "http://arxiv.org/abs/2503.15191v1",
+        "pub_date": "2025-03-19",
+        "summary": "Retrieval-Augmented Generation (RAG) has emerged as a promising framework to mitigate hallucinations in Large Language Models (LLMs), yet its overall performance is dependent on the underlying retrieval system. In the finance domain, documents such as 10-K reports pose distinct challenges due to domain-specific vocabulary and multi-hierarchical tabular data. In this work, we introduce an efficient, end-to-end RAG pipeline that enhances retrieval for financial documents through a three-phase approach: pre-retrieval, retrieval, and post-retrieval. In the pre-retrieval phase, various query and corpus preprocessing techniques are employed to enrich input data. During the retrieval phase, we fine-tuned state-of-the-art (SOTA) embedding models with domain-specific knowledge and implemented a hybrid retrieval strategy that combines dense and sparse representations. Finally, the post-retrieval phase leverages Direct Preference Optimization (DPO) training and document selection methods to further refine the results. Evaluations on seven financial question answering datasets-FinDER, FinQABench, FinanceBench, TATQA, FinQA, ConvFinQA, and MultiHiertt-demonstrate substantial improvements in retrieval performance, leading to more accurate and contextually appropriate generation. These findings highlight the critical role of tailored retrieval techniques in advancing the effectiveness of RAG systems for financial applications. A fully replicable pipeline is available on GitHub: https://github.com/seohyunwoo-0407/GAR.",
+        "translated": "检索增强生成（Retrieval-Augmented Generation, RAG）作为一种有前景的框架，已显示出在减少大型语言模型（LLMs）幻觉方面的潜力，但其整体性能依赖于底层的检索系统。在金融领域，诸如10-K报告之类的文档由于领域特定的词汇和多层次表格数据而带来了独特的挑战。在本研究中，我们引入了一种高效的端到端RAG流程，通过三个阶段的方法增强了对金融文档的检索：检索前、检索和检索后。在检索前阶段，采用了多种查询和语料库预处理技术来丰富输入数据。在检索阶段，我们通过领域特定知识微调了最先进的（SOTA）嵌入模型，并实施了一种结合密集和稀疏表示的混合检索策略。最后，在检索后阶段，利用直接偏好优化（Direct Preference Optimization, DPO）训练和文档选择方法进一步精炼结果。在七个金融问答数据集——FinDER、FinQABench、FinanceBench、TATQA、FinQA、ConvFinQA和MultiHiertt上的评估显示，检索性能有显著提升，从而实现了更准确和上下文更合适的生成。这些发现强调了定制检索技术在提高RAG系统在金融应用中的有效性方面的关键作用。完整的可复制流程可在GitHub上获取：https://github.com/seohyunwoo-0407/GAR。"
+    },
+    {
+        "title": "Pseudo-Relevance Feedback Can Improve Zero-Shot LLM-Based Dense\n  Retrieval",
+        "url": "http://arxiv.org/abs/2503.14887v1",
+        "pub_date": "2025-03-19",
+        "summary": "Pseudo-relevance feedback (PRF) refines queries by leveraging initially retrieved documents to improve retrieval effectiveness. In this paper, we investigate how large language models (LLMs) can facilitate PRF for zero-shot LLM-based dense retrieval, extending the recently proposed PromptReps method. Specifically, our approach uses LLMs to extract salient passage features-such as keywords and summaries-from top-ranked documents, which are then integrated into PromptReps to produce enhanced query representations. Experiments on passage retrieval benchmarks demonstrate that incorporating PRF significantly boosts retrieval performance. Notably, smaller rankers with PRF can match the effectiveness of larger rankers without PRF, highlighting PRF's potential to improve LLM-driven search while maintaining an efficient balance between effectiveness and resource usage.",
+        "translated": "伪相关反馈（Pseudo-relevance feedback, PRF）通过利用初始检索到的文档来优化查询，从而提高检索效果。本文探讨了如何利用大型语言模型（LLMs）来促进基于零样本LLM的密集检索中的PRF，扩展了最近提出的PromptReps方法。具体而言，我们的方法使用LLMs从排名靠前的文档中提取关键段落特征（如关键词和摘要），然后将这些特征整合到PromptReps中，以生成增强的查询表示。在段落检索基准上的实验表明，引入PRF显著提升了检索性能。值得注意的是，带有PRF的较小排序器能够与不带PRF的较大排序器的效果相媲美，这突显了PRF在提高LLM驱动搜索效果的同时，保持了效果与资源使用之间的高效平衡的潜力。"
+    },
+    {
+        "title": "Scaled Supervision is an Implicit Lipschitz Regularizer",
+        "url": "http://arxiv.org/abs/2503.14813v1",
+        "pub_date": "2025-03-19",
+        "summary": "In modern social media, recommender systems (RecSys) rely on the click-through rate (CTR) as the standard metric to evaluate user engagement. CTR prediction is traditionally framed as a binary classification task to predict whether a user will interact with a given item. However, this approach overlooks the complexity of real-world social modeling, where the user, item, and their interactive features change dynamically in fast-paced online environments. This dynamic nature often leads to model instability, reflected in overfitting short-term fluctuations rather than higher-level interactive patterns. While overfitting calls for more scaled and refined supervisions, current solutions often rely on binary labels that overly simplify fine-grained user preferences through the thresholding process, which significantly reduces the richness of the supervision. Therefore, we aim to alleviate the overfitting problem by increasing the supervision bandwidth in CTR training. Specifically, (i) theoretically, we formulate the impact of fine-grained preferences on model stability as a Lipschitz constrain; (ii) empirically, we discover that scaling the supervision bandwidth can act as an implicit Lipschitz regularizer, stably optimizing existing CTR models to achieve better generalizability. Extensive experiments show that this scaled supervision significantly and consistently improves the optimization process and the performance of existing CTR models, even without the need for additional hyperparameter tuning.",
+        "translated": "在现代社交媒体中，推荐系统（RecSys）依赖点击率（CTR）作为评估用户参与度的标准指标。CTR预测传统上被定义为一个二分类任务，用于预测用户是否会与给定的项目进行交互。然而，这种方法忽视了现实世界社交建模的复杂性，其中用户、项目以及它们的交互特征在快节奏的在线环境中动态变化。这种动态特性常常导致模型的不稳定性，表现为过度拟合短期波动而非更高层次的交互模式。尽管过度拟合需要更多规模和精细的监督，当前的解决方案通常依赖于二值标签，这些标签通过阈值化过程过度简化了细粒度的用户偏好，从而显著降低了监督的丰富性。因此，我们旨在通过增加CTR训练中的监督带宽来缓解过度拟合问题。具体来说，（i）理论上，我们将细粒度偏好对模型稳定性的影响表述为Lipschitz约束；（ii）实证上，我们发现扩展监督带宽可以作为一种隐式的Lipschitz正则化器，稳定地优化现有的CTR模型以实现更好的泛化能力。大量实验表明，这种扩展的监督显著且持续地改善了优化过程和现有CTR模型的性能，甚至无需额外的超参数调优。"
+    },
+    {
+        "title": "Graph-Based Re-ranking: Emerging Techniques, Limitations, and\n  Opportunities",
+        "url": "http://arxiv.org/abs/2503.14802v1",
+        "pub_date": "2025-03-19",
+        "summary": "Knowledge graphs have emerged to be promising datastore candidates for context augmentation during Retrieval Augmented Generation (RAG). As a result, techniques in graph representation learning have been simultaneously explored alongside principal neural information retrieval approaches, such as two-phased retrieval, also known as re-ranking. While Graph Neural Networks (GNNs) have been proposed to demonstrate proficiency in graph learning for re-ranking, there are ongoing limitations in modeling and evaluating input graph structures for training and evaluation for passage and document ranking tasks. In this survey, we review emerging GNN-based ranking model architectures along with their corresponding graph representation construction methodologies. We conclude by providing recommendations on future research based on community-wide challenges and opportunities.",
+        "translated": "知识图谱已成为在检索增强生成（Retrieval Augmented Generation, RAG）过程中用于上下文增强的有前景的数据存储候选。因此，图表示学习技术与主要的神经信息检索方法（如两阶段检索，也称为重排序）同时得到了探索。尽管图神经网络（Graph Neural Networks, GNNs）已被提出并展示出在图学习中进行重排序的熟练能力，但在为段落和文档排序任务建模和评估输入图结构方面仍存在持续的局限性。在本综述中，我们回顾了新兴的基于GNN的排序模型架构及其相应的图表示构建方法。最后，我们基于社区范围内的挑战和机遇，为未来的研究提供了建议。"
     }
 ]