chore: update confs

Author: actions-user

arxiv.json

@@ -37014,5 +37014,47 @@
         "pub_date": "2024-12-16",
         "summary": "Session-based recommendation seeks to forecast the next item a user will be interested in, based on their interaction sequences. Due to limited interaction data, session-based recommendation faces the challenge of limited data availability. Traditional methods enhance feature learning by constructing complex models to generate positive and negative samples. This paper proposes a session-based recommendation model using Single Positive optimization loss and Graph Learning (SPGL) to deal with the problem of data sparsity, high model complexity and weak transferability. SPGL utilizes graph convolutional networks to generate global item representations and batch session representations, effectively capturing intrinsic relationships between items. The use of single positive optimization loss improves uniformity of item representations, thereby enhancing recommendation accuracy. In the intent extractor, SPGL considers the hop count of the adjacency matrix when constructing the directed global graph to fully integrate spatial information. It also takes into account the reverse positional information of items when constructing session representations to incorporate temporal information. Comparative experiments across three benchmark datasets, Tmall, RetailRocket and Diginetica, demonstrate the model's effectiveness. The source code can be accessed on https://github.com/liang-tian-tian/SPGL .",
         "translated": "基于会话的推荐旨在根据用户的交互序列预测其下一个感兴趣的项目。由于交互数据有限，基于会话的推荐面临数据可用性有限的挑战。传统方法通过构建复杂模型来生成正负样本，从而增强特征学习。本文提出了一种使用单正优化损失和图学习（SPGL）的基于会话的推荐模型，以解决数据稀疏性、模型复杂性高和迁移能力弱的问题。SPGL利用图卷积网络生成全局项目表示和批量会话表示，有效捕捉项目之间的内在关系。使用单正优化损失提高了项目表示的均匀性，从而提高了推荐准确性。在意图提取器中，SPGL在构建有向全局图时考虑了邻接矩阵的跳数，以充分整合空间信息，并在构建会话表示时考虑了项目的反向位置信息，以纳入时间信息。在三个基准数据集Tmall、RetailRocket和Diginetica上的对比实验证明了该模型的有效性。源代码可在https://github.com/liang-tian-tian/SPGL 获取。"
+    },
+    {
+        "title": "SepLLM: Accelerate Large Language Models by Compressing One Segment into\n  One Separator",
+        "url": "http://arxiv.org/abs/2412.12094v1",
+        "pub_date": "2024-12-16",
+        "summary": "Large Language Models (LLMs) have exhibited exceptional performance across a spectrum of natural language processing tasks. However, their substantial sizes pose considerable challenges, particularly in computational demands and inference speed, due to their quadratic complexity. In this work, we have identified a key pattern: certain seemingly meaningless special tokens (i.e., separators) contribute disproportionately to attention scores compared to semantically meaningful tokens. This observation suggests that information of the segments between these separator tokens can be effectively condensed into the separator tokens themselves without significant information loss. Guided by this insight, we introduce SepLLM, a plug-and-play framework that accelerates inference by compressing these segments and eliminating redundant tokens. Additionally, we implement efficient kernels for training acceleration. Experimental results across training-free, training-from-scratch, and post-training settings demonstrate SepLLM's effectiveness. Notably, using the Llama-3-8B backbone, SepLLM achieves over 50% reduction in KV cache on the GSM8K-CoT benchmark while maintaining comparable performance. Furthermore, in streaming settings, SepLLM effectively processes sequences of up to 4 million tokens or more while maintaining consistent language modeling capabilities.",
+        "translated": "大型语言模型（LLMs）在各类自然语言处理任务中展现了卓越的性能。然而，其庞大的规模带来了显著的挑战，尤其是在计算需求和推理速度方面，这主要归因于其二次复杂性。在本研究中，我们发现了一个关键现象：某些看似无意义的特殊标记（即分隔符）在注意力得分上相较于语义上有意义的标记贡献不成比例地高。这一观察表明，这些分隔符之间的段落信息可以有效地浓缩到分隔符本身，而不会造成显著的信息损失。基于这一洞察，我们提出了SepLLM，这是一个即插即用的框架，通过压缩这些段落并消除冗余标记来加速推理。此外，我们还实现了用于训练加速的高效内核。在无需训练、从头训练和训练后设置下的实验结果证明了SepLLM的有效性。值得注意的是，采用Llama-3-8B作为骨干模型，SepLLM在GSM8K-CoT基准测试中实现了超过50%的KV缓存减少，同时保持了相当的性能水平。此外，在流式处理环境中，SepLLM能够有效处理长达400万甚至更多标记的序列，同时保持一致的语言建模能力。"
+    },
+    {
+        "title": "Making FETCH! Happen: Finding Emergent Dog Whistles Through Common\n  Habitats",
+        "url": "http://arxiv.org/abs/2412.12072v1",
+        "pub_date": "2024-12-16",
+        "summary": "WARNING: This paper contains content that maybe upsetting or offensive to some readers. Dog whistles are coded expressions with dual meanings: one intended for the general public (outgroup) and another that conveys a specific message to an intended audience (ingroup). Often, these expressions are used to convey controversial political opinions while maintaining plausible deniability and slip by content moderation filters. Identification of dog whistles relies on curated lexicons, which have trouble keeping up to date. We introduce \\textbf{FETCH!}, a task for finding novel dog whistles in massive social media corpora. We find that state-of-the-art systems fail to achieve meaningful results across three distinct social media case studies. We present \\textbf{EarShot}, a novel system that combines the strengths of vector databases and Large Language Models (LLMs) to efficiently and effectively identify new dog whistles.",
+        "translated": "警告：本文包含可能令部分读者感到不安或冒犯的内容。“狗哨”是指具有双重含义的编码表达方式：一种面向普通大众（外群体），另一种则向特定受众（内群体）传达特定信息。这些表达常用于传递有争议的政治观点，同时保持合理推诿的可能性，并绕过内容审核过滤器。识别“狗哨”依赖于精心整理的词库，但这些词库往往难以保持最新。我们引入了**FETCH!**任务，旨在从大规模社交媒体语料库中发现新型“狗哨”。研究发现，当前最先进的系统在三个不同的社交媒体案例研究中未能取得有意义的结果。我们提出了**EarShot**，这是一种结合向量数据库和大型语言模型（LLMs）优势的新系统，能够高效且有效地识别新的“狗哨”。"
+    },
+    {
+        "title": "Semi-automated analysis of audio-recorded lessons: The case of teachers'\n  engaging messages",
+        "url": "http://arxiv.org/abs/2412.12062v1",
+        "pub_date": "2024-12-16",
+        "summary": "Engaging messages delivered by teachers are a key aspect of the classroom discourse that influences student outcomes. However, improving this communication is challenging due to difficulties in obtaining observations. This study presents a methodology for efficiently extracting actual observations of engaging messages from audio-recorded lessons. We collected 2,477 audio-recorded lessons from 75 teachers over two academic years. Using automatic transcription and keyword-based filtering analysis, we identified and classified engaging messages. This method reduced the information to be analysed by 90%, optimising the time and resources required compared to traditional manual coding. Subsequent descriptive analysis revealed that the most used messages emphasised the future benefits of participating in school activities. In addition, the use of engaging messages decreased as the academic year progressed. This study offers insights for researchers seeking to extract information from teachers' discourse in naturalistic settings and provides useful information for designing interventions to improve teachers' communication strategies.",
+        "translated": "教师的互动性信息是影响学生学习成果的课堂话语中的关键因素。然而，由于获取观察数据的困难，改善这种沟通方式颇具挑战性。本研究提出了一种从录音课程中高效提取互动性信息实际观察数据的方法。我们在两个学年中收集了来自75名教师的2,477节录音课程。通过自动转录和基于关键词的过滤分析，我们识别并分类了互动性信息。与传统的人工编码相比，这种方法将需要分析的信息量减少了90%，从而优化了时间和资源的利用。随后的描述性分析显示，最常用的信息强调了参与学校活动的未来益处。此外，随着学年的推进，互动性信息的使用频率有所下降。本研究为希望从自然情境中的教师话语中提取信息的研究人员提供了见解，并为设计干预措施以改进教师沟通策略提供了有价值的信息。"
+    },
+    {
+        "title": "Virtual Agent-Based Communication Skills Training to Facilitate Health\n  Persuasion Among Peers",
+        "url": "http://arxiv.org/abs/2412.12061v1",
+        "pub_date": "2024-12-16",
+        "summary": "Many laypeople are motivated to improve the health behavior of their family or friends but do not know where to start, especially if the health behavior is potentially stigmatizing or controversial. We present an approach that uses virtual agents to coach community-based volunteers in health counseling techniques, such as motivational interviewing, and allows them to practice these skills in role-playing scenarios. We use this approach in a virtual agent-based system to increase COVID-19 vaccination by empowering users to influence their social network. In a between-subjects comparative design study, we test the effects of agent system interactivity and role-playing functionality on counseling outcomes, with participants evaluated by standardized patients and objective judges. We find that all versions are effective at producing peer counselors who score adequately on a standardized measure of counseling competence, and that participants were significantly more satisfied with interactive virtual agents compared to passive viewing of the training material. We discuss design implications for interpersonal skills training systems based on our findings.",
+        "translated": "许多外行人士有意愿改善其家人或朋友的健康行为，但往往不知从何入手，尤其是在健康行为可能带有污名化或引发争议的情况下。我们提出了一种利用虚拟代理来指导社区志愿者学习健康咨询技术（如动机性访谈）的方法，并让他们在角色扮演场景中实践这些技能。我们将这一方法应用于一个基于虚拟代理的系统，旨在通过赋予用户影响其社交网络的能力，来提高COVID-19疫苗接种率。在一项组间比较设计研究中，我们测试了代理系统交互性和角色扮演功能对咨询结果的影响，参与者由标准化患者和客观裁判进行评估。研究发现，所有版本的系统都能有效培养出在标准化咨询能力评估中表现合格的同伴咨询师，且参与者对交互式虚拟代理的满意度显著高于被动观看培训材料。基于这些发现，我们讨论了人际技能培训系统的设计启示。"
+    },
+    {
+        "title": "How Private are Language Models in Abstractive Summarization?",
+        "url": "http://arxiv.org/abs/2412.12040v1",
+        "pub_date": "2024-12-16",
+        "summary": "Language models (LMs) have shown outstanding performance in text summarization including sensitive domains such as medicine and law. In these settings, it is important that personally identifying information (PII) included in the source document should not leak in the summary. Prior efforts have mostly focused on studying how LMs may inadvertently elicit PII from training data. However, to what extent LMs can provide privacy-preserving summaries given a non-private source document remains under-explored. In this paper, we perform a comprehensive study across two closed- and three open-weight LMs of different sizes and families. We experiment with prompting and fine-tuning strategies for privacy-preservation across a range of summarization datasets across three domains. Our extensive quantitative and qualitative analysis including human evaluation shows that LMs often cannot prevent PII leakage on their summaries and that current widely-used metrics cannot capture context dependent privacy risks.",
+        "translated": "语言模型（LMs）在文本摘要任务中表现出色，包括医学和法律等敏感领域。在这些场景中，源文档中包含的个人身份信息（PII）不应在摘要中泄露。以往的研究主要集中在探讨语言模型如何无意中从训练数据中引出PII。然而，在给定非隐私源文档的情况下，语言模型能够多大程度地生成保护隐私的摘要仍未得到充分探索。本文对两个闭源和三个开源的不同大小和类型的语言模型进行了全面研究。我们在三个领域的多个摘要数据集上，通过提示和微调策略实验了隐私保护方法。我们的广泛定量和定性分析，包括人工评估，表明语言模型在生成摘要时往往无法防止PII泄露，并且当前广泛使用的指标无法捕捉上下文相关的隐私风险。"
+    },
+    {
+        "title": "Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability\n  Detection",
+        "url": "http://arxiv.org/abs/2412.12039v1",
+        "pub_date": "2024-12-16",
+        "summary": "Despite their remarkable success, large language models (LLMs) have shown limited ability on applied tasks such as vulnerability detection. We investigate various prompting strategies for vulnerability detection and, as part of this exploration, propose a prompting strategy that integrates natural language descriptions of vulnerabilities with a contrastive chain-of-thought reasoning approach, augmented using contrastive samples from a synthetic dataset. Our study highlights the potential of LLMs to detect vulnerabilities by integrating natural language descriptions, contrastive reasoning, and synthetic examples into a comprehensive prompting framework. Our results show that this approach can enhance LLM understanding of vulnerabilities. On a high-quality vulnerability detection dataset such as SVEN, our prompting strategies can improve accuracies, F1-scores, and pairwise accuracies by 23%, 11%, and 14%, respectively.",
+        "translated": "尽管大型语言模型（LLMs）在许多领域取得了显著的成功，但在诸如漏洞检测等应用任务上，其表现仍然有限。我们研究了多种用于漏洞检测的提示策略，并在这一探索过程中，提出了一种将漏洞的自然语言描述与对比性思维链推理方法相结合的提示策略，该方法通过使用合成数据集中的对比样本进行增强。我们的研究表明，通过将自然语言描述、对比性推理和合成示例整合到一个综合的提示框架中，LLMs在检测漏洞方面具有潜力。我们的实验结果显示，这种方法能够增强LLM对漏洞的理解。在如SVEN这样的高质量漏洞检测数据集上，我们的提示策略能够分别将准确率、F1分数和成对准确率提高23%、11%和14%。"
     }
 ]