Tootfinder

Automat & Max Loderbauer, Hall. #sound
https://www.youtube.com/watch?app=desktop&v=Yo5IMEANuxg

The huge wedge between McConnell and Johnson: Donald Trump (Politico)
https://www.politico.com/news/2024/01/29/mcconnell-johnson-trump-border-ukraine-deal-00138165

A Unified Label-Aware Contrastive Learning Framework for Few-Shot Named Entity Recognition
Haojie Zhang, Yimeng Zhuang
https://arxiv.org/abs/2404.17178 htt…

A Unified Label-Aware Contrastive Learning Framework for Few-Shot Named Entity Recognition
Few-shot Named Entity Recognition (NER) aims to extract named entities using only a limited number of labeled examples. Existing contrastive learning methods often suffer from insufficient distinguishability in context vector representation because they either solely rely on label semantics or completely disregard them. To tackle this issue, we propose a unified label-aware token-level contrastive learning framework. Our approach enriches the context by utilizing label semantics as suffix promp…

TGMM: Combining Parse Tree with GPU for Scalable Multilingual and Multi-Granularity Code Clone Detection
Yuhang Ye, Yuekun Wang, Yinxing Xue, Yueming Wu, Yang Liu
https://arxiv.org/abs/2403.18202

TGMM: Combining Parse Tree with GPU for Scalable Multilingual and Multi-Granularity Code Clone Detection
The rapid evolution of programming languages and software systems has necessitated the implementation of multilingual and scalable clone detection tools. However, it is difficult to achieve the above requirements at the same time. Most existing tools only focus on one challenge. In this work, we propose TGMM, a tree and GPU-based tool for multilingual and multi-granularity code clone detection. By generating parse trees based on user-provided grammar files, TGMM can extract code blocks at a spe…

FoC: Figure out the Cryptographic Functions in Stripped Binaries with LLMs
Guoqiang Chen, Xiuwei Shang, Shaoyin Cheng, Yanming Zhang, Weiming Zhang, Nenghai Yu
https://arxiv.org/abs/2403.18403

FoC: Figure out the Cryptographic Functions in Stripped Binaries with LLMs
Analyzing the behavior of cryptographic functions in stripped binaries is a challenging but essential task. Cryptographic algorithms exhibit greater logical complexity compared to typical code, yet their analysis is unavoidable in areas such as virus analysis and legacy code inspection. Existing methods often rely on data or structural pattern matching, leading to suboptimal generalizability and suffering from manual work. In this paper, we propose a novel framework called FoC to Figure out the…

HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web System
Youngseung Jeon, Jaehoon Kim, Sohyun Park, Yunyong Ko, Seongeun Ryu, Sang-Wook Kim, Kyungsik Han
https://arxiv.org/abs/2402.18222

HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web System
Considerable efforts are currently underway to mitigate the negative impacts of echo chambers, such as increased susceptibility to fake news and resistance towards accepting scientific evidence. Prior research has presented the development of computer systems that support the consumption of news information from diverse political perspectives to mitigate the echo chamber effect. However, existing studies still lack the ability to effectively support the key processes of news information consump…

Clustering Change Sign Detection by Fusing Mixture Complexity
Kento Urano, Ryo Yuki, Kenji Yamanishi
https://arxiv.org/abs/2403.18269 https://

Clustering Change Sign Detection by Fusing Mixture Complexity
This paper proposes an early detection method for cluster structural changes. Cluster structure refers to discrete structural characteristics, such as the number of clusters, when data are represented using finite mixture models, such as Gaussian mixture models. We focused on scenarios in which the cluster structure gradually changed over time. For finite mixture models, the concept of mixture complexity (MC) measures the continuous cluster size by considering the cluster proportion bias and ov…

Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations
Shen Zhang, Haojie Zhang, Jing Zhang, Xudong Zhang, Yimeng Zhuang, Jinting Wu
https://arxiv.org/abs/2404.16905

Samsung Research China-Beijing at SemEval-2024 Task 3: A multi-stage framework for Emotion-Cause Pair Extraction in Conversations
In human-computer interaction, it is crucial for agents to respond to human by understanding their emotions. Unraveling the causes of emotions is more challenging. A new task named Multimodal Emotion-Cause Pair Extraction in Conversations is responsible for recognizing emotion and identifying causal expressions. In this study, we propose a multi-stage framework to generate emotion and extract the emotion causal pairs given the target emotion. In the first stage, Llama-2-based InstructERC is uti…

Improving Open-Ended Text Generation via Adaptive Decoding
Wenhong Zhu, Hongkun Hao, Zhiwei He, Yiming Ai, Rui Wang
https://arxiv.org/abs/2402.18223 https:…

Improving Open-Ended Text Generation via Adaptive Decoding
Current language models decode text token by token according to probabilistic distribution, and determining the appropriate candidates for the next token is crucial to ensure generation quality. This study introduces adaptive decoding, a mechanism that empowers the language models to ascertain a sensible candidate set during the generation process dynamically. Specifically, we introduce an entropy-based metric called confidence and conceptualize determining the optimal candidate set as a confid…

The First Place Solution of WSDM Cup 2024: Leveraging Large Language Models for Conversational Multi-Doc QA
Yiming Li, Zhao Zhang
https://arxiv.org/abs/2402.18385

The First Place Solution of WSDM Cup 2024: Leveraging Large Language Models for Conversational Multi-Doc QA
Conversational multi-doc question answering aims to answer specific questions based on the retrieved documents as well as the contextual conversations. In this paper, we introduce our winning approach for the "Conversational Multi-Doc QA" challenge in WSDM Cup 2024, which exploits the superior natural language understanding and generation capability of Large Language Models (LLMs). We first adapt LLMs to the task, then devise a hybrid training strategy to make the most of in-domain unlabeled da…