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Keep it Local: Comparing Domain-Specific LLMs in Native and Machine Translated Text using Parallel Corpora on Political Conflict 2

Keep it Local: Comparing Domain-Specific LLMs in Native and ...

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2nd International conference on Foundation and Large Language Models, FLLM 2024

作者： Osorio, Javier Alsarra, Sultan Converse, Amber Alshammari, Afraa Heintze, Dagmar Khan, Latifur Alatrush, Naif Brandt, Patrick T. D'orazio, Vito Zawad, Niamat Billah, Mahrusa University of Arizona School of Government and Public Policy Tucson United States King Saud University Department of Software Engineering Riyadh Saudi Arabia University of Arizona Department of Linguistics Tucson United States University of Texas - Dallas Department of Computer Science Dallas United States University of Texas - Dallas School of Economic Political and Policy Sciences Dallas United States West Virginia University Department of Political Science Morgantown United States

ISBN: (纸本)9798350354799

The dynamics of political conflict and cooperation require powerful computerized tools capable of effectively tracking security threats and cooperation around the world. This study compares the performance of domain-specific Large Language Models (LLMs) against generically-trained LLMs in binary and multi-class classification using native text in English, Spanish, and Arabic, and their corresponding machine translations. This endeavor yields four key contributions. 1) We present and make available a novel database of annotations using a multi-lingual parallel corpus from the United Nations. 2) Using various metrics, we assess the quality of different machine translation tools. 3) Our results indicate that the ConfliBERT family of LLMs, a set of domain-specific models tailored for political conflict, outperform generically-trained LLMs in English, Spanish, and Arabic in both binary and multi-class tasks. 4) We also disentangle the heterogeneous effects of machine translation on LLM performance in different languages. Overall, results reveal the comparative advantage of native-language domain-specific LLMs specialized on political conflict to understand the dynamics of violence and cooperation worldwide using native text. Our multi-lingual ConfliBERT LLMs provide critical cyber-infrastructure enabling scholars and government agencies use their local languages and information to foster safer, more stable political environments. © 2024 IEEE.

关键词： Machine translation

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From program Chains to Exploratory Workflows: PopinSnake for Genomic Insertion Detection 19

From Program Chains to Exploratory Workflows: PopinSnake for...

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19th IEEE International conference on e-Science, e-Science 2023

作者： Cao, Kedi Elfaramawy, Nourhan Weidlich, Matthias Kehr, Birte Humboldt-Universität zu Berlin Germany Leibniz Institute for Immunotherapy Germany Universität Regensburg Germany

ISBN: (纸本)9798350322231

Scientific data analysis is often an exploratory process, so that it is imperative to trace the precise sequencing and configuration of analysis steps in order to ensure reproducibility of scientific results. In addition, responsible use of computational resources and the avoidance of redundant computation are crucial when dealing with large datasets and complex analysis steps. By using a workflow engine, one may address the need for reproducibility and resource efficiency and also benefit from increased portability to various hardware infrastructures and software environments. However, traditional analysis pipelines that are implemented as chains of program calls do not meet the prerequisites of an exploratory workflow. In this paper, we focus on the question of how to transform an existing data analysis program into an exploratory workflow that can be executed using a common workflow engine and that also includes means to explore intermediate results and act upon them in an interactive manner. Specifically, we exemplify the transformation process using the genomic variation detection program PopIns, which we transform into an exploratory workflow through modularization of the functionality, its migration to the Snakemake workflow engine, and the integration of user interaction features. By combining a highly automated workflow with possibilities of guided user exploration, we are able to reduce the Time-To-Insights on the analysis. We further report on lessons learned from this transformation, thereby providing a generalization from the specific case. This way, we provide some general guidance on the transformation of static analysis programs into exploratory workflows, thereby making data analysis more accessible and user efficient. © 2023 IEEE.

关键词： Genome

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LightNestle: Quick and Accurate Neural Sequential Tensor Completion via Meta Learning 42

LightNestle: Quick and Accurate Neural Sequential Tensor Com...

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42nd IEEE International conference on computer Communications, INFOCOM 2023

作者： Li, Yuhui Liang, Wei Xie, Kun Zhang, Dafang Xie, Songyou Li, KuanChing Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan411201 China

ISBN: (纸本)9798350334142

Network operation and maintenance rely heavily on network traffic monitoring. Due to the measurement overhead reduction, lack of measurement infrastructure, and unexpected transmission error, network traffic monitoring systems suffer from incomplete observed data and high data sparsity problems. Recent studies model missing data recovery as a tensor completion task and show good performance. Although promising, the current tensor completion models adopted in network traffic data recovery lack an effective and efficient retraining scheme to adapt to newly arrived data while retaining historical information. To solve the problem, we propose LightNestle, a novel sequential tensor completion scheme based on meta-learning, which designs (1) an expressive neural network to transfer spatial knowledge from previous embeddings to current embeddings;(2) an attention-based module to transfer temporal patterns into current embeddings in linear complexity;and (3) meta-learning-based algorithms to iteratively recover missing data and update transfer modules to catch up with learned knowledge. We conduct extensive experiments on two real-world network traffic datasets to assess our performance. Results show that our proposed methods achieve both fast retraining and high recovery accuracy. © 2023 IEEE.

关键词： Embeddings

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Unveiling the Knowledge of CLIP for Training-Free Open-Vocabulary Semantic Segmentation 39

Unveiling the Knowledge of CLIP for Training-Free Open-Vocab...

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39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Liu, Yajie Wang, Guodong Zhang, Jinjin Liu, Qingjie Huang, Di State Key Laboratory of Complex and Critical Software Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)157735897X

Training-free open-vocabulary semantic segmentation aims to explore the potential of frozen vision-language models (VLM) for segmentation tasks. Recent works reform the inference process of CLIP and utilize the features from the final layer to reconstruct dense representations for segmentation, demonstrating promising performance. However, the final layer tends to prioritize global components over local representations, leading to suboptimal robustness and effectiveness of existing methods. In this paper, we propose CLIPSeg, a novel training-free framework that fully exploits the diverse knowledge across layers in CLIP for dense predictions. Our study unveils two key discoveries: Firstly, the features in the middle layers exhibit high locality awareness and feature coherence compared to the final layer, based on which we propose the coherence enhanced residual attention module that generates semantic-aware attention. Secondly, despite not being directly aligned with the text, the deep layers capture valid local semantics that complement those in the final layer. Leveraging this insight, we introduce the deep semantic integration module to boost the patch semantics in the final block. Experiments conducted on 9 segmentation benchmarks with various CLIP models demonstrate that CLIPSeg consistently outperforms all training-free methods by substantial margins, e.g., a 7.8% improvement in average mIoU for CLIP with a ViT-L backbone, and competes with learning-based counterparts in generalizing to novel concepts in an efficient way. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Semantic Segmentation

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Line Segment Matching Based on Intersection-Enhanced Point Correspondences 48

Line Segment Matching Based on Intersection-Enhanced Point C...

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48th IEEE International conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Liu, Zhiyu Zhong, Baojiang Soochow University School of Computer Science and Technology Suzhou China

ISBN: (纸本)9781728163277

Line segment matching between two images of the same scene is popularly performed with the assistant of feature point correspondences. However, each existing method only relies on one specific kind of feature points to establish the correspondences, and could yield inferior performance when insufficient high-quality point correspondences are supplied. To address this fundamental problem, a novel line segment matching method is proposed. In our method, ASIFT points are exploited to establish an initial set of correspondences. Then, line intersections are matched between the two input images to establish an additional set of correspondences, i.e., intersection correspondences. Finally, an enhanced set of point correspondences is generated via a union of the ASIFT and intersection correspondences. Based on the yielded intersection-enhanced point correspondences, it will be shown that the performance of line segment matching can be greatly improved. © 2023 IEEE.

关键词： feature point homography estimation Line segment matching mismatch removal

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Learning Quantum Distributions Based on Normalizing Flow

Learning Quantum Distributions Based on Normalizing Flow

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2023 IEEE International conference on Systems, Man, and Cybernetics, SMC 2023

作者： Li, Li Wang, Yong Cheng, Shuming Liu, Lijun College of Electronic and Information Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai201804 China College of Electronic and Information Engineering Tongji University Shanghai201804 China College of Mathematics and Computer Science Shanxi Normal University Linfen041000 China

ISBN: (纸本)9798350337020

Learning many-body quantum systems is of fundamental importance in quantum information processing, however, it is a challenging task which typically requires estimating quantum distributions of dimensionality exponentially scaling to the system size. As generative models have shown a great scalability to learn high-dimensional distributions and found wide applications in the domain of image and text, they can be a powerful tool to facilitate us to accomplish the challenging quantum tasks. In this work, we propose using normalizing flow (NF) models with fast sampling to learn discrete quantum distributions for quantum state tomography. Particularly, three NF models, including denoising flow, argmax flow, and tree flow, are first adapted to the task of explicit quantum probability density estimation. We then perform extensive experiments on a large scale of quantum systems, and our numerical results demonstrate that these discrete NFs admit an excellent sampling efficiency in the sense that they are insensitive to the system size to learn the high-dimensional quantum distributions, without compromising the learning performance. Finally, in comparison to the other generative models, such as autoregressive, the NFs avoid the problem of slow sequential sampling. © 2023 IEEE.

关键词： autoregressive models normalizing flow quantum distribution quantum state tomography

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What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration 38

What Factors Affect Multi-Modal In-Context Learning? An In-D...

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38th conference on Neural Information Processing Systems, NeurIPS 2024

作者： Qin, Libo Chen, Qiguang Fei, Hao Chen, Zhi Li, Min Che, Wanxiang School of Computer Science and Engineering Central South University China Research Center for Social Computing and Information Retrieval China Harbin Institute of Technology China Tsinghua University China ByteDance China

Recently, rapid advancements in Multi-Modal In-Context Learning (MM-ICL) have achieved notable success, which is capable of achieving superior performance across various tasks without requiring additional parameter tuning. However, the underlying rules for the effectiveness of MM-ICL remain under-explored. To fill this gap, this work aims to investigate the research question: "What factors affect the performance of MM-ICL?" To this end, we investigate extensive experiments on the three core steps of MM-ICL including demonstration retrieval, demonstration ordering, and prompt construction using 6 vision large language models and 20 strategies. Our findings highlight (1) the necessity of a multi-modal retriever for demonstration retrieval, (2) the importance of intra-demonstration ordering over inter-demonstration ordering, and (3) the enhancement of task comprehension through introductory instructions in prompts. We hope this study can serve as a foundational guide for optimizing MM-ICL strategies in future research. © 2024 Neural information processing systems foundation. All rights reserved.

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Image Anomaly Detection With Semantic- Enhanced Augmentation and Distributional Kernel 24

Image Anomaly Detection With Semantic- Enhanced Augmentation...

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24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Mingxi Zhu, Ye Li, Gang Liu, Gang Yang, Bo College of Computer Science and Technology Jilin University Changchun China Centre for Cyber Security Research and Innovation Deakin University Geelong Australia College of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9798350319934

In the field of image anomaly detection, self-supervised learning using only normal samples has become a hot research topic since it is difficult to acquire sufficient abnormal data. However, existing self-supervised methods usually consider the impact of the superficial information rather than the semantics of images, resulting in limited generalisation performance. Furthermore, the small amount of training data further limits the performance of anomaly detection. To overcome both issues, we propose a two-stage self-supervised learning framework based on CutPaste with a feature extraction stage followed by an anomaly detection stage. We first add a diminished semantics augmentation to generate harder abnormal samples, so that the feature extraction model pays more attention to semantic information. Furthermore, we increase the density of normal samples with normal sample augmentations and integrate a data-dependent distributional kernel to improve the detection of abnormal images in the second stage, based on the adaptive distributional similarity. Our extensive experiments on the MVTec anomaly detection dataset demonstrate the superior performance of the proposed method. © 2022 IEEE.

关键词： Anomaly detection

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Research on the Mongolian Speech Synthesis Model Based on Transformer and WaveRNN 4

Research on the Mongolian Speech Synthesis Model Based on Tr...

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4th International conference on Frontiers Technology of Information and computer, ICFTIC 2022

作者： Zhang, Wenjing Ren, Qingdaoerji Li, Yuan Chen, Xiuhong Inner Mongolia University of Technology Department of Computer Science Inner Mongolia Autonomous Region Hohhot China

ISBN: (纸本)9798350321951

The main task of speech synthesis is how to convert text information into audible voice information, which can be widely used in smart home, virtual anchor, voice navigation and other fields. The research of Mongolian speech synthesis will help to enhance the independent development ability of informatization in Inner Mongolia, and accelerate the integration pace of Mongolian language and modernization. This paper proposes the improved Trans-WaveRNN based on Transformer and WaveRNN, aiming at the existing Mongolian speech synthesis model has the phenomenon of wrong words and missing words, the synthesized speech is unnatural, the synthesized speech speed is too slow, and cannot meet the real-time interaction of the system. First of all, according to the characteristics of the Mongolian language itself, the Mongolian text is preprocessed at the front-end, the Mongolian words are converted into their corresponding Latin form, and then converted into a phonemes, which are performed on the pre-normalized phoneme sequence fir training, reducing the problem of mispronunciation trained on character sequences;Secondly, the proposed model can be trained in parallel, there is no recursive connection between the framewoks, and the WaveRNN mode is used as a vocoder to synthesize the audio, while improves the performance of Mongolian speech synthesis, while speeding up the training speed, making the audio process much smoother and satisfying the real-time and interaction properties of the speech synthesis system. Experiments show that the average subjective opinion score of the improved Trans-WaveRNN Mongolian speech synthesis model reaches 4.13, and the real-time rate reaches 0.94 while satisfying the practicality. The method proposed in this paper effectively improves the quality and performance of the Mongolian speech synthesis model. © 2022 IEEE.

关键词： Speech synthesis

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Re-Attentional Controllable Video Diffusion Editing 39

Re-Attentional Controllable Video Diffusion Editing

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39th Annual AAAI conference on Artificial Intelligence, AAAI 2025

作者： Wang, Yuanzhi Li, Yong Liu, Mengyi Zhang, Xiaoya Liu, Xin Cui, Zhen Chan, Antoni B. School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Content Security Kuaishou Technology Beijing China Department of Computer Science City University of Hong Kong Hong Kong SeetaCloud Nanjing China

ISBN: (纸本)157735897X

Editing videos with textual guidance has garnered popularity due to its streamlined process which mandates users to solely edit the text prompt corresponding to the source video. Recent studies have explored and exploited large-scale text-to-image diffusion models for text-guided video editing, resulting in remarkable video editing capabilities. However, they may still suffer from some limitations such as mislocated objects, incorrect number of objects. Therefore, the controllability of video editing remains a formidable challenge. In this paper, we aim to challenge the above limitations by proposing a Re-Attentional Controllable Video Diffusion Editing (ReAtCo) method. Specially, to align the spatial placement of the target objects with the edited text prompt in a training-free manner, we propose a Re-Attentional Diffusion (RAD) to refocus the cross-attention activation responses between the edited text prompt and the target video during the denoising stage, resulting in a spatially location-aligned and semantically high-fidelity manipulated video. In particular, to faithfully preserve the invariant region content with less border artifacts, we propose an Invariant Region-guided Joint Sampling (IRJS) strategy to mitigate the intrinsic sampling errors w.r.t the invariant regions at each denoising timestep and constrain the generated content to be harmonized with the invariant region content. Experimental results verify that ReAtCo consistently improves the controllability of video diffusion editing and achieves superior video editing performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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