topic stringclasses 2
values | relevance score int64 1 10 | paper name stringlengths 19 239 | text stringlengths 1.56k 680k |
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synthetic_cpt | 2 | Prompt_Programming_for_Large_Language_Models_Beyond_the_Few-Shot_Paradigm.pdf | 1
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Prompt Programming for Large Language Models:
Beyond the Few-Shot Paradigm
Laria Reynolds
moire@knc.ai
Kyle McDonell
kyle@knc.ai
Abstract
Prevailing methods for mapping large generative language models to supervised tasks may fail to
sufficiently... |
synthetic_cpt | 7 | Improving_Text_Embeddings_with_Large_Language_Models.pdf | 4
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Enhancing Embedding Performance through Large
Language Model-based Text Enrichment and
Rewriting
Nicholas Harris
Arizona State University
Tempe, Arizona
nick@myautobio.com
Anand Butani
MyAutoBio Inc.
Scottsdale, Arizona
anand@myautobio.com
Syed ... |
synthetic_cpt | 1 | Hierarchical_Patch_Selection_An_Improved_Patch_Sampling_for_No_Reference_Image_Quality_Assessment.pdf | 9
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Isogeometric analysis with C 1 hierarchical functions on planar
two-patch geometries
Cesare Braccoa, Carlotta Giannellia, Mario Kaplb,∗, Rafael V´azquezc,d
aDipartimento di Matematica e Informatica “U. Dini”,
Universit`a degli Studi di Fire... |
synthetic_cpt | 3 | Active_Learning_Principles_for_In-Context_Learning_with_Large_Language_Models.pdf | 7
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Generative Adversarial Active Learning
Jia-Jie Zhu
Max Planck Institute for Intelligent Systems
Tübingen, Germany
jia-jie.zhu@tuebingen.mpg.de
Jose Bento
Department of Computer Science
Boston College
Chestnut Hill, Massachusetts, USA
jose.bento@... |
synthetic_cpt | 2 | NaturalSpeech_2_Latent_Diffusion_Models_are_Natural_and_Zero-Shot_Speech_and_Singing_Synthesizers.pdf | 3
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NaturalSpeech 2: Latent Diffusion Models are Natural
and Zero-Shot Speech and Singing Synthesizers
Kai Shen∗, Zeqian Ju∗, Xu Tan∗, Yanqing Liu, Yichong Leng, Lei He
Tao Qin, Sheng Zhao, Jiang Bian
Microsoft Research Asia & Microsoft Azure Spee... |
synthetic_cpt | 2 | NeKo_Toward_Post_Recognition_Generative_Correction_Large_Language_Models_with_Task-Oriented_Experts.pdf | Neko: a Library for Exploring Neuromorphic Learning Rules
Zixuan Zhao
University of Chicago
Nathan Wycoff
Virginia Tech
Neil Getty
Argonne National Laboratory
Rick Stevens
Argonne National Laboratory &
University of Chicago
Fangfang Xia
Argonne National Laboratory &
University of Chicago
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synthetic_cpt | 8 | Generating_Training_Data_with_Language_Models_Towards_Zero-Shot_Language_Understanding.pdf | JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2021
1
Self-Training Vision Language BERTs with a
Unified Conditional Model
Xiaofeng Yang, Fengmao Lv, Fayao Liu, Guosheng Lin
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Abstract—Natural language BERTs are trained with language
corpu... |
synthetic_cpt | 2 | How_to_Learn_a_New_Language_An_Efficient_Solution_for_Self-Supervised_Learning_Models_Unseen_Languages_Adaption_in_Low-Resource_Scenario.pdf | Teaching Embodied Reinforcement Learning Agents:
Informativeness and Diversity of Language Use
Jiajun Xi* Yinong He∗
Jianing Yang Yinpei Dai
Joyce Chai
University of Michigan
{jiajunxi, heyinong, jianingy, daiyp, chaijy}@umich.edu
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Abstract
I... |
synthetic_cpt | 7 | ELLE_Efficient_Lifelong_Pre-training_for_Emerging_Data.pdf | CERN-TH-2018-127
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ZZ production at the LHC:
NNLO predictions for 2(cid:96)2ν and 4(cid:96) signatures
Stefan Kallweit and Marius Wiesemann
TH Division, Physics Department, CERN, CH-1211 Geneva 23, Switzerland
stefan.kallweit@cern.ch
marius.wi... |
synthetic_cpt | 1 | Semi-Automated_Construction_of_Food_Composition_Knowledge_Base.pdf | 6
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Semi-homotopy and semi-fundamental groups
Ayhan ERC˙IYES∗a, Ali AYTEK˙INb and Tunçar ¸SAHANb
aDepartment of Elementary Mathematics Education, Aksaray University, Aksaray, TURKEY
bDepartment of Mathematics, Aksaray University, Aksaray, TURKEY
... |
synthetic_cpt | 5 | Automatic_Document_Selection_for_Efficient_Encoder_Pretraining.pdf | Automatic Document Selection for Efficient Encoder Pretraining
Yukun Feng1 Patrick Xia1 Benjamin Van Durme1
João Sedoc2
1Johns Hopkins University
2New York University
{yfeng55, paxia, vandurme}@jhu.edu, jsedoc@stern.nyu.edu
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Abstract
Building pr... |
synthetic_cpt | 3 | Enhancing_Tool_Retrieval_with_Iterative_Feedback_from_Large_Language_Models.pdf | Enhancing Tool Retrieval with Iterative Feedback from
Large Language Models
Qiancheng Xu, Yongqi Li†, Heming Xia, Wenjie Li
Department of Computing, The Hong Kong Polytechnic University, China
{qiancheng.xu, he-ming.xia}@connect.polyu.hk
cswjli@comp.polyu.edu.hk
liyongqi0@gmail.com
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synthetic_cpt | 4 | LiDA_Language-Independent_Data_Augmentation_for_Text_Classification.pdf | LIDA: A Tool for Automatic Generation of Grammar-Agnostic
Visualizations and Infographics using Large Language Models
Victor Dibia
Microsoft Research
victordibia@microsoft.com
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Abstract
Systems that support users in the automatic
creation of visu... |
synthetic_cpt | 1 | A_systematic_evaluation_of_large_language_models_for_biomedical_natural_language_processing_benchmarks_baselines_and_recommendations.pdf | 4
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BENCHMARKING RETRIEVAL-AUGMENTED LARGE
LANGUAGE MODELS IN BIOMEDICAL NLP: APPLICA-
TION, ROBUSTNESS, AND SELF-AWARENESS
Mingchen Li, Zaifu Zhan, Han Yang, Yongkang Xiao, Jiatan Huang, Rui Zhang
University of Minnesota Twin Cities
{li003378,zhan802... |
synthetic_cpt | 8 | BERTtime_Stories_Investigating_the_Role_of_Synthetic_Story_Data_in_Language_pre-training.pdf | 4
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BERTtime Stories: Investigating the Role of Synthetic Story Data in
Language Pre-training
Nikitas Theodoropoulos, Giorgos Filandrianos, Vassilis Lyberatos,
Maria Lymperaiou and Giorgos Stamou
Artificial Intelligence and Learning Systems Laboratory
S... |
synthetic_cpt | 2 | LLM-Adapters_An_Adapter_Family_for_Parameter-Efficient_Fine-Tuning_of_Large_Language_Models.pdf | 4
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Large Language Models as Software Components:
A Taxonomy for LLM-Integrated Applications
Irene Weber
Kempten University of Applied Sciences, Germany
irene.weber@hs-kempten.de
Abstract
Large Language Models (LLMs) have become widely adopted rec... |
synthetic_cpt | 4 | Advancing_Large_Language_Model_Attribution_through_Self-Improving.pdf | Advancing Large Language Model Attribution through Self-Improving
Lei Huang1, Xiaocheng Feng1,2*, Weitao Ma1, Liang Zhao1, Yuchun Fan3,
Weihong Zhong1, Dongliang Xu4, Qing Yang4, Hongtao Liu4, Bing Qin1,2
1 Harbin Institute of Technology, Harbin, China
2 Peng Cheng Laboratory, Shenzhen, China
3 Northeastern Universi... |
synthetic_cpt | 7 | Hybrid_Training_Approaches_for_LLMs_Leveraging_Real_and_Synthetic_Data_to_Enhance_Model_Performance_in_Domain-Specific_Applications.pdf | 4
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Preprint
HDFLOW: ENHANCING LLM COMPLEX PROBLEM-
SOLVING WITH HYBRID THINKING AND DYNAMIC
WORKFLOWS
Wenlin Yao, Haitao Mi, Dong Yu
Tencent AI Lab
Bellevue, WA 98004, USA
{wenlinyao,haitaomi,dyu}@global.tencent.com
ABSTRACT
Despite recent advance... |
synthetic_cpt | 3 | Synthetic_Query_Generation_using_Large_Language_Models_for_Virtual_Assistants.pdf | 4
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Synthetic Query Generation using Large Language Models
for Virtual Assistants
Sonal Sannigrahi∗†
sonal.sannigrahi@tecnico.ulisboa.pt
Instituto Superior Técnico
Lisbon, Portugal
Youssef Oualil
youalil@apple.com
Apple
Aachen, Germany
Thiago Fra... |
synthetic_cpt | 2 | Neural_Codec_Language_Models_are_Zero-Shot_Text_to_Speech_Synthesizers.pdf | Towards audio language modeling - an overview
Haibin Wu1, Xuanjun Chen1∗, Yi-Cheng Lin1∗, Kai-wei Chang1, Ho-Lam Chung1,
Alexander H. Liu2, Hung-yi Lee1
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Abstract—Neural audio codecs are initially introduced to
compress audio data into comp... |
synthetic_cpt | 1 | I-BERT_Integer-only_BERT_Quantization.pdf | 3
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BOUNDS FOR THE REDUCTION NUMBER OF PRIMARY IDEAL IN
DIMENSION THREE
MOUSUMI MANDAL AND KUMARI SALONI
Abstract. Let (R, m) be a Cohen-Macaulay local ring of dimension d ≥ 3 and I an m-primary
ideal of R. Let rJ (I) be the reduction number of... |
synthetic_cpt | 3 | Can_Language_Models_Induce_Grammatical_Knowledge_from_Indirect_Evidence.pdf | Can Language Models Induce Grammatical Knowledge
from Indirect Evidence?
Miyu Oba1 Yohei Oseki2 Akiyo Fukatsu2 Akari Haga1
Hiroki Ouchi1 Taro Watanabe1 Saku Sugawara3
1Nara Institute of Science and Technology
2The University of Tokyo 3National Institute of Informatics
{oba.miyu.ol2,haga.akari.ha0,hiroki.ouchi,taro}@is.... |
synthetic_cpt | 4 | InPars_Data_Augmentation_for_Information_Retrieval_using_Large_Language_Models.pdf | 4
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Published in Transactions on Machine Learning Research (MM/YYYY)
InPars-Light: Cost-Effective Unsupervised Training of Effi-
cient Rankers
Leonid Boytsov∗
Amazon AWS AI Labs
Pittsburgh
USA
Preksha Patel
Vivek Sourabh
Riddhi Nisar
Sayani Kundu
R... |
synthetic_cpt | 4 | Data_Augmentation_for_Spoken_Language_Understanding_via_Pretrained_Language_Models.pdf | Data Augmentation for Spoken Language Understanding via Pretrained
Language Models
Baolin Peng∗, Chenguang Zhu∗, Michael Zeng, Jianfeng Gao
Microsoft Research, Redmond
{bapeng,chezhu,nzeng,jfgao}@microsoft.com
Abstract
The training of spoken language understanding (SLU) models
often faces the problem of data scarci... |
synthetic_cpt | 1 | Exploring_the_Utility_of_Self-Supervised_Pretraining_Strategies_for_the_Detection_of_Absent_Lung_Sliding_in_M-Mode_Lung_Ultrasound.pdf | 1
SERE: Exploring Feature Self-relation for
Self-supervised Transformer
Zhong-Yu Li, Shanghua Gao, Ming-Ming Cheng
Abstract—Learning representations with self-supervision for convolutional networks (CNN) has been validated to be effective for
vision tasks. As an alternative to CNN, vision transformers (ViT) have str... |
synthetic_cpt | 1 | Dex-Net_20_Deep_Learning_to_Plan_Robust_Grasps_with_Synthetic_Point_Clouds_and_Analytic_Grasp_Metrics.pdf | 0
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Chemical composition of the old globular clusters NGC 1786, NGC 2210 and
NGC 2257 in the Large Magellanic Cloud. 1
Dipartimento di Astronomia, Universit`a degli Studi di Bologna, Via Ranzani, 1 - 40127 Bologna,
ITALY
Alessio Mucciarelli
... |
synthetic_cpt | 3 | Neural_Machine_Translation_between_Low-Resource_Languages_with_Synthetic_Pivoting.pdf | International Journal of Engineering Trends and Technology
ISSN: 2231 – 5381 /doi:10.14445/22315381/IJETT-V69I9P227
Volume 69 Issue 9, 230-235, September, 2021
© 2021 Seventh Sense Research Group®
Original Article
Attention based Sequence to Sequence Learning for
Machine Translation of Low Resourced Indic ... |
synthetic_cpt | 7 | TransformLLM_Adapting_Large_Language_Models_via_LLM-Transformed_Reading_Comprehension_Text.pdf | TRANSFORMLLM: ADAPTING LARGE LANGUAGE MODELS VIA
LLM-TRANSFORMED READING COMPREHENSION TEXT
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Iftach Arbel
School of Mathematical Sciences
Tel Aviv University
Tel Aviv, Israel
i.arbel84@gmail.com
Yehonathan Refael
Department of Electrical Engineeri... |
synthetic_cpt | 1 | Chemometric_Quality_Assessment_of_Doxylamine_Succinate_with_Its_Degradation_product;_Implementation_of_Two_Predictive_Models_on_UV-Spectrophotometric_Data_of_Anti-emetic_binary_Mixture.pdf | Artificial Intelligence for reverse engineering:
application to detergents using Raman spectroscopy.
Pedro Marote1, Marie Martin1, Anne, Bonhommé², Pierre Lantéri1, Yohann Clément1*
1 Université de Lyon, Institut des Sciences Analytiques, UMR 5280 CNRS, Université
Claude Bernard Lyon 1, 5 rue de la Doua, 69100 Vil... |
synthetic_cpt | 4 | Reward_Modeling_with_Weak_Supervision_for_Language_Models.pdf | Reward Modeling with Weak Supervision for Language Models
Ben Hauptvogel1, Malte Ostendorff2, Georg Rehm2,3, Sebastian Möller1,3
1Technical University of Berlin 2Occiglot
3DFKI GmbH
Corresponding author: b.hauptvogel@tu-berlin.de
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Rec... |
synthetic_cpt | 2 | Words_Matter_Leveraging_Individual_Text_Embeddings_for_Code_Generation_in_CLIP_Test-Time_Adaptation.pdf | MarkBERT: Marking Word Boundaries Improves Chinese BERT
Linyang Li2* ,Yong Dai1, Duyu Tang1† , Xipeng Qiu2, Zenglin Xu3, Shuming Shi1
1 Tencent AI Lab, China,2 Fudan University,3 PengCheng Laboratory
{yongdai,duyutang}@tencent.com,
{linyangli19, xpqiu}@fudan.edu.cn
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synthetic_cpt | 2 | Style-Content_Disentanglement_in_Language-Image_Pretraining_Representations_for_Zero-Shot_Sketch-to-Image_Synthesis.pdf | A Unified Framework for Generalizable Style
Transfer: Style and Content Separation
Yexun Zhang, Student Member, IEEE, Ya Zhang, Member, IEEE, and Wenbin Cai, Member, IEEE
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Abstract—Image style transfer has drawn broad attention in
recent years... |
synthetic_cpt | 2 | Reflexive_Guidance_Improving_OoDD_in_Vision-Language_Models_via_Self-Guided_Image-Adaptive_Concept_Generation.pdf | Proofs of the Technical Results Justifying a Biologically Inspired
Algorithm for Reactive Navigation of Nonholonomic Robots in
Maze-Like Environments
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Alexey S. Matveev a, Michael C. Hoy b, Andrey V. Savkin b
aDepartment of Mathematics and Mec... |
synthetic_cpt | 1 | A_User-Centered_Evaluation_of_the_Data-Driven_Sign_Language_Avatar_System_A_Pilot_Study.pdf | Rate Regions of Secret Key Sharing in a New Source Model1,2
Somayeh Salimi*, Mahmoud Salmasizadeh†, Mohammad Reza Aref*
*ISSL Lab., Dept. of Electrical Engineering, Sharif University of Technology, Tehran, Iran
†Electronics Research Center, Sharif University of Technology, Tehran, Iran
Email: salimi@ee.sharif.ed... |
synthetic_cpt | 4 | DART-Math_Difficulty-Aware_Rejection_Tuning_for_Mathematical_Problem-Solving.pdf | 6
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CONNECTEDNESS OF THE DART DIGRAPH AND THE
SQUARED-DART DIGRAPH
PRIMOˇZ POTO ˇCNIK AND STEVE WILSON
Abstract. In this note we revisit the dart graph and the squared dart digraph
constructions and prove that they yield strongly connected digr... |
synthetic_cpt | 4 | Gecko_Versatile_Text_Embeddings_Distilled_from_Large_Language_Models.pdf | 4
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GECKO: Generative Language Model for English,
Code and Korean
Sungwoo Oh
KIFAI∗
david.oh0126@gmail.com
Donggyu Kim
KIFAI
donggyukimc@gmail.com
Abstract
We introduce GECKO, a bilingual large language model (LLM) optimized for
Korean and English,... |
synthetic_cpt | 8 | Quality_Matters_Evaluating_Synthetic_Data_for_Tool-Using_LLMs.pdf | Quality Measures in Biometric Systems
Fernando Alonso-Fernandez1, Member, IEEE, Julian Fierrez, Member, IEEE
Javier Ortega-Garcia, Senior Member, IEEE
Abstract— Biometric technology has been
increasingly
deployed in the last decade, offering greater security and
convenience than traditional metho... |
synthetic_cpt | 8 | Beware_of_Calibration_Data_for_Pruning_Large_Language_Models.pdf | 4
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Preprint
BEWARE OF CALIBRATION DATA FOR PRUNING
LARGE LANGUAGE MODELS
Yixin Ji1, Yang Xiang1, Juntao Li1∗,
Qingrong Xia2, Ping Li2, Xinyu Duan2, Zhefeng Wang2, Min Zhang1
1School of Computer Science and Technology, Soochow University
2Huawei Clou... |
synthetic_cpt | 5 | Self-Play_Fine-Tuning_Converts_Weak_Language_Models_to_Strong_Language_Models.pdf | 1
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Non-abelian self-duality from self-interaction
A. Khoudeir
Instituto de F´ısica, Universidad Nacional Aut´onoma de M´exico
Apdo. Postal 20-364, 01000 M´exico D. F. M´exico
and
Centro de Astrof´ısica Te´orica, Departamento de F´ısica, Facultad de
Ciencia... |
synthetic_cpt | 2 | Surface_Form_Competition_Why_the_Highest_Probability_Answer_Isn’t_Always_Right.pdf | Molecular explanation for why talc surfaces can be both
hydrophilic and hydrophobic
Benjamin Rotenberg
CNRS et UPMC-Paris6, Laboratoire PECSA,
UMR 7195, 4 pl. Jussieu, F-75005 Paris, France∗
Amish J. Patel
Howard P. Isermann Department of Chemical & Biological Engineering,
and Center for Biotechnology and Interd... |
synthetic_cpt | 2 | Can_Models_Help_Us_Create_Better_Models_Evaluating_LLMs_as_Data_Scientists.pdf | KAUCUS: Knowledge Augmented User Simulators for Training Language
Model Assistants
Kaustubh D. Dhole
Department of Computer Science
Emory University
Atlanta, USA
kdhole@emory.edu
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Abstract
An effective multi-turn instruction-following
assistant... |
synthetic_cpt | 1 | Intra_prediction_using_template_matching_with_adaptive_illumination_compensation.pdf | 2
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GENERATIVE TARGET UPDATE FOR ADAPTIVE
SIAMESE TRACKING
A PREPRINT
Madhu Kiran∗†
Le Thanh Nguyen-Meidine∗
Rajat Sahay‡
Rafael Menelau Oliveira E Cruz∗
Louis-Antoine Blais-Morin§
Eric Granger∗
February 22, 2022
ABSTRACT
Siamese trackers pe... |
synthetic_cpt | 1 | Using_Large_Language_Models_in_Automatic_Hint_Ranking_and_Generation_Tasks.pdf | Using Large Language Models in Automatic Hint Ranking and Generation
Tasks
Jamshid Mozafari
University of Innsbruck
jamshid.mozafari@uibk.ac.at
Florian Gerhold
University of Innsbruck
florian.gerhold@student.uibk.ac.at
Adam Jatowt
University of Innsbruck
adam.jatowt@uibk.ac.at
Abstract
The use of Large Language Mo... |
synthetic_cpt | 4 | Tailored-LLaMA_Optimizing_Few-Shot_Learning_in_Pruned_LLaMA_Models_with_Task-Specific_Prompts.pdf | Tailors: New Music Timbre Visualizer
to Entertain Music Through Imagery
음악의 음색을 강조한 시각화 시스템 개발:
심상 형성과 음악 향유 중심의 분석
Contents
Abstract
List of Tables
List of Figures
I. Introduction
II. Related Works & Background
2. 1. Timbre
2. 2. Music Vi... |
synthetic_cpt | 1 | OFDM_Emitter_Identification_Method_Based_on_Data_Augmentation_and_Contrastive_Learning.pdf | IEEE WIRELESS COMMUNICATIONS LETTERS, VOL. XX, NO. XX, XXX 2022
1
Few-Shot Specific Emitter Identification via Hybrid
Data Augmentation and Deep Metric Learning
Cheng Wang, Xue Fu, Yu Wang, Guan Gui, Senior Member, IEEE, Haris Gacanin, Fellow, IEEE,
Hikmet Sari, Life Fellow, IEEE, and Fumiyuki Adachi, Life Fellow, IEE... |
synthetic_cpt | 8 | Data_Selection_for_Language_Models_via_Importance_Resampling.pdf | 3
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Data Selection for Language Models
via Importance Resampling
Sang Michael Xie, Shibani Santurkar, Tengyu Ma, Percy Liang
Stanford University
{xie, shibani, tengyuma, pliang}@cs.stanford.edu
Abstract
Selecting a suitable pretraining dataset is cr... |
synthetic_cpt | 1 | Generative_Adversarial_Networks_for_Synthetic_Data_Generation_in_Finance_Evaluating_Statistical_Similarities_and_Quality_Assessment.pdf | FedSyn: Synthetic Data Generation using Federated
Learning
Monik Raj Behera1, Sudhir Upadhyay1, Suresh Shetty1, Sudha Priyadarshini1, Palka Patel1, Ker Farn Lee1
{monik.r.behera,sudhir.x.upadhyay,suresh.shetty,sudha.priyadarshini}
@jpmorgan.com
1Onyx by J.P. Morgan
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synthetic_cpt | 4 | Ontology-Free_General-Domain_Knowledge_Graph-to-Text_Generation_Dataset_Synthesis_using_Large_Language_Model.pdf | Keet
ONTOLOGY
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The African Wildlife Ontology tutorial ontologies:
requirements, design, and content
C. Maria Keet
Abstract
Background: Most tutorial ontologies focus on illustrating one aspect of ontology development, notably
language features... |
synthetic_cpt | 1 | Image_Quality_Assessment_by_Integration_of_Low-level_&_High-Level_Features_Threshold_Similarity_Index.pdf | BLIND OMNIDIRECTIONAL IMAGE QUALITY ASSESSMENT: INTEGRATING LOCAL
STATISTICS AND GLOBAL SEMANTICS
Wei Zhou and Zhou Wang
Department of Electrical & Computer Engineering, University of Waterloo, Canada
Email: {wei.zhou, zhou.wang}@uwaterloo.ca
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synthetic_cpt | 2 | Detecting_Offensive_Content_in_Open-domain_Conversations_using_Two_Stage_Semi-supervision.pdf | WLV-RIT at SemEval-2021 Task 5: A Neural Transformer Framework
for Detecting Toxic Spans
Tharindu Ranasinghe1, Diptanu Sarkar2, Marcos Zampieri2, Alexander Ororbia2
1University of Wolverhampton,UK
2Rochester Institute of Technology, USA
T.D.RanasingheHettiarachchige@wlv.ac.uk
Abstract
In recent years, the widespread... |
synthetic_cpt | 4 | Gradient_Localization_Improves_Lifelong_Pretraining_of_Language_Models.pdf | Gradient Localization Improves Lifelong Pretraining of Language Models
Jared Fernandez
Yonatan Bisk
Carnegie Mellon University
{jaredfern, ybisk, strubell}@cmu.edu
Emma Strubell
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Abstract
Large Language Models (LLMs) trained on
web-scale text cor... |
synthetic_cpt | 8 | Can_Open-source_LLMs_Enhance_Data_Synthesis_for_Toxic_Detection_An_Experimental_Study.pdf | 3
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OPEN AND CLOSED STRING FIELD THEORY
INTERPRETED IN CLASSICAL ALGEBRAIC
TOPOLOGY
DENNIS SULLIVAN
Dedicated to Graeme Segal on his 60th birthday
Abstract: There is an interpretation of open string field theory in al-
gebraic topology. An... |
synthetic_cpt | 1 | VLMs_meet_UDA_Boosting_Transferability_of_Open_Vocabulary_Segmentation_with_Unsupervised_Domain_Adaptation.pdf | 4
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VLMs meet UDA: Boosting Transferability of Open Vocabulary
Segmentation with Unsupervised Domain Adaptation
Roberto Alcover-Couso1, Marcos Escudero-Vi˜nolo1, Juan C. SanMiguel1 and Jesus Bescos1 ∗
December 13, 2024
Abstract
1
Introduction
Seg... |
synthetic_cpt | 3 | JAPAGEN_Efficient_FewZero-shot_Learning_via_Japanese_Training_Dataset_Generation_with_LLM.pdf | JAPAGEN: Efficient Few/Zero-shot Learning
via Japanese Training Dataset Generation with LLM
Takuro Fujii1,2,∗ and Satoru Katsumata3
1Yokohama National University 2Nomura Research Institute, Ltd.
3Retrieva, Inc.
tkr.fujii.ynu@gmail.com
satoru.katsumata@retrieva.jp
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synthetic_cpt | 3 | Can_Large_Language_Models_Really_Improve_by_Self-critiquing_Their_Own_Plans.pdf | 3
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Can Large Language Models Really Improve by
Self-critiquing Their Own Plans?
Karthik Valmeekam∗
School of Computing & AI
Arizona State University Tempe.
kvalmeek@asu.edu
Matthew Marquez∗
School of Computing & AI
Arizona State University, Tempe.
... |
synthetic_cpt | 1 | LLM_Based_Multi-Agent_Generation_of_Semi-structured_Documents_from_Semantic_Templates_in_the_Public_Administration_Domain.pdf | A Survey of Large Language Models on Generative
Graph Analytics: Query, Learning, and Applications
Wenbo Shang
Department of Computer Science
Hong Kong Baptist University
Hong Kong, China
cswbshang@comp.hkbu.edu.hk
Xin Huang
Department of Computer Science
Hong Kong Baptist University
Hong Kong, China
xinhuang@comp.hk... |
synthetic_cpt | 2 | The_Benefits_of_Bad_Advice_Autocontrastive_Decoding_across_Model_Layers.pdf | 3
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Active causal structure learning with advice
Davin Choo
National University of Singapore
Themis Gouleakis
National University of Singapore
Arnab Bhattacharyya
National University of Singapore
Abstract
We introduce the problem of active causal... |
synthetic_cpt | 4 | Prioritized_Training_on_Points_that_are_Learnable_Worth_Learning_and_Not_Yet_Learnt.pdf | 2
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Prioritized Training on Points that are Learnable,
Worth Learning, and Not Yet Learnt
Sören Mindermann * 1 Jan Brauner * 1 Muhammed Razzak * 1 Mrinank Sharma * 2 Andreas Kirsch 1
Winnie Xu 3 4 Benedikt Höltgen 1 Aidan N. Gomez 3 1 Adrien Morisot ... |
synthetic_cpt | 3 | Improving_Zero-Shot_Multilingual_Text_Generation_via_Iterative_Distillation.pdf | Zero-Inflated Stochastic Volatility Model for Disaggregated
Inflation Data with Exact Zeros
Geonhee Han∗1 and Kaoru Irie†2
1Graduate School of Arts and Sciences, Columbia University
2Faculty of Economics, The University of Tokyo
March 19, 2024
Abstract
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synthetic_cpt | 3 | Procedural_Knowledge_in_Pretraining_Drives_Reasoning_in_Large_Language_Models.pdf | 4
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Preprint, under review.
PROCEDURAL KNOWLEDGE IN PRETRAINING DRIVES
REASONING IN LARGE LANGUAGE MODELS
Laura Ruis∗
AI Centre, UCL
Maximilian Mozes
Cohere
Juhan Bae
University of Toronto & Vector Institute
Siddhartha Rao Kamalakara
Cohere
Dwara... |
synthetic_cpt | 1 | Non-Autoregressive_Fully_Parallel_Deep_Convolutional_Neural_Speech_Synthesis.pdf | hep-th/0107226
NSF-ITP-01-74
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Non-Linear / Non-Commutative
Non-Abelian Monopoles
Koji Hashimoto∗
Institute for Theoretical Physics, University of California,
Santa Barbara, CA 93106-4030
Abstract
Using recently proposed non-linearly realized supersym... |
synthetic_cpt | 3 | L2G_Repurposing_Language_Models_for_Genomics_Tasks.pdf | 9
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L2G Auto-encoder: Understanding Point Clouds by
Local-to-Global Reconstruction with Hierarchical Self-Attention
Xinhai Liu
School of Software, Tsinghua
University & Beijing National
Research Center for Information
Science and Technology (BNRist)
Be... |
synthetic_cpt | 1 | A_Comprehensive_Evaluation_of_Large_Language_Models_on_Aspect-Based_Sentiment_Analysis.pdf | SPOR: A Comprehensive and Practical Evaluation Method for
Compositional Generalization in Data-to-Text Generation
Ziyao Xu, Houfeng Wang
National Key Laboratory for Multimedia Information Processing, Peking University
{xzyxzy,wanghf}@pku.edu.cn
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synthetic_cpt | 2 | Self-Play_Fine-Tuning_of_Diffusion_Models_for_Text-to-Image_Generation.pdf | 1
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Non-abelian self-duality from self-interaction
A. Khoudeir
Instituto de F´ısica, Universidad Nacional Aut´onoma de M´exico
Apdo. Postal 20-364, 01000 M´exico D. F. M´exico
and
Centro de Astrof´ısica Te´orica, Departamento de F´ısica, Facultad de
Ciencia... |
synthetic_cpt | 3 | STraTA_Self-Training_with_Task_Augmentation_for_Better_Few-shot_Learning.pdf | CONNECTED COMPONENTS OF STRATA OF RESIDUELESS MEROMORPHIC
DIFFERENTIALS
MYEONGJAE LEE
Abstract. Generalized strata of meromorphic differentials are loci in the usual strata of differentials, where
certain sets of residues sum up to zero. They appear naturally in the boundary of the multi-scale compact-
ification of the ... |
synthetic_cpt | 3 | Cold-Start_Data_Selection_for_Few-shot_Language_Model_Fine-tuning_A_Prompt-Based_Uncertainty_Propagation_Approach.pdf | A network-based biomarkers discovery of Cold/Hot ZHENG
chronic gastritis and Cold/Hot herbs of formulae
Boyang Wanga, Pan Chena, Peng Zhanga and Shao Lia,*
aInstitute for TCM-X, MOE Key Laboratory of Bioinformatics, Bioinformatics
Division, BNRist, Department of Automation, Tsinghua University... |
synthetic_cpt | 7 | Generate_Annotate_and_Learn_NLP_with_Synthetic_Text.pdf | 4
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Generalized Baer and Generalized Quasi-Baer Rings of
Skew Generalized Power Series
R. M. Salema, R. E. Abdel-Khaleka, M. M. Hamamb
aDepartment of Mathematics, Faculty of Science, Al-Azhar Univ., Nasr City 11884, Cairo, Egypt.
bDepartment of ... |
synthetic_cpt | 1 | Uncertainty-Guided_Optimization_on_Large_Language_Model_Search_Trees.pdf | The Creation of Puffin, the Automatic Uncertainty Compiler
Nicholas Graya,b,
∗, Marco de Angelisa, Scott Fersona
aInstitute for Risk and Uncertainty, University of Liverpool, Liverpool, United Kingdom, L69 7ZX
bnickgray@liverpool.ac.uk
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... |
synthetic_cpt | 1 | Shift-Collapse_Acceleration_of_Generalized_Polarizable_Reactive_Molecular_Dynamics_for_Machine_Learning-Assisted_Computational_Synthesis_of_Layered_Materials.pdf | 8
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ARC SHIFT NUMBER AND REGION ARC SHIFT
NUMBER FOR VIRTUAL KNOTS
K. KAUR, A. GILL, AND M. PRABHAKAR
Abstract. In this paper, we formulate a new local move on virtual
knot diagram, called arc shift move. Further, we extend it to another
local m... |
synthetic_cpt | 4 | UltraFeedback_Boosting_Language_Models_with_High-quality_Feedback.pdf | ULTRAFEEDBACK: Boosting Language Models with Scaled AI Feedback
Ganqu Cui * 1 Lifan Yuan * 1 2 Ning Ding 1 Guanming Yao 1 3 Bingxiang He 1 Wei Zhu 4 Yuan Ni 4
Guotong Xie 4 Ruobing Xie 5 Yankai Lin 6 Zhiyuan Liu 1 Maosong Sun 1 7
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Learni... |
synthetic_cpt | 1 | Towards_Semi-Automated_Construction_of_Laboratory_Test_Result_Comprehension_Knowledgebase_for_a_Patient-Facing_Application.pdf | 8
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Towards a specialization map modulo semi-orthogonal decompositions
Xiaowen Hu
Abstract
We propose a conjecture on the existence of a specialization map for derived cate-
gories of smooth proper varieties modulo semi-orthogonal decompositions... |
synthetic_cpt | 3 | TrueTeacher_Learning_Factual_Consistency_Evaluation_with_Large_Language_Models.pdf | TrueTeacher: Learning Factual Consistency Evaluation
with Large Language Models
Zorik GekhmanT,G,∗ Jonathan HerzigG Roee AharoniG
Chen ElkindG Idan SzpektorG
T Technion - Israel Institute of Technology
GGoogle Research
zorik@campus.technion.ac.il
{zorik|jherzig|roeeaharoni|chenel|szpektor}@google.com
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synthetic_cpt | 2 | Dynamic_Sparse_No_Training_Training-Free_Fine-tuning_for_Sparse_LLMs.pdf | Published as a conference paper at ICLR 2024
DYNAMIC SPARSE NO TRAINING ○:
TRAINING-FREE FINE-TUNING FOR SPARSE LLMS
Yuxin Zhang1† Lirui Zhao1† Mingbao Lin2 Yunyun Sun3 Yiwu Yao3
Xingjia Han3
Shiwei Liu4,5,6 Rongrong Ji1,7‡∗
Jared Tanner4
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1Ke... |
synthetic_cpt | 3 | kNN-Adapter_Efficient_Domain_Adaptation_for_Black-Box_Language_Models.pdf | kNN-BOX: A Unified Framework for Nearest Neighbor Generation
Wenhao Zhu∗, Qianfeng Zhao∗, Yunzhe Lv∗,
Shujian Huang, Siheng Zhao, Sizhe Liu, Jiajun Chen
National Key Laboratory for Novel Software Technology, Nanjing University, China
{zhuwh, qianfeng, lvyz, zhaosh, liusz}@smail.nju.edu.cn, {huangsj, chenjj}@nju.edu.cn
... |
synthetic_cpt | 2 | Finding_needles_in_a_haystack_Sampling_Structurally-diverse_Training_Sets_from_Synthetic_Data_for_Compositional_Generalization.pdf | Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes
Julian Lorenz
Florian Barthel
Daniel Kienzle
Rainer Lienhart
University of Augsburg
Augsburg, Germany
{julian.lorenz,florian.barthel,daniel.kienzle,rainer.lienhart}@uni-a.de
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synthetic_cpt | 1 | Large_language_model_based_framework_for_automated_extraction_of_genetic_interactions_from_unstructured_data.pdf | GAMEDX: GENERATIVE AI-BASED MEDICAL ENTITY DATA
EXTRACTOR USING LARGE LANGUAGE MODELS
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Mohammed-Khalil Ghali, Abdelrahman Farrag, Hajar Sakai, Hicham El Baz, Yu Jin, Sarah Lam
School of Systems Science and Industrial Engineering
State University of... |
synthetic_cpt | 3 | SCAR_Efficient_Instruction-Tuning_for_Large_Language_Models_via_Style_Consistency-Aware_Response_Ranking.pdf | Entanglement Oscillations from Many-Body Quantum Scars
Nicholas O’Dea∗ and Adithya Sriram∗
Department of Physics, Stanford University, Stanford, CA 94305, USA
Quantum scars are nonthermal eigenstates that prevent thermalization of initial states with weight
on the scars. When the scar states are equally spaced in ene... |
synthetic_cpt | 6 | On_Domain-Specific_Post-Training_for_Multimodal_Large_Language_Models.pdf | 0
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Maximal non valuative domains
Rahul Kumar1 & Atul Gaur2
Department of Mathematics
University of Delhi, Delhi, India.
E-Mail: rahulkmr977@gmail.com; gaursatul@gmail.com
Abstract
The notion of maximal non valuative domain is introduced and cha... |
synthetic_cpt | 4 | SyntheT2C_Generating_Synthetic_Data_for_Fine-Tuning_Large_Language_Models_on_the_Text2Cypher_Task.pdf | SyntheT2C: Generating Synthetic Data for Fine-Tuning Large Language
Models on the Text2Cypher Task
Zijie Zhong1, Linqing Zhong2, Zhaoze Sun2, Qingyun Jin3,
Zengchang Qin3, *, and Xiaofan Zhang1, *
1Shanghai Artificial Intelligence Laboratory
2Sino-French Engineer School, Beihang University
3School of Automation Scien... |
synthetic_cpt | 4 | MAF_Multi-Aspect_Feedback_for_Improving_Reasoning_in_Large_Language_Models.pdf | 0
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A powerful MAF-neutral allele-based test
for case-control association studies
M. A. Jonkera, J. Pecankab
aRadboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Netherlands
bDepartment of Biomedical Data Science... |
synthetic_cpt | 1 | Aligning_CodeLLMs_with_Direct_Preference_Optimization.pdf | Aligning CodeLLMs with Direct Preference Optimization
Yibo Miao1, Bofei Gao2, Shanghaoran Quan3, Junyang Lin3, Daoguang Zan4,
Jiaheng Liu3, Jian Yang3, Tianyu Liu3*, Zhijie Deng1†
1Shanghai Jiao Tong University
2Peking University
3Alibaba Group
4Institute of Software, Chinese Academy of Sciences
{miaoyibo, zhijie... |
synthetic_cpt | 2 | Fine-grained_Pluggable_Gradient_Ascent_for_Knowledge_Unlearning_in_Language_Models.pdf | 9
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CONTINUITY PROPERTIES OF FINELY
PLURISUBHARMONIC FUNCTIONS AND
PLURIPOLARITY
SAID EL MARZGUIOUI AND JAN WIEGERINCK
Abstract. We prove that every bounded finely plurisubharmonic func-
tion can be locally (in the pluri-fine topology) written as ... |
synthetic_cpt | 1 | ToolQA_A_Dataset_for_LLM_Question_Answering_with_External_Tools.pdf | 3
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ToolQA: A Dataset for LLM Question Answering
with External Tools
Yuchen Zhuang∗, Yue Yu∗, Kuan Wang∗, Haotian Sun, Chao Zhang
College of Computing, Georgia Institute of Technology, Atlanta GA
{yczhuang, yueyu, kuanwang, haotian.sun, chaozhang}@ga... |
synthetic_cpt | 2 | Simplifying_CLIP_Unleashing_the_Power_of_Large-Scale_Models_on_Consumer-level_Computers.pdf | 1
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Nonlinear Multi-Carrier System with Signal
Clipping: Measurement, Analysis, and Optimization
Yuyang Du, Graduate Student Member, IEEE, Liang Hao, Member, IEEE,
Yiming Lei, Member, IEEE, Qun Yang, Student Member, IEEE, Shiqi Xu, Student Mem... |
synthetic_cpt | 1 | Non-Reference_Quality_Assessment_for_Medical_Imaging_Application_to_Synthetic_Brain_MRIs.pdf | hep-th/0107226
NSF-ITP-01-74
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Non-Linear / Non-Commutative
Non-Abelian Monopoles
Koji Hashimoto∗
Institute for Theoretical Physics, University of California,
Santa Barbara, CA 93106-4030
Abstract
Using recently proposed non-linearly realized supersym... |
synthetic_cpt | 2 | Distilling_Knowledge_from_Reader_to_Retriever_for_Question_Answering.pdf | Published as a conference paper at ICLR 2021
DISTILLING KNOWLEDGE
RETRIEVER FOR QUESTION ANSWERING
FROM READER
TO
Gautier Izacard1,2,3, Edouard Grave1
1Facebook AI Research, 2 ´Ecole normale sup´erieure, PSL University, 3Inria
gizacard|egrave
@fb.com
{
}
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synthetic_cpt | 2 | SayPlan_Grounding_Large_Language_Models_using_3D_Scene_Graphs_for_Scalable_Task_Planning.pdf | SayPlan: Grounding Large Language Models using
3D Scene Graphs for Scalable Robot Task Planning
Krishan Rana†1, Jesse Haviland∗1,2, Sourav Garg∗3, Jad Abou-Chakra∗1,
Ian Reid3, Niko S ¨underhauf1
1QUT Centre for Robotics, Queensland University of Technology
2CSIRO Data61 Robotics and Autonomous Systems Group
3Universi... |
synthetic_cpt | 1 | Pretrained_Language_Models_for_Semantics-Aware_Data_Harmonisation_of_Observational_Clinical_Studies_in_the_Era_of_Big_Data.pdf | Explicit Pairwise Word Interaction Modeling Improves Pretrained
Transformers for English Semantic Similarity Tasks
Yinan Zhang, Raphael Tang, and Jimmy Lin
David R. Cheriton School of Computer Science
University of Waterloo
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Abstract
In English sem... |
synthetic_cpt | 1 | Sample-Efficient_Unsupervised_Domain_Adaptation_of_Speech_Recognition_Systems_A_Case_Study_for_Modern_Greek.pdf | The General sampling theorem, Compressed
sensing and a method of image sampling and
reconstruction with sampling rates close to the
theoretical limit
L. Yaroslavsky
School of Electrical Engineering, Tel Aviv University, Tel Aviv, Israel
E-mail: yaro@eng.tau.ac.il
Abstract
The article addresses the problem ... |
synthetic_cpt | 1 | Harvest_Video_Foundation_Models_via_Efficient_Post-Pretraining.pdf | 3
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Harvest Video Foundation Models via Efficient Post-Pretraining
Yizhuo Li1,2∗, Kunchang Li2,3∗, Yinan He2, Yi Wang2,
Yali Wang2,3, Limin Wang2,4, Yu Qiao2,3, Ping Luo1,2
1The University of Hong Kong, 2Shanghai AI Lab, 3Shenzhen Institutes of Advan... |
synthetic_cpt | 3 | Federated_Data-Efficient_Instruction_Tuning_for_Large_Language_Models.pdf | 2
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An Experience Report of Large Scale
Federations
Andreas Schwarte1, Peter Haase1, Michael Schmidt1,
Katja Hose2, and Ralf Schenkel2
1 fluid Operations AG,
69190 Walldorf, Germany,
firstname.lastname@fluidops.com
2 Max-Planck-Institut f¨ur Informatik... |
synthetic_cpt | 2 | The_Super_Weight_in_Large_Language_Models.pdf | 4
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Preprint. Under review.
THE SUPER WEIGHT IN LARGE LANGUAGE MODELS
Mengxia Yu1∗, De Wang2, Qi Shan2, Colorado Reed2†, Alvin Wan2
2Apple
1University of Notre Dame
ABSTRACT
Recent works have shown a surprising result: a small fraction of Large Lan... |
synthetic_cpt | 6 | Synthetic_Data_Generation_with_Large_Language_Models_for_Text_Classification_Potential_and_Limitations.pdf | Synthetic Data Generation with Large Language Models for Text
Classification: Potential and Limitations
Zhuoyan Li1, Hangxiao Zhu2, Zhuoran Lu1, Ming Yin1
1Purdue University
2Washington University in St. Louis
{li4178, lu800, mingyin}@purdue.edu, hangxiao@wustl.edu
Abstract
The collection and curation of high-qualit... |
synthetic_cpt | 8 | Unveiling_the_Flaws_Exploring_Imperfections_in_Synthetic_Data_and_Mitigation_Strategies_for_Large_Language_Models.pdf | 4
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Unveiling the Flaws: Exploring Imperfections in Synthetic
Data and Mitigation Strategies for Large Language Models
Jie Chen1,2∗, Yupeng Zhang1∗, Bingning Wang1†, Wayne Xin Zhao2†,
Ji-Rong Wen2, and Weipeng Chen1
1Baichuan Inc.
2Gaoling School of... |
synthetic_cpt | 1 | Quality_Assessment_of_Synthetic_Fluorescence_Microscopy_Images_for_Image_Segmentation.pdf | 8
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Three Dimensional Fluorescence Microscopy
Image Synthesis and Segmentation
Chichen Fu
Purdue University
West Lafayette, Indiana
Soonam Lee
Purdue University
West Lafayette, Indiana
David Joon Ho
Purdue University
West Lafayette, Indiana
Shuo H... |
synthetic_cpt | 3 | Can_LLMs_Learn_from_Previous_Mistakes_Investigating_LLMs'_Errors_to_Boost_for_Reasoning.pdf | Can LLMs Learn from Previous Mistakes? Investigating LLMs’ Errors to
Boost for Reasoning
Yongqi Tong1, Dawei Li1, Sizhe Wang2, Yujia Wang1, Fei Teng1, Jingbo Shang1∗
1University of California, San Diego, {yotong, dal034, yuw103, feteng, jshang}@ucsd.edu
3University of Southern California, sizhewan@usc.edu
4
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synthetic_cpt | 1 | Impact-generated_seismic_signals_on_Mars.pdf | manuscript submitted to JGR: Planets
Effect of impact velocity and angle on deformational
heating and post-impact temperature
S. Wakita1,2, H. Genda3, K. Kurosawa4, T. M. Davison 5, and B. C. Johnson
1,6
1Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN, USA
2Department ... |
synthetic_cpt | 2 | Towards_More_Effective_Table-to-Text_Generation_Assessing_In-Context_Learning_and_Self-Evaluation_with_Open-Source_Models.pdf | 4
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Towards End-to-End Semi-Supervised Table Detection
with Semantic Aligned Matching Transformer
Tahira Shehzadi*1,2,3[0000−0002−7052−979X], Shalini Sarode1,3[0009−0007−9968−4068],
Didier Stricker1,2,3, and Muhammad Zeshan Afzal1,2,3[0000−0002−0536−... |
synthetic_cpt | 1 | Efficient_domain_adaptation_of_language_models_in_ASR_systems_using_Prompt-tuning.pdf | PROMPT TUNING GPT-2 LANGUAGE MODEL FOR PARAMETER-EFFICIENT
DOMAIN ADAPTATION OF ASR SYSTEMS
Saket Dingliwal, Ashish Shenoy*, Sravan Bodapati, Ankur Gandhe, Ravi Teja Gadde, Katrin Kirchhoff
{skdin, ashenoy, sravanb, aggandhe, gadderav, katrinki}@amazon.com
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synthetic_cpt | 2 | SLED_Self_Logits_Evolution_Decoding_for_Improving_Factuality_in_Large_Language_Models.pdf | Submitted to ‘Chinese Physics C'
Modeling and Analysis of SLED
LI Lin1,2 , FANG WenCheng1 ,WANG Chao-Peng1 ,GU Qiang1*
1 Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
2 University of Chinese Academy of Science, Beijing 100049, China
Abstract SLED is a crucial compo... |
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