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 | 7 | ProGen_Progressive_Zero-shot_Dataset_Generation_via_In-context_Feedback.pdf | ProGen:RevisitingProbabilisticSpatial-TemporalTimeSeriesForecastingfromaContinuousGenerativePerspectiveUsingStochasticDifferentialEquationsMingzeGong,LeiChen,JiaLiHongKongUniversityofScienceandTechnology(Guangzhou)mgong081@connect.hkust-gz.edu.cnAbstractAccurateforecastingofspatiotemporaldataremainschal-lengingduetocom... |
synthetic_cpt | 4 | Can_Large_Language_Models_Invent_Algorithms_to_Improve_Themselves.pdf | Can Large Language Models Invent Algorithms to Improve Themselves?
Yoichi Ishibashi*
NEC
Taro Yano
NEC
Masafumi Oyamada
NEC
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Abstract
Large Language Models (LLMs) have shown
remarkable performance improvements and are
rapidly gaining adoption i... |
synthetic_cpt | 4 | LLM_Distillation_for_Efficient_Few-Shot_Multiple_Choice_Question_Answering.pdf | 4
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Under Review
LLM DISTILLATION FOR EFFICIENT FEW-SHOT MUL-
TIPLE CHOICE QUESTION ANSWERING
Patrick Sutanto, Joan Santoso
Department of Informatics
Institut Sains dan Teknologi Terpadu Surabaya (ISTTS)
Surabaya, East Java, Indonesia
patrick.s21@mhs.... |
synthetic_cpt | 2 | Improved_Baselines_with_Visual_Instruction_Tuning.pdf | Learning to Follow Object-Centric Image Editing Instructions Faithfully
Tuhin Chakrabarty1∗ Kanishk Singh1∗ Arkadiy Saakyan1
Smaranda Muresan1,2
1Department of Computer Science, Columbia University
2Data Science Institute, Columbia University
tuhin.chakr@cs.columbia.edu, ks4038@columbia.edu, a.saakyan@cs.columbia.ed... |
synthetic_cpt | 1 | The_Simple4All_entry_to_the_Blizzard_Challenge_2013.pdf | Blizzard: Adding True Persistence to Main Memory
Data Structures
Daniel Zahka
Georgia Institute of Technology
Atlanta, USA
Pradeep Fernando
Georgia Institute of Technology
Atlanta, USA
Subramanya R. Dulloor
Kumo.AI
Mountain View, USA
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Amitabha R... |
synthetic_cpt | 2 | Few-shot_Natural_Language_Generation_for_Task-Oriented_Dialog.pdf | 5
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Relativistic few-body methods
W. N. Polyzou
Department of Physics and Astronomy
The University of Iowa
Iowa City, IA 52242, USA
Contribution to the 21-st International Conference on Few-Body Problems in
Physics
I discuss the role of relativist... |
synthetic_cpt | 4 | Differentially_Private_Language_Models_for_Secure_Data_Sharing.pdf | 9
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dpUGC: Learn Differentially Private
Representation for User Generated Contents
Xuan-Son Vu1, Son N. Tran2, Lili Jiang1
1Department of Computing Science, Ume˚a University, Sweden;
2ICT Discipline, University of Tasmania, Australia;
1{sonvx, lili.j... |
synthetic_cpt | 2 | Automatic_Model_Selection_with_Large_Language_Models_for_Reasoning.pdf | A Systematic Evaluation of Large Language Models for Natural
Language Generation Tasks
Xuanfan Ni, Piji Li∗
College of Computer Science and Technology,
Nanjing University of Aeronautics and Astronautics
MIIT Key Laboratory of Pattern Analysis and Machine Intelligence
{xuanfanni, pjli}@nuaa.edu.cn
Abstract
Recent eff... |
synthetic_cpt | 2 | Chain_of_Thought_Prompting_Elicits_Reasoning_in_Large_Language_Models.pdf | Chain-of-Thought Prompting Elicits Reasoning
in Large Language Models
Jason Wei
Xuezhi Wang
Dale Schuurmans
Maarten Bosma
Brian Ichter
Fei Xia
Ed H. Chi
Quoc V. Le
Denny Zhou
Google Research, Brain Team
{jasonwei,dennyzhou}@google.com
Abstract
We explore how generating a chain of thought—a series of interme... |
synthetic_cpt | 1 | Improving_Data_Quality_with_Training_Dynamics_of_Gradient_Boosting_Decision_Trees.pdf | 4
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Improving Data Quality with Training Dynamics of
Gradient Boosting Decision Trees
A Preprint
Moacir A. Ponti∗, Lucas de Angelis Oliveira
Mercado Livre
Osasco, Brazil
moacir.ponti@mercadolibre.com
Valentina Garcia
Mercado Libre
Medellín, Colombi... |
synthetic_cpt | 2 | Improving_End-to-End_Speech_Translation_by_Imitation-Based_Knowledge_Distillation_with_Synthetic_Transcripts.pdf | IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING (J-STSP)
1
End-to-end Networks for Supervised Single-channel
Speech Separation
Shrikant Venkataramani, Student Member, IEEE, Paris Smaragdis, Fellow, IEEE,
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Abstract—The performance of singl... |
synthetic_cpt | 3 | WizardMath_Empowering_Mathematical_Reasoning_for_Large_Language_Models_via_Reinforced_Evol-Instruct.pdf | 3
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WizardMath: Empowering Mathematical Reasoning
for Large Language Models via
Reinforced Evol-Instruct
Haipeng Luo2∗ Qingfeng Sun1∗ Can Xu1† Pu Zhao1
Jianguang Lou1
Chongyang Tao1 Xiubo Geng1 Qingwei Lin1
Shifeng Chen2† Dongmei Zhang1
1Microsoft... |
synthetic_cpt | 1 | VisualGPT_Data-efficient_Adaptation_of_Pretrained_Language_Models_for_Image_Captioning.pdf | VisualGPT: Data-efficient Adaptation of Pretrained Language Models for Image
Captioning
Jun Chen1 , Han Guo2, Kai Yi 1, Boyang Li3, Mohamed Elhoseiny1
1 King Abdullah University of Science and Technology (KAUST),
2Carnegie Mellon University, 3 Nanyang Technological University
{jun.chen,kai.yi,mohamed.elhoseiny}@kaust.... |
synthetic_cpt | 2 | Active_Prompt_Learning_with_Vision-Language_Model_Priors.pdf | Visual Attention Prompted Prediction and Learning
Yifei Zhang1∗ , Bo Pan1 , Siyi Gu2 , Guangji Bai1 , Meikang Qiu3 ,
Xiaofeng Yang1 , Liang Zhao1
1Emory University
2Stanford University
3Augusta University
{yifei.zhang2, bo.pan, guangji.bai, xyang43, liang.zhao}@emory.edu, sgu33@stanford.edu,
qiumeikang@yahoo.com
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synthetic_cpt | 2 | SCAR_Sparse_Conditioned_Autoencoders_for_Concept_Detection_and_Steering_in_LLMs.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 | 2 | Interpreting_Pretrained_Language_Models_via_Concept_Bottlenecks.pdf | Interpreting Pretrained Language Models via Concept Bottlenecks
Zhen Tan
Arizona State University
ztan36@asu.edu
Lu Cheng
University of Illinois Chicago
lucheng@uic.edu
Song Wang
University of Virginia
sw3wv@virginia.edu
Yuan Bo
Zhejiang University
byuan@zju.edu.cn
Jundong Li
University of Virginia
jundong@virgini... |
synthetic_cpt | 3 | Efficient_Alignment_of_Large_Language_Models_via_Data_Sampling.pdf | 4
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Efficient Alignment of Large Language Models via
Data Sampling
Amrit Khera1
∗
, Rajat Ghosh2, Debojyoti Dutta2
1Georgia Institute of Technology, 2Nutanix
akhera30@gatech.edu, {rajat.ghosh, debojyoti.dutta}@nutanix.com
Abstract
LLM alignment e... |
synthetic_cpt | 3 | Cold-Start_Data_Selection_for_Better_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 | 4 | Augmenting_Math_Word_Problems_via_Iterative_Question_Composing.pdf | Augmenting Math Word Problems via Iterative Question Composing
Haoxiong Liu1*†, Yifan Zhang1*, Yifan Luo1 2, Andrew Chi-Chih Yao1 2
1Institute for Interdisciplinary Information Sciences, Tsinghua University, 2Shanghai Qizhi Institute
{liuhx20,zhangyif21,luoyf24}@mails.tsinghua.edu.cn, andrewcyao@tsinghua.edu.cn
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synthetic_cpt | 1 | SPHINX_The_Joint_Mixing_of_Weights_Tasks_and_Visual_Embeddings_for_Multi-modal_Large_Language_Models.pdf | 6
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Matching Rules for the Sphinx Tiling Substitution
Chaim Goodman-Strauss
Univ. Arkansas
strauss@uark.edu
This is a copy of notes dated August 14, 2003, available as [4], here transliterated into a more
traditional format, with some amendment... |
synthetic_cpt | 3 | RECOST_External_Knowledge_Guided_Data-efficient_Instruction_Tuning.pdf | RECOST: External Knowledge Guided Data-efficient Instruction Tuning
Qi Zhang, Yiming Zhang, Haobo Wang, Junbo Zhao
Zhejiang University
{cheung_se,yimingz,wanghaobo,j.zhao}@zju.edu.cn
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Abstract
In the current landscape of large language
models (LL... |
synthetic_cpt | 4 | ReGen_Zero-Shot_Text_Classification_via_Training_Data_Generation_with_Progressive_Dense_Retrieval.pdf | Regenerative partition structures ∗
Alexander Gnedin† and
Jim Pitman‡
September 8, 2018
Abstract
We consider Kingman’s partition structures which are regenerative with respect to a general operation
of random deletion of some part. Prototypes of this class are the Ewens partition structures which
Kingman character... |
synthetic_cpt | 8 | Magpie_Alignment_Data_Synthesis_from_Scratch_by_Prompting_Aligned_LLMs_with_Nothing.pdf | Related papers at https://gipplab.org/pub
Preprint of the paper:
Horych, T. & Wessel, M. & Wahle, J. & Ruas, T. & Wassmuth, J. & Greiner-Petter, A
& Aizawa, A & Gipp, B & Spinde, T, "MAGPIE: Multi-Task Analysis of Media-Bias Gen-
eralization with Pre-Trained Identification of Expressions", in Proceedings of the 2024
J... |
synthetic_cpt | 2 | Retaining_and_Enhancing_Pre-trained_Knowledge_in_Vision-Language_Models_with_Prompt_Ensembling.pdf | 4
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Adaptive Rank, Reduced Forgetting: Knowledge Retention in Continual
Learning Vision-Language Models with Dynamic Rank-Selective LoRA
Haodong Lu1,2, Chongyang Zhao1, Jason Xue2, Lina Yao2,1, Kristen Moore2, Dong Gong1*
1University of New South Wales,... |
synthetic_cpt | 2 | Unleashing_the_Potential_of_Compact_Language_Models_A_Context-Optimized_Soft_Prompting_Approach.pdf | HOLLMWOOD: Unleashing the Creativity of Large Language Models in
Screenwriting via Role Playing
Jing Chen1∗
Xinyu Zhu3∗
Cheng Yang3‡
Yadong Xi2 Yuxiang Zhang4
Junjie Wang4
Chufan Shi3‡
Jiashu Pu2
Rongsheng Zhang2†
1Zhejiang University
Yujiu Yang3
Tian Feng1†
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synthetic_cpt | 2 | TADA_Efficient_Task-Agnostic_Domain_Adaptation_for_Transformers.pdf | On the supersymmetric formulation of Unitary Matrix Model of type IIB
Tsukasa Tadaa and Asato Tsuchiyab
a KEK Theory Group
1-1 Oho, Tsukuba
Ibaraki 305-0801, Japan
tada@ccthmail.kek.jp
b
Department of Physics, Graduate School of Science
Osaka University
Toyonaka, Osaka 560-0043, Japan
tsuchiya@funpth.phys.sci.osaka... |
synthetic_cpt | 2 | Best_Practices_and_Lessons_Learned_on_Synthetic_Data_for_Language_Models.pdf | 4
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Published as a conference paper at COLM 2024
Best Practices and Lessons Learned on Synthetic Data
Ruibo Liu, Jerry Wei, Fangyu Liu
Google DeepMind
ruiboliu@google.com
Chenglei Si, Yanzhe Zhang
Stanford University, Georgia Institute of Technology... |
synthetic_cpt | 2 | ECoFLaP_Efficient_Coarse-to-Fine_Layer-Wise_Pruning_for_Vision-Language_Models.pdf | 4
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Published as a conference paper at ICLR 2024
ECOFLAP: EFFICIENT COARSE-TO-FINE LAYER-WISE
PRUNING FOR VISION-LANGUAGE MODELS
Yi-Lin Sung
Mohit Bansal
Jaehong Yoon
Department of Computer Science, UNC Chapel Hill
{ylsung, jhyoon, mbansal}@cs.unc.... |
synthetic_cpt | 7 | On_the_Diversity_of_Synthetic_Data_and_its_Impact_on_Training_Large_Language_Models.pdf | Preprint
ON THE DIVERSITY OF SYNTHETIC DATA AND ITS IM-
PACT ON TRAINING LARGE LANGUAGE MODELS
Hao Chen1∗, Abdul Waheed1, Xiang Li1, Yidong Wang2
Jindong Wang3,4, Bhiksha Raj1,5, Marah I. Abdin3
Carnegie Mellon University1, Peking University2, Microsoft Research3, William & Mary4, MBZUAI5
haoc3, abdulw, xl6, bhiksha
... |
synthetic_cpt | 1 | Biases_in_Large_Language_Models_Origins_Inventory_and_Discussion.pdf | 3
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Cognitive bias in large language models:
Cautious optimism meets
anti-Panglossian meliorism
David Thorstad | Vanderbilt University
Abstract
Traditional discussions of bias in large language models focus on a conception of bias
closely tied to un... |
synthetic_cpt | 1 | DSMix_Distortion-Induced_Sensitivity_Map_Based_Pre-training_for_No-Reference_Image_Quality_Assessment.pdf | DSMix:Distortion-InducedSensitivityMapBasedPre-trainingforNo-ReferenceImageQualityAssessmentJinsongShi1,2,PanGao1,2⋆,XiaojiangPeng3,andJieQin1,21CollegeofArtificialIntelligence,NanjingUniversityofAeronauticsandAstronautics2TheKeyLaboratoryofBrain-MachineIntelligenceTechnology,MinistryofEducation,Nanjing,211106,China3Co... |
synthetic_cpt | 3 | DRESS__Instructing_Large_Vision-Language_Models_to_Align_and_Interact_with_Humans_via_Natural_Language_Feedback.pdf | 4
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This paper is accepted and will appear in the IEEE Transactions on Robotics. ©2024 IEEE. Personal use of this material is permitted.
Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/rep... |
synthetic_cpt | 3 | Dehallucinating_Large_Language_Models_Using_Formal_Methods_Guided_Iterative_Prompting.pdf | 4
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Leveraging Hallucinations to Reduce Manual Prompt
Dependency in Promptable Segmentation
Jian Hu1, Jiayi Lin1, Junchi Yan2, Shaogang Gong1
1Queen Mary University of London, 2Shanghai Jiao Tong University
{jian.hu, jiayi.lin, s.gong}@qmul.ac.uk, ya... |
synthetic_cpt | 1 | Large_Language_Models_as_Automated_Aligners_for_benchmarking_Vision-Language_Models.pdf | 3
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Technical Report
LARGE LANGUAGE MODELS AS AUTOMATED ALIGN-
ERS FOR BENCHMARKING VISION-LANGUAGE MODELS
Yuanfeng Ji1∗, Chongjian Ge1∗, Weikai Kong2, Enze Xie2,
Zhengying Liu2, Zhenguo Li2, Ping Luo1†
1The University of Hong Kong,
{u3008013, rhett... |
synthetic_cpt | 1 | MixPro_Simple_yet_Effective_Data_Augmentation_for_Prompt-based_Learning.pdf | Few-shot Adaptation to Distribution Shifts By Mixing
Source and Target Embeddings
Yihao Xue 1 Ali Payani 2 Yu Yang 1 Baharan Mirzasoleiman 1
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Pretrained machine learning models need to be
adapted to distribution shifts when deployed
in n... |
synthetic_cpt | 4 | Synthetic_Proof_Term_Data_Augmentation_for_Theorem_Proving_with_Language_Models.pdf | 4
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ALCHEMY: AMPLIFYING THEOREM-PROVING CAPA-
BILITY THROUGH SYMBOLIC MUTATION
Shaonan Wu 1,2, ∗ Shuai Lu 3,† Yeyun Gong 3, Nan Duan 3, Ping Wei 1,2,†
1 National Key Laboratory of Human-Machine Hybrid Augmented Intelligence
2 Institute of Artificial ... |
synthetic_cpt | 2 | Data-Centric_AI_Tabular_Data_Synthesis_with_Deep_Generative_Models.pdf | Data Gathering from Path Constrained Mobile Sensors Using Data MULE
Dinesh Dash, NIT Patna, India
dd@nitp.ac.in
Abstract—In Wireless Sensor Network (WSN) sensor nodes are deployed to sense useful data from
environment. Sensors are energy-constrained devices. To prolong the sensor network lifetime, now a
days mobi... |
synthetic_cpt | 1 | Transparency_strategy-based_data_augmentation_for_BI-RADS_classification_of_mammograms.pdf | CC-BY 4.0
This is the author’s pre-print version of article “Transparent Serverless execution of
Python multiprocessing applications” published in journal Future Generation
Computer Systems (Volume 140, March 2023, Pages 436-449).
DOI: 10.1016/j.future.2022.10.038
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synthetic_cpt | 1 | Object_Scene_Representation_Transformer.pdf | Neural Scene Graphs for Dynamic Scenes
Julian Ost1
Fahim Mannan1
Nils Th¨urey2
Julian Knodt3
Felix Heide1,3
1Algolux
2Technical University of Munich
3Princeton University
http://light.princeton.edu/neural-scene-graphs
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Recent imp... |
synthetic_cpt | 2 | MedAdapter_Efficient_Test-Time_Adaptation_of_Large_Language_Models_Towards_Medical_Reasoning.pdf | MedAdapter: Efficient Test-Time Adaptation of Large
Language Models Towards Medical Reasoning
Wenqi Shi♠*, Ran Xu♡*, Yuchen Zhuang♠, Yue Yu♠, Haotian Sun♠,
Hang Wu♠, Carl Yang♡, May D. Wang♠
♠ Georgia Tech ♡ Emory University
{wqshi,yczhuang,yueyu,haotian.sun,hangwu,maywang}@gatech.edu
{ran.xu,j.carlyang}@emory.edu
4... |
synthetic_cpt | 1 | PCC_Paraphrasing_with_Bottom-k_Sampling_and_Cyclic_Learning_for_Curriculum_Data_Augmentation.pdf | 8
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A Comparison of CP-OFDM, PCC-OFDM and UFMC for 5G
Uplink Communications
Gayathri Kongara1, Lei Yang2, Cuiwei He1, and Jean Armstrong1
1Department of Electrical and Computer Systems Engineering, Monash University,
Melbourne, Vic., Australia
2F... |
synthetic_cpt | 2 | Breaking_ReLU_Barrier_Generalized_MoEfication_for_Dense_Pretrained_Models.pdf | An Analytical Formula of Population Gradient for two-layered ReLU network
and its Applications in Convergence and Critical Point Analysis
Yuandong Tian 1
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In this paper, we explore theoretical prop-
training a two-layered ReLU net-
erti... |
synthetic_cpt | 2 | OPT_Open_Pre-trained_Transformer_Language_Models.pdf | 3
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Computational Aspects of Optional P´olya Tree
Hui Jiang1,2,*, John C. Mu3, Kun Yang4, Chao Du2, Luo Lu2 and Wing Hung
Wong2,5,*
1Department of Biostatistics, University of Michigan
2Department of Statistics, Stanford University
3Department of... |
synthetic_cpt | 2 | Multicalibration_for_Confidence_Scoring_in_LLMs.pdf | Multicalibration for Confidence Scoring in LLMs
Gianluca Detommaso 1 Martin Bertran * 1 Riccardo Fogliato * 1 Aaron Roth 1 2
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Abstract
This paper proposes the use of “multicalibra-
tion” to yield interpretable and reliable confi-
dence scores ... |
synthetic_cpt | 2 | Filter-then-Generate_Large_Language_Models_with_Structure-Text_Adapter_for_Knowledge_Graph_Completion.pdf | 8
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Kalman Filter, Unscented Filter and Particle Flow
Filter on Non-linear Models
Author: Yan Zhao
Advisor: prof. Zhongqiang Zhang
Contents
1 Kalman Filter
1.0.1 Linear Dynamic Systems in Discrete Time . . . . . . .
1.0.2 Example... |
synthetic_cpt | 2 | Exploiting_Paraphrasers_and_Inverse_Paraphrasers_A_Novel_Approach_to_Enhance_English_Writing_Fluency_through_Improved_Style_Transfer_Training_Data.pdf | Neural Paraphrasing by Automatically Crawled
and Aligned Sentence Pairs
Achille Globo†, Antonio Trevisi†, Andrea Zugarini∗, Leonardo Rigutini†, Marco Maggini‡, Stefano Melacci‡
∗DINFO, University of Florence, Florence, Italy, andrea.zugarini@unifi.it
†QuestIT S.r.l., The Digital Box S.p.a., Siena, Italy, {globo,trevisi... |
synthetic_cpt | 3 | ReFT_Reasoning_with_Reinforced_Fine-Tuning.pdf | 4
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ReFT: Representation Finetuning
for Language Models
Zhengxuan Wu∗† Aryaman Arora∗† Zheng Wang† Atticus Geiger‡
Dan Jurafsky† Christopher D. Manning† Christopher Potts†
†Stanford University
‡Pr(Ai)2R Group
{wuzhengx,aryamana,peterwz,atticusg}@st... |
synthetic_cpt | 1 | TinyVLA_Towards_Fast_Data-Efficient_Vision-Language-Action_Models_for_Robotic_Manipulation.pdf | TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models
for Robotic Manipulation
Junjie Wen1,∗, Yichen Zhu2,∗,†, Jinming Li3, Minjie Zhu1, Kun Wu4, Zhiyuan Xu5,
Ning Liu2, Ran Cheng2, Chaomin Shen1,†, Yaxin Peng3, Feifei Feng2, and Jian Tang5
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synthetic_cpt | 1 | MAPLE_A_Framework_for_Active_Preference_Learning_Guided_by_Large_Language_Models.pdf | 0
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ON THE COMPLETE SOLUTION TO THE
MOST GENERAL FIFTH DEGREE
POLYNOMIAL
Richard J. Drociuk
Physics Department
Simon Fraser University
Burnaby British Columbia, Canada.
April 10, 2000.
Dedicated to Erland Samuel Bring
The first great pione... |
synthetic_cpt | 1 | Synthetic_Data_and_Computer-Vision-Based_Automated_Quality_Inspection_System_for_Reused_Scaffolding.pdf | 9
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Synthetic Data for Deep Learning
Sergey I. Nikolenko1,2
1Synthesis.ai, San Francisco, CA
2Steklov Institute of Mathematics at St. Petersburg, Russia
snikolenko@synthesis.ai
September 26, 2019
Abstract
Synthetic data is an increasingly popul... |
synthetic_cpt | 2 | SpecFuse_Ensembling_Large_Language_Models_via_Next-Segment_Prediction.pdf | 4
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SPECFUSE: ENSEMBLING LARGE LANGUAGE
MODELS VIA NEXT-SEGMENT PREDICTION
Bo Lv1,2,3, Chen Tang4 , Yanan Zhang3, Xin Liu2, Yue Yu2, Ping Luo 1,2,3
1Key Lab of Intelligent Information Processing,
Institute of Computing Technology, Chinese Academy of Sc... |
synthetic_cpt | 8 | Generating_Datasets_with_Pretrained_Language_Models.pdf | Arithmetic-Based Pretraining – Improving Numeracy of Pretrained
Language Models
Dominic Petrak† , Nafise Sadat Moosavi‡, Iryna Gurevych†
†Ubiquitous Knowledge Processing Lab (UKP Lab),
Department of Computer Science and Hessian Center for AI (hessian.AI),
Technical University of Darmstadt, Germany
https://www.ukp.tu-d... |
synthetic_cpt | 8 | Training_and_Evaluating_Language_Models_with_Template-based_Data_Generation.pdf | 4
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Template Matters: Understanding the Role of Instruction Templates
in Multimodal Language Model Evaluation and Training
Shijian Wang* 1
Linxin Song* 2
Jieyu Zhang3 Ryotaro Shimizu4,5 Ao Luo6
Li Yao† 1 Cunjian Chen7
1Southeast University
4 Unive... |
synthetic_cpt | 2 | End-to-End_Speech-Translation_with_Knowledge_Distillation_FBK@IWSLT2020.pdf | 2
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ON THE END DEPTH AND ENDS OF GROUPS
M. GIANNOUDOVARDI
Abstract. We prove that any finitely generated one ended group
has linear end depth. Moreover, we give alternative proofs to theo-
rems relating the growth of a finitely generated group to th... |
synthetic_cpt | 8 | Scalable_Data_Ablation_Approximations_for_Language_Models_through_Modular_Training_and_Merging.pdf | Scalable Data Ablation Approximations for Language Models through
Modular Training and Merging
Clara Na1,2 Ian Magnusson1,3 Ananya Harsh Jha1,3
Tom Sherborne4 Emma Strubell1,2 Jesse Dodge1 Pradeep Dasigi1
1Allen Institute for AI 2Carnegie Mellon University 3University of Washington
4Cohere
csna@cs.cmu.edu
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synthetic_cpt | 2 | GenZI_Zero-Shot_3D_Human-Scene_Interaction_Generation.pdf | GenZI: Zero-Shot 3D Human-Scene Interaction Generation
Lei Li
Angela Dai
Technical University of Munich
craigleili.github.io/projects/genzi
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Figure 1. Given an arbitrary 3D scene, GenZI can synthesize virtual humans interacting with the 3D envi... |
synthetic_cpt | 2 | Large_Language_Models_Engineer_Too_Many_Simple_Features_For_Tabular_Data.pdf | 4
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Preprint
LARGE LANGUAGE MODELS ENGINEER TOO MANY
SIMPLE FEATURES FOR TABULAR DATA
Jaris Küken1, Lennart Purucker1, Frank Hutter2,1
1University of Freiburg, 2ELLIS Institute Tübingen
Correspondence to {kuekenj,purucker}@cs.uni-freiburg.de
ABSTRA... |
synthetic_cpt | 4 | Improving_In-Context_Learning_with_Small_Language_Model_Ensembles.pdf | 3
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A perturbative determination of O(a) boundary improvement coefficients
for the Schr¨odinger Functional coupling at 1-loop with improved gauge
actions ∗
Shinji Takeda, Sinya Aoki and Kiyotomo Ide
Institute of Physics, University of Tsukuba, Tsukuba, Ibara... |
synthetic_cpt | 1 | MILU_A_Multi-task_Indic_Language_Understanding_Benchmark.pdf | 9
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Robust Optimal-Complexity Multilevel ILU for
Predominantly Symmetric Systems
Aditi Ghai∗
Xiangmin Jiao∗†
Abstract
Incomplete factorization is a powerful preconditioner for Krylov subspace methods for solving large-
scale sparse linear sy... |
synthetic_cpt | 7 | ChipExpert_The_Open-Source_Integrated-Circuit-Design-Specific_Large_Language_Model.pdf | ChipExpert: The Open-Source
Integrated-Circuit-Design-Specific Large Language Model
Ning Xu1,2 Zhaoyang Zhang1,2 Lei Qi1,2 Wensuo Wang1 Chao Zhang1 Zihao Ren2
Huaiyuan Zhang2 Xin Cheng2 Yanqi Zhang2 Zhichao Liu2 Qingwen Wei2 Shiyang Wu1,2
Lanlan Yang1 Qianfeng Lu2 Yiqun Ma2 Mengyao Zhao2
Junbo Liu2 Yufan Song1
Xin ... |
synthetic_cpt | 3 | Some_things_are_more_CRINGE_than_others_Preference_Optimization_with_the_Pairwise_Cringe_Loss.pdf | Some things are more CRINGE than others:
Iterative Preference Optimization with the Pairwise Cringe Loss
Jing Xu 1 Andrew Lee 1 Sainbayar Sukhbaatar 1 Jason Weston 1
Abstract
and other variants.
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Practitioners commonly align large language mod-
... |
synthetic_cpt | 8 | Towards_Zero-Label_Language_Learning.pdf | 2
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Modelling structural zeros in compositional data via a zero-censored
multivariate normal model
Michail Tsagris
Department of Economics, University of Crete, Rethymnon, Greece,
mtsagris@uoc.gr
Abstract
Inspired by
We present a new model for... |
synthetic_cpt | 5 | Two_Directions_for_Clinical_Data_Generation_with_Large_Language_Models_Data-to-Label_and_Label-to-Data.pdf | 3
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Preprint
KNOWLEDGE-INFUSED PROMPTING: ASSESSING AND
ADVANCING CLINICAL TEXT DATA GENERATION
WITH LARGE LANGUAGE MODELS
Ran Xu♡, Hejie Cui♡, Yue Yu♠, Xuan Kan♡, Wenqi Shi♠, Yuchen Zhuang♠,
Wei Jin♡, Joyce C. Ho♡, Carl Yang♡
♡ Emory University ♠ Geor... |
synthetic_cpt | 3 | Improving_Factuality_and_Reasoning_in_Language_Models_through_Multiagent_Debate.pdf | 3
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Improving Factuality and Reasoning in Language
Models through Multiagent Debate
Yilun Du
MIT CSAIL
yilundu@mit.edu
Shuang Li
MIT CSAIL
lishuang@mit.edu
Antonio Torralba
MIT CSAIL
torralba@mit.edu
Joshua B. Tenenbaum
MIT CSAIL, BCS, CBMM
jbt@mit... |
synthetic_cpt | 2 | On_the_Impact_of_Calibration_Data_in_Post-training_Quantization_and_Pruning.pdf | On the Impact of Calibration Data in
Post-training Quantization and Pruning
Miles Williams and Nikolaos Aletras
University of Sheffield
United Kingdom
{mwilliams15, n.aletras}@sheffield.ac.uk
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Quantization and pruning form the foundation... |
synthetic_cpt | 1 | PMU_Data_Quality_and_Sensor_Health_Monitoring.pdf | Anomaly Detection Using Optimally-Placed µPMU
Sensors in Distribution Grids
Mahdi Jamei, Anna Scaglione, Ciaran Roberts, Emma Stewart,
Sean Peisert, Chuck McParland, Alex McEachern
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monit... |
synthetic_cpt | 1 | Data_Augmentation_for_Low-Resource_Keyphrase_Generation.pdf | Distributional Data Augmentation Methods for Low Resource Language
Mosleh Mahamud, Zed Lee, Isak Samsten
Department of Computer and Systems Sciences
Borgarfjordsgatan 12, Kista, Sweden
{mosleh.mahamud,zed.lee,samsten}@dsv.su.se
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Text augm... |
synthetic_cpt | 1 | Data-Efficient_Language-Supervised_Zero-Shot_Learning_with_Self-Distillation.pdf | Data Gathering from Path Constrained Mobile Sensors Using Data MULE
Dinesh Dash, NIT Patna, India
dd@nitp.ac.in
Abstract—In Wireless Sensor Network (WSN) sensor nodes are deployed to sense useful data from
environment. Sensors are energy-constrained devices. To prolong the sensor network lifetime, now a
days mobi... |
synthetic_cpt | 3 | Self-Improvement_in_Language_Models_The_Sharpening_Mechanism.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 | Systematic_Assessment_of_Tabular_Data_Synthesis_Algorithms.pdf | 4
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Systematic Assessment of Tabular Data Synthesis Algorithms
Yuntao Du
ytdu@purdue.edu
Purdue University
USA
Ninghui Li
ninghui@purdue.edu
Purdue University
USA
ABSTRACT
Data synthesis has been advocated as an important approach for uti-
lizing d... |
synthetic_cpt | 8 | SELF-GUIDE_Better_Task-Specific_Instruction_Following_via_Self-Synthetic_Finetuning.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 | Domain_Dynamics_Evaluating_Large_Language_Models_in_English-Hindi_Translation.pdf | 1
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DYNAMICALLY DECODING SOURCE DOMAIN KNOWL-
EDGE FOR DOMAIN GENERALIZATION
Cuicui Kang and Karthik Nandakumar
Department of Computer Vision
Mohamed bin Zayed University of Artificial Intelligence
Masdar City, Abu Dhabi, UAE
{Cuicui.Kang, Karthik.Nandak... |
synthetic_cpt | 2 | Influence_Scores_at_Scale_for_Efficient_Language_Data_Sampling.pdf | Influence Scores at Scale for Efficient Language Data Sampling
Nikhil Anand∗ and Joshua Tan∗ and Maria Minakova
Amazon Alexa AI
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Abstract
Modern ML systems ingest data aggregated
from diverse sources, such as synthetic, human-
annotated, and liv... |
synthetic_cpt | 3 | AdaSelection_Accelerating_Deep_Learning_Training_through_Data_Subsampling.pdf | 3
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AdaSelection: Accelerating Deep Learning Training
through Data Subsampling
Minghe Zhang∗
Georgia Institute of Technology
minghe_zhang@gatech.edu
Chaosheng Dong†
Amazon
chaosd@amazon.com
Jinmiao Fu
Amazon
jinmiaof@amazon.com
Tianchen Zhou*
The... |
synthetic_cpt | 9 | ZeroGen_Efficient_Zero-shot_Learning_via_Dataset_Generation.pdf | ZEROGEN: Zero-shot Multimodal Controllable Text Generation with
Multiple Oracles
Haoqin Tu, Bowen Yang, Xianfeng Zhao
State Key Laboratory of Information Security, Institute of Information Engineering,
School of Cyber Security, University of Chinese Academy of Sciences
tuisaac163@gmail.com, {yangbowen,zhaoxianfeng}@ii... |
synthetic_cpt | 1 | A_Data_Augmentation_Method_and_the_Embedding_Mechanism_for_Detection_and_Classification_of_Pulmonary_Nodules_on_Small_Samples.pdf | A Data Augmentation Method and the Embedding
Mechanism for Detection and Classification of
Pulmonary Nodules on Small Samples
Yang Liu,a,b,1 Yue-Jie Hou,a,1 Chen-Xin Qin,a Xin-Hui Li,a Qi-Meng Du,a Si-Jing
Li,a,2 Bin Wang,c,d,e,2 Chi-Chun Zhoua,2
aSchool of Engineering, Dali University, Dali, Yunnan 671003, PR China;
b... |
synthetic_cpt | 2 | Self-Diagnosis_and_Self-Debiasing_A_Proposal_for_Reducing_Corpus-Based_Bias_in_NLP.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 | 4 | AlphaVerus_Bootstrapping_Formally_Verified_Code_Generation_through_Self-Improving_Translation_and_Treefinement.pdf | 4
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AlphaVerus: Bootstrapping Formally Verified Code Generation through
Self-Improving Translation and Treefinement
Pranjal Aggarwal 1 Bryan Parno 1 Sean Welleck 1
Abstract
Automated code generation with large language
models has gained significant t... |
synthetic_cpt | 1 | Lightweight_Privacy-Preserving_GAN_Framework_for_Model_Training_and_Image_Synthesis.pdf | > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) <
1
Towards Lightweight and Privacy-preserving Data
Provision in Digital Forensics for Driverless Taxi
Yanwei Gong, Xiaolin Chang, Jelena Mišić, Vojislav B. Mišić, Junchao Fan, Kaiwen Wang
Abstract—Data provision, referring to the ... |
synthetic_cpt | 8 | What_Makes_Good_Data_for_Alignment_A_Comprehensive_Study_of_Automatic_Data_Selection_in_Instruction_Tuning.pdf | Published as a conference paper at ICLR 2024
WHAT MAKES GOOD DATA FOR ALIGNMENT?
A COMPREHENSIVE STUDY OF AUTOMATIC DATA
SELECTION IN INSTRUCTION TUNING
Wei Liu∗1 Weihao Zeng∗2 Keqing He3 Yong Jiang4
1ShanghaiTech University
4Alibaba Group
3Meituan
liuwei4@shanghaitech.edu.cn
junxianh@cse.ust.hk
2Beijing University ... |
synthetic_cpt | 5 | Harnessing_Diversity_for_Important_Data_Selection_in_Pretraining_Large_Language_Models.pdf | 4
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HARNESSING DIVERSITY FOR IMPORTANT DATA SE-
LECTION IN PRETRAINING LARGE LANGUAGE MOD-
ELS
Chi Zhang *1, Huaping Zhong *2, Kuan Zhang1, Chengliang Chai †1, Rui Wang3, Xinlin Zhuang3,
Tianyi Bai3, Jiantao Qiu3, Lei Cao4, Ju Fan5, Ye Yuan1, Guoren Wa... |
synthetic_cpt | 1 | Unraveling_the_hidden_environmental_impacts_of_AI_solutions_for_environment.pdf | 2
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Unraveling the Hidden Environmental Impacts of AI
Solutions for Environment
Life Cycle Assessment of AI Solutions
Anne-Laure Ligozat
Univ. Paris-Saclay, LIMSI, CNRS, ENSIIE
Orsay, France
Julien Lefèvre
Univ. Aix-Marseille, CNRS, Centrale Marseil... |
synthetic_cpt | 2 | Corpus_Synthesis_for_Zero-Shot_ASR_Domain_Adaptation_Using_Large_Language_Models.pdf | 1
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AISHELL-3: A MULTI-SPEAKER MANDARIN TTS CORPUS AND THE BASELINES
Yao Shi1,2, Hui Bu3, Xin Xu3, Shaoji Zhang3, Ming Li1,2
1 School of Computer Science, Wuhan University, Wuhan, China
2 Data Science Research Center, Duke Kunshan University, Kunsha... |
synthetic_cpt | 3 | Large_Language_Models_Can_Self-Improve_in_Long-context_Reasoning.pdf | Self-Cognition in Large Language Models: An Exploratory Study
Dongping Chen * 1 Jiawen Shi * 1 Yao Wan 1 Pan Zhou 1 Neil Zhenqiang Gong 2 Lichao Sun 3
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Abstract
While Large Language Models (LLMs) have
achieved remarkable success across various ap-
... |
synthetic_cpt | 1 | TeleMelody_Lyric-to-Melody_Generation_with_a_Template-Based_Two-Stage_Method.pdf | TeleMelody: Lyric-to-Melody Generation with a Template-Based
Two-Stage Method
Zeqian Ju1, Peiling Lu2, Xu Tan∗ 2, Rui Wang2, Chen Zhang3,
Songruoyao Wu3, Kejun Zhang3, Xiangyang Li1, Tao Qin2, Tie-Yan Liu2
1 University of Science and Technology of China
2 Microsoft Research Asia, 3 Zhejiang University, China
https://g... |
synthetic_cpt | 1 | CMSSW_Scaling_Limits_on_Many-Core_Machines.pdf | CMSSW Scaling Limits on Many-Core Machines
Christopher Jones1 and Patrick Gartung1
*
1Fermi National Accelerator Laboratory, Batavia, IL, USA
Abstract. Today the LHC offline computing relies heavily on CPU
resources, despite the interest in compute accelerators, such as GPUs, for
the longer... |
synthetic_cpt | 1 | Understanding_hand_gestures_using_approximate_graph_matching.pdf | Talking With Your Hands: Scaling Hand Gestures and Recognition With CNNs
Okan K¨op¨ukl¨u1, Yao Rong1,2, Gerhard Rigoll1
1 Institute for Human-Machine Communication, TU Munich, Germany
2 Infineon Technologies AG, Germany
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The use of hand ... |
synthetic_cpt | 3 | FLAME_Factuality-Aware_Alignment_for_Large_Language_Models.pdf | On the flame transfer function models for laminar premixed conical and V-
flames considering the stretch effect
Yu Tiana, Lijun Yanga,b, Aimee S. Morgansc, Jingxuan Lia,b,∗
aSchool of Astronautics, Beihang University, Beijing 100191, China.
bAircraft and Propulsion Laboratory, Ningbo Institute of Technology, Beihang ... |
synthetic_cpt | 1 | Construction_and_analysis_of_Tibetan_Khampa_dialect_corpus_for_speech_synthesis.pdf | Method of Tibetan Person Knowledge Extraction
Sun Yuan*,1,2, Zhu Zhen1,2
1School of Information Engineering, Minzu University of China, Beijing, 100081, P.R. China
2 Minority Languages Branch, National Language Resource and Monitoring Research Center
Abstract: Person knowledge extraction is the foundation of ... |
synthetic_cpt | 1 | MT-Speech_at_SemEval-2022_Task_10_Incorporating_Data_Augmentation_and_Auxiliary_Task_with_Cross-Lingual_Pretrained_Language_Model_for_Structured_Sentiment_Analysis.pdf | Microtubule length dependence of motor traffic in cells
Yunxin Zhang∗
Laboratory of Mathematics for Nonlinear Science,
Shanghai Key Laboratory for Contemporary Applied Mathematics, Centre for Computational Systems Biology,
School of Mathematical Sciences, Fudan University, Shanghai 200433, China.
(Dated: August 17, 2021... |
synthetic_cpt | 2 | Evolve_Cost-aware_Acquisition_Functions_Using_Large_Language_Models.pdf | 5
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The evolutionary origins of hierarchy
Henok Mengistu, University of Wyoming
Joost Huizinga, University of Wyoming
Jean-Baptiste Mouret, Inria, Villers-l`es-Nancy, F-54600, France
Jeff Clune, University of Wyoming
Abstract
Hierarchical organizatio... |
synthetic_cpt | 1 | Leveraging_Large_Language_Models_for_Tradespace_Exploration.pdf | 1
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A PARALLEL TEMPERING APPROACH FOR EFFICIENT
EXPLORATION OF THE VERIFICATION TRADESPACE IN
ENGINEERED SYSTEMS
A PREPRINT
Peng Xu
Grado Department of Industrial and Systems Engineering
Virginia Tech
Blacskburg, VA 24060
xupeng@vt.edu
Alejandro Sal... |
synthetic_cpt | 1 | Bias_Out-of-the-Box_An_Empirical_Analysis_of_Intersectional_Occupational_Biases_in_Popular_Generative_Language_Models.pdf | Demoting the Lead Bias in News Summarization via Alternating
Adversarial Learning
Linzi Xing∗, Wen Xiao∗, Giuseppe Carenini
Department of Computer Science
University of British Columbia
Vancouver, BC, Canada, V6T 1Z4
{lzxing, xiaowen3, carenini}@cs.ubc.ca
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ai_researcher | 3 | OpenScholar_Synthesizing_Scientific_Literature_with_Retrieval-augmented_LMs.pdf | 4
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Asai et al. (2024)
OPENSCHOLAR: SYNTHESIZING SCIENTIFIC
LITERATURE WITH RETRIEVAL-AUGMENTED LMS
Akari Asai1,5 Jacqueline He1∗ Rulin Shao1,5∗ Weijia Shi1,2
Amanpreet Singh2 Joseph Chee Chang2 Kyle Lo2 Luca Soldaini2
Sergey Feldman2 Mike D’arcy2 Da... |
ai_researcher | 4 | Reflexion_language_agents_with_verbal_reinforcement_learning.pdf | 4
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Sur le nombre de r´eflexions pleines dans les
groupes de Coxeter finis
F. Chapoton
November 19, 2018
Abstract
On consid`ere diff´erents aspects d’une formule dans les groupes de Cox-
eter finis.
0
Introduction
Cet article tourne autour... |
ai_researcher | 2 | GENERATIVE_AI_A_TOOL_FOR_ADDRESSING_DATA_SCARCITY_IN_SCIENTIFIC_RESEARCH.pdf | “Weak AI” is Likely to Never Become “Strong AI”,
1
So What is its Greatest Value for us?
⋆Bin Liu
First posted March 30th, 2021
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AI has surpassed humans across a variety of tasks such as image classification, playing games (e.g., go,... |
ai_researcher | 4 | PaperQA_Retrieval-Augmented_Generative_Agent_for_Scientific_Research.pdf | 3
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PaperQA: Retrieval-Augmented Generative Agent for
Scientific Research
Jakub L´ala
Future House
Francis Crick Institute
Odhran O’Donoghue
Align to Innovate
Francis Crick Institute
University of Oxford
Aleksandar Shtedritski
Align to Innovate
Franc... |
ai_researcher | 4 | Augmenting_Scientific_Creativity_with_an_Analogical_Search_Engine.pdf | 1
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Augmenting Scientific Creativity with an Analogical Search
Engine
HYEONSU B. KANG, Carnegie Mellon University, USA
XIN QIAN, University of Maryland, College Park, USA
TOM HOPE, Allen Institute for AI and The University of Washington, USA
DAFNA... |
ai_researcher | 2 | Creativity_Support_Tool_for_Sustainability_An_AI-first_Approach_to_Accelerating_the_Idea_Selection_Process.pdf | 0
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Supporting Creative Work
with Crowd Feedback Systems
Jonas Oppenlaender
jonas.oppenlaender@oulu.fi
Center for Ubiquitous Computing
University of Oulu
Oulu, Finland
Simo Hosio
simo.hosio@oulu.fi
Center for Ubiquitous Computing
University of Oulu
... |
ai_researcher | 1 | A_Study_on_Statistical_Analysis_for_Performance_Evaluation_of_Digital_Watermarking.pdf | 4
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Adversarial Watermarking for Face Recognition
Yuguang Yao
Anil Jain
Sijia Liu
Michigan State University
Abstract
Watermarking is an essential technique for embedding an identifier (i.e., water-
mark message) within digital images to assert o... |
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