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 | 1 | CodeGRAG_Bridging_the_Gap_between_Natural_Language_and_Programming_Language_via_Graphical_Retrieval_Augmented_Generation.pdf | CodeGRAG: Bridging the Gap between Natural Language and
Programming Language via Graphical Retrieval Augmented Generation
Kounianhua Du1, Jizheng Chen1, Renting Rui1, Huacan Chai1, Lingyue Fu1,
Wei Xia2, Yasheng Wang2, Ruiming Tang2, Yong Yu1, Weinan Zhang1
1Shanghai Jiao Tong University, 2 Huawei Noah’s Ark Lab
Shang... |
synthetic_cpt | 2 | From_Crowdsourced_Data_to_High-Quality_Benchmarks_Arena-Hard_and_BenchBuilder_Pipeline.pdf | 1
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A Survey on Task Assignment in Crowdsourcing
DANULA HETTIACHCHI, The University of Melbourne, Australia
VASSILIS KOSTAKOS, The University of Melbourne, Australia
JORGE GONCALVES, The University of Melbourne, Australia
Quality improvement methods... |
synthetic_cpt | 4 | Beyond_neural_scaling_laws_beating_power_law_scaling_via_data_pruning.pdf | Neural Scaling Laws From Large-N Field Theory:
Solvable Model Beyond the Ridgeless Limit
Department of Physics and Astronomy, University of Utah, Salt Lake City, UT 84112, USA
Zhengkang Zhang
Many machine learning models based on neural networks exhibit scaling laws: their perfor-
mance scales as power laws with r... |
synthetic_cpt | 2 | Training_a_Helpful_and_Harmless_Assistant_with_Reinforcement_Learning_from_Human_Feedback.pdf | Training a Helpful and Harmless Assistant with
Reinforcement Learning from Human Feedback
Yuntao Bai∗, Andy Jones, Kamal Ndousse,
Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort,
Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion,
Tom Conerly, Sheer El-Showk, Nelson El... |
synthetic_cpt | 7 | Source2Synth_Synthetic_Data_Generation_and_Curation_Grounded_in_Real_Data_Sources.pdf | Source2Synth: Synthetic Data Generation and Curation
Grounded in Real Data Sources
Alisia Lupidi1,2, Carlos Gemmell1, Nicola Cancedda 1, Jane Dwivedi-Yu 1,
Jason Weston 1, Jakob Foerster 2, Roberta Raileanu1,3, Maria Lomeli1
1Meta, 2Oxford University, 3University College London
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synthetic_cpt | 1 | Rethinking_the_Evaluation_of_In-Context_Learning_for_LLMs.pdf | 9
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Proceedings of Machine Learning Research 101:1–16, 2019
ACML 2019
Deep Learning with a Rethinking Structure
for Multi-label Classification
Yao-Yuan Yang
Yi-An Lin
Hong-Min Chu
Hsuan-Tien Lin
Department of Computer Science and Information Engineeri... |
synthetic_cpt | 2 | Online_Speculative_Decoding.pdf | Optimizing Speculative Decoding for Serving Large
Language Models Using Goodput
Xiaoxuan Liu1 Cade Daniel2 Langxiang Hu3 Woosuk Kwon1 Zhuohan Li1 Xiangxi Mo1
Alvin Cheung1 Zhijie Deng4 Ion Stoica1 Hao Zhang3
1UC Berkeley 2Anyscale Inc.
3UCSD 4SJTU
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synthetic_cpt | 2 | BiLLM_Pushing_the_Limit_of_Post-Training_Quantization_for_LLMs.pdf | BiLLM: Pushing the Limit of Post-Training Quantization for LLMs
Wei Huang 1 Yangdong Liu 2 Haotong Qin(cid:66) 3 Ying Li 2 Shiming Zhang 1
Xianglong Liu 2 Michele Magno 3 Xiaojuan Qi 1
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Abstract
Pretrained large language models (LLMs) exhibit
exc... |
synthetic_cpt | 2 | Zero-Shot_Learning_Teaching_AI_to_Understand_the_Unknown.pdf | A CHARACTERIZATION OF ZERO DIVISORS AND
TOPOLOGICAL DIVISORS OF ZERO IN C[a, b] AND ℓ∞
HARISH CHANDRA AND ANURAG KUMAR PATEL
Abstract. We give a characterization of zero divisors of the ring
C[a, b]. Using the Weierstrass approximation theorem, we com-
pletely characterize topological divisors of zero of the Banach a... |
synthetic_cpt | 1 | Use_of_a_Structured_Knowledge_Base_Enhances_Metadata_Curation_by_Large_Language_Models.pdf | Structured Knowledge Base Enhances Effective Use of Large Language
Models for Metadata Curation
Sowmya S. Sundaram, Ph.D.1, Benjamin Solomon, M.D., Ph.D.1,2, Avani Khatri M.S.2,
Anisha Laumas A.B.1,2, Purvesh Khatri, Ph.D.1,2 and Mark A. Musen, M.D., Ph.D.1
1Center for Biomedical Informatics Research, School... |
synthetic_cpt | 1 | Featurized_Density_Ratio_Estimation.pdf | Featurized Density Ratio Estimation
Kristy Choi*1
Madeline Liao∗1
Stefano Ermon1
1Computer Science Department, Stanford University
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Abstract
Density ratio estimation serves as an important
technique in the unsupervised machine learning
toolbox... |
synthetic_cpt | 7 | From_Quantity_to_Quality_Boosting_LLM_Performance_with_Self-Guided_Data_Selection_for_Instruction_Tuning.pdf | 4
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Preprint
BALANCING LABEL QUANTITY AND QUALITY FOR
SCALABLE ELICITATION
Alex Mallen & Nora Belrose
EleutherAI
{alex,nora}@eleuther.ai
ABSTRACT
Scalable oversight studies methods of training and evaluating AI systems in do-
mains where human jud... |
synthetic_cpt | 1 | ULTra_Unveiling_Latent_Token_Interpretability_in_Transformer_Based_Understanding.pdf | 4
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ON GRAEV TYPE ULTRA-METRICS
MENACHEM SHLOSSBERG
Abstract. We study Graev ultra-metrics which were introduced by Gao [3]. We show
that the free non-archimedean balanced topological group defined over an ultra-metric
space is metrizable by a Graev... |
synthetic_cpt | 2 | Does_Vision_Accelerate_Hierarchical_Generalization_of_Neural_Language_Learners.pdf | Does Vision Accelerate Hierarchical Generalization in
Neural Language Learners?
Tatsuki Kuribayashi and Timothy Baldwin
MBZUAI
tatsuki.kuribayashi@mbzuai.ac.ae
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Abstract
Neural language models (LMs) are arguably
less data-efficient than humans fro... |
synthetic_cpt | 1 | Self-Supervised_Singing_Voice_Pre-Training_towards_Speech-to-Singing_Conversion.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 | Efficient_Domain_Adaptation_of_Language_Models_via_Adaptive_Tokenization.pdf | Efficient Domain Adaptation of Language Models via Adaptive
Tokenization
Vin Sachidananda∗
Stanford University
vsachi@stanford.edu
Jason S. Kessler
Amazon
jasokess@amazon.com
Yi-An Lai
AWS AI HLT
yianl@amazon.com
Abstract
Contextual embedding-based language mod-
els trained on large data sets, such as BERT
and RoBE... |
synthetic_cpt | 2 | Outlier_Weighed_Layerwise_Sparsity_(OWL)_A_Missing_Secret_Sauce_for_Pruning_LLMs_to_High_Sparsity.pdf | OWLed: Outlier-weighed Layerwise Pruning for
Efficient Autonomous Driving Framework
Jiaxi Li
Computer Science Research Centre
University of Surrey
Guildford, United Kingdom
Lu Yin
Computer Science Research Centre
University of Surrey
Guildford, United Kingdom
Xilu Wang
Computer Science Research Centre
University of ... |
synthetic_cpt | 1 | -generAItor_Tree-in-the-loop_Text_Generation_for_Language_Model_Explainability_and_Adaptation.pdf | -generAItor: Tree-in-the-Loop Text Generation
for Language Model Explainability and Adaptation
THILO SPINNER, ETH Zurich, Switzerland
REBECCA KEHLBECK, University of Konstanz, Germany
RITA SEVASTJANOVA, ETH Zurich, Switzerland
TOBIAS STÄHLE, University of Konstanz, Germany
DANIEL A. KEIM, University of Konstanz, Germa... |
synthetic_cpt | 4 | Self-Refine_Iterative_Refinement_with_Self-Feedback.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 | 5 | Self-Training_with_Direct_Preference_Optimization_Improves_Chain-of-Thought_Reasoning.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 | Optimizing_Alignment_with_Less_Leveraging_Data_Augmentation_for_Personalized_Evaluation.pdf | KaLM: Knowledge-aligned Autoregressive Language Modeling via
Dual-view Knowledge Graph Contrastive Learning
Peng Yu 1, Cheng Deng1, Beiya Dai1, Xinbing Wang1, Ying Wen1*
1Shanghai Jiao Tong University
{pursuit_yp, davendw, beiya_dai, xwang8, ying.wen}@sjtu.edu.cn
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synthetic_cpt | 1 | End-to-End_Full-Page_Optical_Music_Recognition_for_Pianoform_Sheet_Music.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 | 2 | Teaching_Smaller_Language_Models_To_Generalise_To_Unseen_Compositional_Questions_(Full_Thesis).pdf | 4
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Teaching Smaller Language Models To
Generalise To Unseen Compositional
Questions
Timothy John Hartill
A thesis submitted in fulfilment of the requirements for the degree of
Doctor of Philosophy in Computer Science,
The University of Auckland, 202... |
synthetic_cpt | 4 | Iterative_Data_Generation_with_Large_Language_Models_for_Aspect-based_Sentiment_Analysis.pdf | Iterative Data Generation with Large Language
Models for Aspect-based Sentiment Analysis
Qihuang Zhong, Member, IEEE, Haiyun Li, Luyao Zhuang, Juhua Liu, Member, IEEE,
Bo Du, Senior Member, IEEE
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Abstract—Aspect-based Sentiment Analysis (ABSA) ... |
synthetic_cpt | 1 | Predicting_band_gaps_of_MOFs_on_small_data_by_deep_transfer_learning_with_data_augmentation_strategies.pdf | 3
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CarbNN: A Novel Active Transfer Learning Neural Network To
Build De Novo Metal Organic Frameworks (MOFs) for Carbon
Capture
MATS055
Neel Redkar∗1
1Independent Researcher — San Ramon CA, US
2nd May, 2022
Abstract
Over the past decade, climate ch... |
synthetic_cpt | 1 | Grounding_Language_Models_to_Images_for_Multimodal_Inputs_and_Outputs.pdf | Grounding Language Models to Images for Multimodal Inputs and Outputs
Jing Yu Koh 1 Ruslan Salakhutdinov 1 Daniel Fried 1
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Abstract
We propose an efficient method to ground pre-
trained text-only language models to the visual
domain, enabling the... |
synthetic_cpt | 7 | Tuning_Language_Models_as_Training_Data_Generators_for_Augmentation-Enhanced_Few-Shot_Learning.pdf | DAGAM: Data Augmentation with Generation And Modification
Byeong-Cheol Jo1, Tak-Sung Heo1, Yeongjoon Park1
Yongmin Yoo1, Won Ik Cho2, Kyungsun Kim1
AI R&D Group, NHN Diquest1
Department of Electrical and Computer Engineering and INMC, Seoul National University2
{
byeongcheol7674, gjxkrtjd221, yeongjoon1227, yooyo... |
synthetic_cpt | 4 | Synthetic_Data_Augmentation_for_Zero-Shot_Cross-Lingual_Question_Answering.pdf | Exploring Augmentation and Cognitive Strategies for AI based Synthetic Personae
Rafael Arias Gonzalez∗, Simon Fraser University, Canada
Steve DiPaola, Simon Fraser University, Canada
Abstract:
Large language models (LLMs) hold potential for innovative HCI research, including the creation
of synthet... |
synthetic_cpt | 1 | Bridging_the_Synthetic-to-Authentic_Gap_Distortion-Guided_Unsupervised_Domain_Adaptation_for_Blind_Image_Quality_Assessment.pdf | Instance Segmentation of Reinforced Concrete Bridges with
Synthetic Point Clouds
Asad Ur Rahmana, Vedhus Hoskerea*
a Department of Civil and Environmental Engineering, University of Houston, 4226 MLK Blvd, Houston, TX 77204, United States
* Corresponding author at: Department of Civil and Environmental Engine... |
synthetic_cpt | 7 | Adapting_Large_Language_Models_to_Log_Analysis_with_Interpretable_Domain_Knowledge.pdf | Adapting Large Language Models to Log Analysis
with Interpretable Domain Knowledge
Yuhe Ji∗†, Yilun Liu∗†(cid:0), Feiyu Yao†, Minggui He†, Shimin Tao†, Xiaofeng Zhao†,
Su Chang†, Xinhua Yang†, Weibin Meng†, Yuming Xie†, Boxing Chen‡, Hao Yang†
†Huawei, China
‡Huawei Canada, Canada
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synthetic_cpt | 3 | MAmmoTH-VL_Eliciting_Multimodal_Reasoning_with_Instruction_Tuning_at_Scale.pdf | Excess of genomic defects in a woolly mammoth on
Wrangel island
Rebekah L. Rogers1 and Montgomery Slatkin1
Research Article
1) Dept of Integrative Biology, University of California, Berkeley
Running head: Mutational meltdown in woolly mammoths
Key words:Mammoths, elephantids, ancient DNA, deletions, retrogenes, ne... |
synthetic_cpt | 2 | GAugLLM_Improving_Graph_Contrastive_Learning_for_Text-Attributed_Graphs_with_Large_Language_Models.pdf | 4
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GAugLLM: Improving Graph Contrastive Learning for
Text-Attributed Graphs with Large Language Models
Yi Fang
SFSC of AI and DL
New York University(Shanghai)
Shanghai, China
yf2722@nyu.edu
Daochen Zha
Department of Computer Science
Rice Universit... |
synthetic_cpt | 3 | Scalable_Influence_and_Fact_Tracing_for_Large_Language_Model_Pretraining.pdf | Preprint
SCALABLE INFLUENCE AND FACT TRACING FOR
LARGE LANGUAGE MODEL PRETRAINING
Tyler A. Chang,1,2∗ Dheeraj Rajagopal,1 Tolga Bolukbasi,1 Lucas Dixon,1
{tylerchang, rajagopald, tolgab, ldixon, iftenney}@google.com
1Google DeepMind
2UC San Diego
Ian Tenney1
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synthetic_cpt | 1 | Progress_in_Discovery_Science__final_report_of_the_Japanese_dicsovery_science_project.pdf | Big and Small
R D Ekers1
CSIRO-ATNF
Sydney, NSW, Australia
E-mail: ron.ekers@csiro.au
Abstract
Technology leads discovery in astronomy, as in all other areas of science, so growth in
technology leads to the continual stream of new discoveries which makes our field so
fascinating. Derek de Solla Price had an... |
synthetic_cpt | 2 | Aggregate-and-Adapt_Natural_Language_Prompts_for_Downstream_Generalization_of_CLIP.pdf | Chasing Similarity: Distribution aware
Aggregation Scheduling (Extended Version) ∗
Feilong Liu1, Ario Salmasi1, Spyros Blanas1, Anastasios Sidiropoulos2
1The Ohio State University, 2University of Illinois at Chicago
{liu.3222,salmasi.1,blanas.2}@osu.edu, sidiropo@gmail.com
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synthetic_cpt | 1 | Evaluation_Metrics_for_NLG_and_TTS_in_Task-Oriented_Dialog_PhD_Thesis_Proposal.pdf | 1
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Evaluation of Text Generation: A Survey
Evaluation of Text Generation: A Survey
Asli Celikyilmaz
Facebook AI Research
Elizabeth Clark
University of Washington
Jianfeng Gao
Microsoft Research
aslic@fb.com
eaclark7@cs.washington.edu
jfgao@micr... |
synthetic_cpt | 1 | Semantic_Image_Synthesis_from_Text_Current_Trends_and_Future_Horizons_in_Text-to-Image_Generation.pdf | Semantic-aware Data Augmentation for Text-to-image Synthesis
Zhaorui Tan1,2, Xi Yang1∗, Kaizhu Huang3*
1Department of Intelligent Science, Xi’an Jiaotong-Liverpool University
2Department of Computer Science, University of Liverpool
3 Data Science Research Center, Duke Kunshan University
Zhaorui.Tan21@student.xjtlu.edu... |
synthetic_cpt | 3 | Encouraging_Divergent_Thinking_in_Large_Language_Models_through_Multi-Agent_Debate.pdf | Encouraging Divergent Thinking in Large Language Models
through Multi-Agent Debate
Tian Liang1,3* Zhiwei He2* Wenxiang Jiao3* Xing Wang3† Yan Wang3
Rui Wang2 Yujiu Yang1†
Shuming Shi3 Zhaopeng Tu3
1Tsinghua University 2Shanghai Jiao Tong University 3Tencent AI Lab
1{liangt21@mails,yang.yujiu@sz}.tsinghua.edu.cn
2z... |
synthetic_cpt | 1 | Boosting_Unsupervised_Contrastive_Learning_Using_Diffusion-Based_Data_Augmentation_From_Scratch.pdf | ICE: Inter-instance Contrastive Encoding for Unsupervised Person
Re-identification
Hao Chen1,2,3 Benoit Lagadec3
2Universit´e Cˆote d’Azur
Francois Bremond1,2
3European Systems Integration
1Inria
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benoit.la... |
synthetic_cpt | 1 | Beyond_Synthetic_Benchmarks_Assessing_Recent_LLMs_for_Code_Generation.pdf | 1
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Synthetic weather radar using hybrid
quantum-classical machine learning
Graham R. Enos
Rigetti Computing
genos@rigetti.com
Matthew J. Reagor
Rigetti Computing
matt@rigetti.com
Maxwell P Henderson
Rigetti Computing
Christina Young
Rigetti ... |
synthetic_cpt | 2 | Compresso_Structured_Pruning_with_Collaborative_Prompting_Learns_Compact_Large_Language_Models.pdf | 3
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Preprint
COMPRESSO: STRUCTURED PRUNING WITH COLLABO-
RATIVE PROMPTING LEARNS COMPACT LARGE LAN-
GUAGE MODELS
Song Guo∗
Jiahang Xu∗ Li Lyna Zhang‡ Mao Yang
Microsoft Research
ABSTRACT
Despite the remarkable success of Large Language Models (LL... |
synthetic_cpt | 2 | CEM_A_Data-Efficient_Method_for_Large_Language_Models_to_Continue_Evolving_From_Mistakes.pdf | JOURNAL OF ?, VOL. ?, NO. ?, ? ?
1
MF is always superior to CEM
Xiurui Geng, Luyan Ji, Weitun Yang, Fuxiang Wang, Yongchao Zhao
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Abstract—The constrained energy minimization (CEM) and
matched filter (MF) are two most frequently used target det... |
synthetic_cpt | 1 | Synthesis_of_Natural-Inspired_Materials_by_Irradiation_Data_Mining_from_the_Perspective_of_Their_Functional_Properties_in_Wastewater_Treatment.pdf | Modular System Synthesis
Kanghee Park
Keith J.C. Johnson
Loris D’Antoni
Thomas Reps
University of Wisconsin–Madison
Madison, USA
{khpark, keithj, loris, reps}@cs.wisc.edu
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of progr... |
synthetic_cpt | 3 | Scaling_Laws_and_Interpretability_of_Learning_from_Repeated_Data.pdf | 2
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Scaling Laws and Interpretability of Learning from
Repeated Data
Danny Hernandez∗
Tom Brown, Tom Conerly, Nova DasSarma, Dawn Drain, Sheer El-Showk, Nelson Elhage,
Zac Hatfield-Dodds, Tom Henighan, Tristan Hume, Scott Johnston,
Ben Mann, Chris ... |
synthetic_cpt | 8 | Rethinking_Data_Synthesis_A_Teacher_Model_Training_Recipe_with_Interpretation.pdf | Rethinking Blur Synthesis for Deep Real-World Image Deblurring
Hao Wei, Chenyang Ge, Xin Qiao, Pengchao Deng
Xi’an Jiaotong University
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In this paper, we examine the problem of real-world image
deblurring and take into account two key ... |
synthetic_cpt | 2 | How_Large_Language_Models_Will_Disrupt_Data_Management.pdf | 2
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Relational Dynamic Bayesian Network Modeling for
Uncertainty Quantification and Propagation in Airline
Disruption Management(cid:63)
Kolawole Ogunsina1,1,∗, Marios Papamichalis1,2, Daniel DeLaurentis1,3
Abstract
Disruption management during the... |
synthetic_cpt | 4 | Training_LLMs_for_Generating_IEC_61131-3_Structured_Text_with_Online_Feedback.pdf | Exploring LLM Support for Generating IEC
61131-3 Graphic Language Programs
Yimin Zhang
CISTER / Faculty of Engineering
University of Porto
Porto, Portugal
0009-0005-0746-315X
Mario de Sousa
Faculty of Engineering
University of Porto
Porto, Portugal
0000-0001-7200-1705
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synthetic_cpt | 5 | Large_Small_or_Both_A_Novel_Data_Augmentation_Framework_Based_on_Language_Models_for_Debiasing_Opinion_Summarization.pdf | 8
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The spectrum for large sets of (3, λ)-GDDs of type gu
X. Niu 1, H. Cao 1 ∗, and R. Javadi 2, 3 †
1 Institute of Mathematics, Nanjing Normal University, Nanjing 210023, China
2 Department of Mathematical Sciences, Isfahan University of Techno... |
synthetic_cpt | 1 | Glottal_Stops_in_Upper_Sorbian_A_Data-Driven_Approach.pdf | CUNI Systems for the Unsupervised and Very Low Resource
Translation Task in WMT20
Ivana Kvapil´ıkov´a
Tom Kocmi
Ondˇrej Bojar
Charles University, Faculty of Mathematics and Physics
Institute of Formal and Applied Linguistics
Malostransk´e n´amˇest´ı 25, 118 00 Prague, Czech Republic
<surname>@ufal.mff.cuni.cz
Abst... |
synthetic_cpt | 8 | Instruction_Tuning_with_Human_Curriculum.pdf | Distilling Instruction-following Abilities of Large Language Models
with Task-aware Curriculum Planning
Yuanhao Yue1,2∗, Chengyu Wang2†, Jun Huang2, Peng Wang1†
1 School of Computer Science, Fudan University, Shanghai, China
2 Alibaba Cloud Computing, Hangzhou, China
yhyue22@m.fudan.edu.cn
{chengyu.wcy,huangjun.hj}@al... |
synthetic_cpt | 2 | Language-Inspired_Relation_Transfer_for_Few-Shot_Class-Incremental_Learning.pdf | 6
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Comparing Fifty Natural Languages and Twelve Genetic Languages Using
Word Embedding Language Divergence (WELD) as a Quantitative Measure
of Language Distance
Ehsaneddin Asgari and Mohammad R.K. Mofrad
Departments of Bioengineering
University of Ca... |
synthetic_cpt | 2 | Scalable_Efficient_Training_of_Large_Language_Models_with_Low-dimensional_Projected_Attention.pdf | DistTrain: Addressing Model and Data Heterogeneity with Disaggregated Training
for Multimodal Large Language Models
Zili Zhang∗
Yinmin Zhong∗
Ranchen Ming†
Hanpeng Hu†
Jianjian Sun†
Zheng Ge†
Yibo Zhu†
Xin Jin∗
∗Peking University
†StepFun
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synthetic_cpt | 2 | Translating_Words_to_Worlds_Zero-Shot_Synthesis_of_3D_Terrain_from_Textual_Descriptions_Using_Large_Language_Models.pdf | Optimizing Rare Word Accuracy in Direct Speech Translation with a
Retrieval-and-Demonstration Approach
Siqi Li*1
Danni Liu*2
Jan Niehues2
1University of California, Irvine, USA
2Karlsruhe Institute of Technology, Germany
siqil31@uci.edu, {danni.liu, jan.niehues}@kit.edu
Abstract
Direct speech translation (ST) mod... |
synthetic_cpt | 2 | Curiosity-driven_Red-teaming_for_Large_Language_Models.pdf | Computational Curiosity
(A Book Draft)
by
Qiong Wu
wuqi0005@e.ntu.edu.sg
Nanyang Technological University
Contents
Preface
Chapter 1 Psychology Underpinnings of Curiosity
1.1. Categories of Curiosity
1.2. Curiosity-Related Emotions
1.3. Curiosity-Related Behaviors
1.4. Be... |
synthetic_cpt | 2 | Learning_to_Reason_via_Self-Iterative_Process_Feedback_for_Small_Language_Models.pdf | Towards Zero-shot Commonsense Reasoning
with Self-supervised Refinement of Language Models
Tassilo Klein
SAP AI Research
tassilo.klein@sap.com
Moin Nabi
SAP AI Research
m.nabi@sap.com
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Abstract
Can we get existing language models and re-
fine them... |
synthetic_cpt | 2 | TempLM_Distilling_Language_Models_into_Template-Based_Generators.pdf | TempLM: Distilling Language Models into Template-Based Generators
Tianyi Zhang, Mina Lee∗, Lisa Li∗, Ende Shen∗, Tatsunori B. Hashimoto
Computer Science Department, Stanford University
{tz58, minalee, xlisali, endeshen, thashim}@stanford.edu
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synthetic_cpt | 4 | Enhancing_Task-Specific_Distillation_in_Small_Data_Regimes_through_Language_Generation.pdf | 4
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Optimizing Dense Visual Predictions Through Multi-Task Coherence and
Prioritization
Maxime Fontana1, Michael Spratling2, and Miaojing Shi3*
1Department of Informatics, King’s College London
2Department of Behavioural and Cognitive Sciences, Universi... |
synthetic_cpt | 3 | Sheared_LLaMA_Accelerating_Language_Model_Pre-training_via_Structured_Pruning.pdf | 4
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Published as a conference paper at ICLR 2024
SHEARED LLAMA: ACCELERATING LANGUAGE
MODEL PRE-TRAINING VIA STRUCTURED PRUNING
Mengzhou Xia1, Tianyu Gao1, Zhiyuan Zeng2 , Danqi Chen1
1Princeton Language and Intelligence, Princeton University
2Depart... |
synthetic_cpt | 2 | Template-Based_Question_Generation_from_Retrieved_Sentences_for_Improved_Unsupervised_Question_Answering.pdf | 2
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SemiRetro: Semi-template framework boosts deep retrosynthesis prediction
Zhangyang Gao * 1 2 Cheng Tan * 1 2 Lirong Wu 1 2 Stan Z. Li 1
Abstract
Recently,
template-based (TB) and template-
free (TF) molecule graph learning methods have
shown pr... |
synthetic_cpt | 3 | ZeroPrompt_Scaling_Prompt-Based_Pretraining_to_1_000_Tasks_Improves_Zero-Shot_Generalization.pdf | ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs
Xingchen Song1,2,3, Di Wu2,3,
Binbin Zhang2,3, Zhendong Peng2,3, Bo Dang3, Fuping Pan3, Zhiyong Wu1
1Tsinghua Univ., Beijing, China 2Horizon Inc., Beijing, China 3WeNet Open Source Community
xingchen.song@horizon.ai
Abstract
In this paper, we present Zer... |
synthetic_cpt | 2 | Pruning_Foundation_Models_for_High_Accuracy_without_Retraining.pdf | 4
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ACCURATE RETRAINING-FREE PRUNING FOR PRE-
TRAINED ENCODER-BASED LANGUAGE MODELS
Seungcheol Park1, Hojun Choi2∗& U Kang1†
1Seoul National University, Seoul, South Korea
2Kim Jaechul Graduate School of AI, KAIST, Seoul, South Korea
{ant6si, ukang}@... |
synthetic_cpt | 2 | Voyager_An_Open-Ended_Embodied_Agent_with_Large_Language_Models.pdf | 33ND INTERNATIONAL COSMIC RAY CONFERENCE, RIO DE JANEIRO 2013
THE ASTROPARTICLE PHYSICS CONFERENCE
Time-dependent cosmic ray modulation in the outer heliosphere: Signatures
of a heliospheric asymmetry and model predictions along Voyager 1 and 2
trajectories
R. MANUEL1, S.E.S. FERREIRA1, M.S. POTGIETER1
1 Centre for Sp... |
synthetic_cpt | 1 | Incorporating_Semi-Supervised_and_Positive-Unlabeled_Learning_for_Boosting_Full_Reference_Image_Quality_Assessment_Supplemental_Materials.pdf | ProbStat Models 6, January-2007, p.1-5.
An Autoregressive Model with Semi-stable Marginals
S Satheesh
NEELOLPALAM, S. N. Park Road
Trichur – 680 004, India.
ssatheesh1963@yahoo.co.in
E Sandhya
Department of Statistics, Prajyoti Niketan College
Pudukkad, Trichur – 680 301, India.
esandhya@hotmail.com
Abs... |
synthetic_cpt | 6 | Let's_Synthesize_Step_by_Step_Iterative_Dataset_Synthesis_with_Large_Language_Models_by_Extrapolating_Errors_from_Small_Models.pdf | Let’s Synthesize Step by Step: Iterative Dataset Synthesis with Large
Language Models by Extrapolating Errors from Small Models
Ruida Wang∗ H
H HKUST
Wangchunshu Zhou A
A AIWaves Inc.
Mrinmaya Sachan E
E ETH Zürich
rwangbr@connect.ust.hk chunshu@aiwaves.cn msachan@ethz.ch
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synthetic_cpt | 2 | SwitchCIT_Switching_for_Continual_Instruction_Tuning_of_Large_Language_Models.pdf | 4
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SwitchCIT: Switching for Continual Instruction Tuning of
Large Language Models
Xinbo Wu1,2, Max Hartman2, Vidhata Arjun Jayaraman2,3, Lav R. Varshney 1,2
1Coordinated Science Laboratory
2Department of Electrical and Computer Engineering
3Departme... |
synthetic_cpt | 2 | Generation_of_TextualVideo_Descriptions_for_Technological_Products_Based_on_Structured_Data.pdf | Ontology-Based Skill Description Learning for
Flexible Production Systems
Anna Himmelhuber, Stephan Grimm, Thomas Runkler, Sonja Zillner
Siemens AG
Munich, Germany
{anna.himmelhuber, stephan.grimm, thomas.runkler, sonja.zillner}@siemens.com
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synthetic_cpt | 4 | CRITIC_Large_Language_Models_Can_Self-Correct_with_Tool-Interactive_Critiquing.pdf | Multi-critical dynamics of the Boson system in the vicinity of the second-order
quantum phase transition
Mikhail Vasin1, 2
1Physical-Technical Institute, Ural Branch of Russian Academy of Sciences, 426000 Izhevsk, Russia
2High Pressure Physics Institute, Russian Academy of Sciences, Moscow, Russia
The non-equilibrium... |
synthetic_cpt | 5 | Learning_from"Silly"Questions_Improves_Large_Language_Models_But_Only_Slightly.pdf | 2
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CEDILLE:
A LARGE AUTOREGRESSIVE LANGUAGE MODEL IN FRENCH
Martin Müller∗
Florian Laurent∗
Cedille AI1
hello@cedille.ai
ABSTRACT
Scaling up the size and training of autoregressive language models has enabled novel ways of solving
Natural Language ... |
synthetic_cpt | 4 | GDPO_Learning_to_Directly_Align_Language_Models_with_Diversity_Using_GFlowNets.pdf | 4
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Graph Diffusion Policy Optimization
Yijing Liu∗1, Chao Du∗†2, Tianyu Pang2, Chongxuan Li3, Min Lin2, Wei Chen†1
1State Key Lab of CAD&CG, Zhejiang University
2Sea AI Lab, Singapore
3Renmin University of China
{liuyj86,chenvis}@zju.edu.cn;
{duchao... |
synthetic_cpt | 1 | Inferring_Offensiveness_In_Images_From_Natural_Language_Supervision.pdf | 1
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Preprint. Work in progress.
INFERRING OFFENSIVENESS IN IMAGES FROM
NATURAL LANGUAGE SUPERVISION
Patrick Schramowski1 & Kristian Kersting1,2
1Computer Science Department, TU Darmstadt, Germany
2Centre for Cognitive Science, TU Darmstadt, and Hessia... |
synthetic_cpt | 8 | CorrSynth_-_A_Correlated_Sampling_Method_for_Diverse_Dataset_Generation_from_LLMs.pdf | CorrSynth - A Correlated Sampling Method for Diverse Dataset
Generation from LLMs
Suhas S Kowshik*, Abhishek Divekar*, Vijit Malik
Amazon
{kowssuhp, adivekar, vijitvm}@amazon.com
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Large language models (LLMs) have demon-
strated remarkab... |
synthetic_cpt | 2 | Improving_Diversity_of_Demographic_Representation_in_Large_Language_Models_via_Collective-Critiques_and_Self-Voting.pdf | Improving Diversity of Demographic Representation in Large Language
Models via Collective-Critiques and Self-Voting
Preethi Lahoti†∗ Nicholas Blumm† Xiao Ma† Raghavendra Kotikalapudi‡
Sahitya Potluri‡ Qijun Tan‡ Hansa Srinivasan† Ben Packer†
Ahmad Beirami† Alex Beutel♢ Jilin Chen†
†Google Research ‡Google DeepMind ♢Op... |
synthetic_cpt | 3 | Measuring_the_Knowledge_Acquisition-Utilization_Gap_in_Pretrained_Language_Models.pdf | Measuring the Knowledge Acquisition-Utilization Gap in Pretrained
Language Models
Amirhossein Kazemnejad1,2 Mehdi Rezagholizadeh3
Prasanna Parthasarathi3† Sarath Chandar2,4,5†
1McGill University; 2Mila - Quebec AI; 3Huawei Noah’s Ark Lab;
4École Polytechnique de Montréal; 5Canada CIFAR AI Chair;
amirhossein.kazemneja... |
synthetic_cpt | 2 | Zero-_and_few-shot_prompting_of_generative_large_language_models_provides_weak_assessment_of_risk_of_bias_in_clinical_trials.pdf | A CHARACTERIZATION OF ZERO DIVISORS AND
TOPOLOGICAL DIVISORS OF ZERO IN C[a, b] AND ℓ∞
HARISH CHANDRA AND ANURAG KUMAR PATEL
Abstract. We give a characterization of zero divisors of the ring
C[a, b]. Using the Weierstrass approximation theorem, we com-
pletely characterize topological divisors of zero of the Banach a... |
synthetic_cpt | 1 | Image_Quality_Assessment_using_Synthetic_Images.pdf | 2
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A Survey on Image Quality Assessment
Lanjiang Wang
University of Electronic Science and Technology of China
Abstract
Image quality assessment(IQA) is of increasing importance for image-based appli-
cations. Its purpose is to establish a m... |
synthetic_cpt | 1 | Balancing_Speed_and_Stability_The_Trade-offs_of_FP8_vs_BF16_Training_in_LLMs.pdf | Dynamic Modeling and Stability Analysis of Balancing in Riderless
Electric Scooters
Yun-Hao Lin, Alireza Jafari, and Yen-Chen Liu
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bility vehicle. The rising demand and opportunities... |
synthetic_cpt | 1 | Leveraging_the_Power_of_Data_Augmentation_for_Transformer-based_Tracking.pdf | Leveraging the Power of Data Augmentation for Transformer-based Tracking
Jie Zhao1, Johan Edstedt2, Michael Felsberg2, Dong Wang1, Huchuan Lu1
1Dalian University of Technology, 2Link¨oping University
zj982853200@mail.dlut.edu.cn, {johan.edstedt,michael.felsberg}@liu.se,{wdice,lhchuan}@dlut.edu.cn
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synthetic_cpt | 2 | Language_Models_Enable_Simple_Systems_for_Generating_Structured_Views_of_Heterogeneous_Data_Lakes.pdf | LANGUAGE MODELS ENABLE SIMPLE SYSTEMS FOR
GENERATING STRUCTURED VIEWS OF HETEROGENEOUS DATA
LAKES
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2Cornell University
April 21, 2023
ABSTRACT
... |
synthetic_cpt | 2 | FSL-QuickBoost_Minimal-Cost_Ensemble_for_Few-Shot_Learning.pdf | 2
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Cross-Domain Cross-Set Few-Shot Learning via
Learning Compact and Aligned Representations
Wentao Chen1,2, Zhang Zhang2,3, Wei Wang2,3, Liang Wang2,3, Zilei Wang1,
and Tieniu Tan1,2,3
1 University of Science and Technology of China, Hefei, China... |
synthetic_cpt | 1 | Improving_Low-Resource_Question_Answering_with_Cross-Lingual_Data_Augmentation_Strategies.pdf | 9
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Spin dependent structure function g1 at
low x and low Q2
B. Bade lek a,b J. Kiryluk b and J. Kwieci´nski c
a Department of Physics, Uppsala University, P.O.Box 530, 751 21 Uppsala, Sweden
b Institute of Experimental Physics, Warsaw University, Ho˙za 69... |
synthetic_cpt | 1 | Visualization_question_answering_using_introspective_program_synthesis.pdf | 4
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: Visual Unit Tests for More Robust Visual Programming
Artemis Panagopoulou†,*
Honglu Zhou‡
Silvio Savarese‡
Caiming Xiong‡
Chris Callison-Burch†
Mark Yatskar†
Juan Carlos Niebles‡
‡Salesforce AI Research
†University of Pennsylvania
https... |
synthetic_cpt | 1 | Enhancing_Voice_Cloning_Quality_through_Data_Selection_and_Alignment-Based_Metrics.pdf | PERSONALIZED LIGHTWEIGHT TEXT-TO-SPEECH: VOICE CLONING WITH ADAPTIVE
STRUCTURED PRUNING
Sung-Feng Huang1, Chia-ping Chen2, Zhi-Sheng Chen2, Yu-Pao Tsai2, Hung-yi Lee1
1National Taiwan University, 2Intelligo Technology Inc.
f06942045@ntu.edu.tw, ailsa.chen@intelli-go.com, cs.chen@intelli-go.com,
yptsai@gmail.com, hung... |
synthetic_cpt | 2 | SA-Attack_Improving_Adversarial_Transferability_of_Vision-Language_Pre-training_Models_via_Self-Augmentation.pdf | Investigating Explanations in Conditional and Highly Automated
Driving: The Effects of Situation Awareness and Modality
Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn
Lilit Avetisyan
Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn
Jackie Ayoub
In... |
synthetic_cpt | 1 | One2Set_Generating_Diverse_Keyphrases_as_a_Set.pdf | WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation
Binbin Xie1,3, Xiangpeng Wei2, Baosong Yang2, Huan Lin2, Jun Xie2,
Xiaoli Wang3, Min Zhang4 and Jinsong Su1,3∗
1School of Informatics, Xiamen University, China 2Alibaba Group, China
3Key Laboratory of Digital Protection and Intelligent Processing of Intangible
Cu... |
synthetic_cpt | 2 | Is_Your_Code_Generated_by_ChatGPT_Really_Correct_Rigorous_Evaluation_of_Large_Language_Models_for_Code_Generation.pdf | 3
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Is Your Code Generated by ChatGPT Really Correct?
Rigorous Evaluation of Large Language Models
for Code Generation
Jiawei Liu ∗
Chunqiu Steven Xia ∗ Yuyao Wang
Lingming Zhang
University of Illinois Urbana-Champaign
Nanjing University
{jiawei6... |
synthetic_cpt | 1 | Leveraging_Speech_PTM_Text_LLM_And_Emotional_TTS_For_Speech_Emotion_Recognition.pdf | A Comparative Study of Pre-trained Speech and
Audio Embeddings for Speech Emotion Recognition
Orchid Chetia Phukan
Dept. of CSE
IIIT Delhi, India
orchidp@iiitd.ac.in
Arun Balaji Buduru
Dept. of CSE
IIIT Delhi, India
arunb@iiitd.ac.in
Rajesh Sharma
Institute of Computer Science
University of Tartu, Estonia
rajesh.sha... |
synthetic_cpt | 1 | An_Annotation_Saved_is_an_Annotation_Earned_Using_Fully_Synthetic_Training_for_Object_Instance_Detection.pdf | 9
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An Annotation Saved is an Annotation Earned:
Using Fully Synthetic Training for Object Instance Detection
Stefan Hinterstoisser, Olivier Pauly∗, Hauke Heibel ∗, Martina Marek, Martin Bokeloh ∗
Google Cloud AI
Erika-Mann-Strasse 33, 80636 Munich, ... |
synthetic_cpt | 2 | Evaluating_Large_Language_Models_in_Generating_Synthetic_HCI_Research_Data_a_Case_Study.pdf | 4
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Concerns on Bias in Large Language Models when Creating Synthetic Personae
HELENA A. HAXVIG, Dipartimento Di Ingegneria E Scienza Dell’Informazione, Università Di Trento, Italia
This position paper explores the benefits, drawbacks, and ethical cons... |
synthetic_cpt | 3 | A_Practical_Guide_to_Fine-tuning_Language_Models_with_Limited_Data.pdf | Partial Fine-Tuning: A Successor to Full Fine-Tuning for Vision Transformers
Peng Ye1†, Yongqi Huang1†, Chongjun Tu1,
Minglei Li1, Tao Chen1*, Tong He2, Wanli Ouyang2
1Fudan University, 2Shanghai AI Laboratory
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Abstract
Fine-tuning pre-trained fo... |
synthetic_cpt | 2 | Reassessing_Layer_Pruning_in_LLMs_New_Insights_and_Methods.pdf | Work in Progress
REASSESSING LAYER PRUNING IN LLMS:
NEW INSIGHTS AND METHODS
Yao Lu1∗ Hao Cheng Yujie Fang1 Zeyu Wang1
Dongwei Xu1 Qi Xuan1† Xiaoniu Yang1 Zhaowei Zhu
1Zhejiang University of Technology
2HKUST-GZ
Jiaheng Wei2
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ABSTRACT
Althoug... |
synthetic_cpt | 2 | PRESENT_Zero-Shot_Text-to-Prosody_Control.pdf | 1
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Indicable Groups and Endomorphic Presentations
Mustafa G¨okhan Benli
September 14, 2021
Abstract
In this note we look at presentations of subgroups of finitely presented
groups with infinite cyclic quotients. We prove that if H is a finitely
gen... |
synthetic_cpt | 1 | Automated_LLM_enabled_extraction_of_synthesis_details_for_reticular_materials_from_scientific_literature.pdf | Automated Fix Detection Given Flaky Tests
David Landsberg
University College London
d.landsberg@ucl.ac.uk
Earl T. Barr
University College London
e.barr@ucl.ac.uk
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1 Introduction Developers ignore tools that they think waste
their time — hampering t... |
synthetic_cpt | 1 | Data_Augmentation_and_Feature_Engineering_for_Machine_Learning_in_Neutron_Activation_Analysis.pdf | 4
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ACTION: Augmentation and Computation Toolbox for Brain Network
Analysis with Functional MRI
Yuqi Fanga, Junhao Zhangb, Linmin Wangb, Qianqian Wanga and Mingxia Liua,∗
aDepartment of Radiology and Biomedical Research Imaging Center, Universi... |
synthetic_cpt | 4 | LEGO_Language_Model_Building_Blocks.pdf | Proceedings of the ASME 2024
International Symposium on Flexible Automation
ISFA 2024
July 21-24, 2024, Seattle, WA
ISFA2024-139981
4
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A LIGHTWEIGHT AND TRANSFERABLE DESIGN
FOR ROBUST LEGO MANIPULATION
Ruixuan Liu, Yifan Sun, Changliu Liu ∗†
Robotics Institute
Carnegie Mellon University
Pittsburg... |
synthetic_cpt | 1 | SciDaSynth_Interactive_Structured_Knowledge_Extraction_and_Synthesis_from_Scientific_Literature_with_Large_Language_Model.pdf | SciDaSynth: Interactive Structured Knowledge Extraction and
Synthesis from Scientific Literature with Large Language Model
Samantha L. Huey
Cornell University
Ithaca, USA
slh277@cornell.edu
Xingbo Wang
Weill Cornell Medicine
New York, USA
xiw4011@med.cornell.edu
Rui Sheng
Hong Kong University of Science and
Technolog... |
synthetic_cpt | 1 | Modeling_And_Decision_Tree_Based_Prediction_of_Pitch_Contour_In_IBM_Mandarin_Speech_Synthesis_System.pdf | Generating Mandarin and Cantonese F0 Contours
with Decision Trees and BLSTMs
Weidong Yuan, Alan W Black
Language Technologies Institute, Carnegie Mellon University, Pittsburgh, USA
weidongy@andrew.cmu.edu, awb@cs.cmu.edu
Abstract
2. Decision tree
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synthetic_cpt | 3 | KnowledgeSG_Privacy-Preserving_Synthetic_Text_Generation_with_Knowledge_Distillation_from_Server.pdf | KnowledgeSG: Privacy-Preserving Synthetic Text Generation with
Knowledge Distillation from Server
Wenhao Wang1,3,4, Xiaoyu Liang1, Rui Ye2,4, Jingyi Chai2,4,
Siheng Chen2,3,4 *, Yanfeng Wang2,3 *,
1Zhejiang University, 2Shanghai Jiao Tong University,
3Shanghai AI Laboratory,
4Multi-Agent Governance & Intelligence Crew... |
synthetic_cpt | 2 | Decoding_Data_Quality_via_Synthetic_Corruptions_Embedding-guided_Pruning_of_Code_Data.pdf | 3
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Decoding Data Quality via Synthetic Corruptions:
Embedding-guided Pruning of Code Data
Yu Yang1,2∗
yuyang@cs.ucla.edu
Aaditya K. Singh2
aaditya.singh.21@ucl.ac.uk
Mostafa Elhoushi2
melhoushi@meta.com
Anas Mahmoud2
nas.mahmoud@mail.utoronto.ca
Kus... |
synthetic_cpt | 2 | Active_Data_Curation_Effectively_Distills_Large-Scale_Multimodal_Models.pdf | Active Data Curation Effectively Distills Large-Scale Multimodal Models
Vishaal Udandarao* 3,4‡ Nikhil Parthasarathy*2 Muhammad Ferjad Naeem1
Samuel Albanie2
Federico Tombari1 Yongqin Xian1† Alessio Tonioni1† Olivier J. H´enaff2†
Talfan Evans2
1Google 2Google DeepMind 3T¨ubingen AI Center, University of T¨ubingen 4... |
synthetic_cpt | 1 | ChatGPT_usage_in_the_Reactome_curation_process.pdf | 3
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ChatGPT is a Remarkable Tool—For Experts
Amos Azaria1, Rina Azoulay2, and Shulamit Reches3
1School of Computer Science, Ariel University, Israel
2Dept. of Computer Science, Jerusalem College of Technology, Israel
3Dept. of Mathematics, Jerusalem ... |
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