topic stringclasses 2
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synthetic_cpt | 2 | LLM-Neo_Parameter_Efficient_Knowledge_Distillation_for_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 | 1 | Role_of_Data_Augmentation_Strategies_in_Knowledge_Distillation_for_Wearable_Sensor_Data.pdf | IEEE INTERNET OF THINGS JOURNAL, VOL. 0, NO. 0, JANUARY 2022
1
Role of Data Augmentation Strategies in
Knowledge Distillation for Wearable Sensor Data
Eun Som Jeon, Student Member, IEEE, Anirudh Som, Ankita Shukla, Kristina Hasanaj, Matthew P. Buman,
and Pavan Turaga, Senior Member, IEEE
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synthetic_cpt | 1 | The_Promise_and_Challenge_of_Large_Language_Models_for_Knowledge_Engineering_Insights_from_a_Hackathon.pdf | Using Large Language Models for Knowledge
Engineering (LLMKE): A Case Study on Wikidata
Bohui Zhang1, Ioannis Reklos1, Nitisha Jain1, Albert Meroño Peñuela1 and
Elena Simperl1
1Department of Informatics, King’s College London, London, UK
Abstract
In this work, we explore the use of Large Language Models (LLMs) for k... |
synthetic_cpt | 2 | Medical_Image_Synthesis_via_Fine-Grained_Image-Text_Alignment_and_Anatomy-Pathology_Prompting.pdf | 4
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Medical Image Synthesis via
Fine-Grained Image-Text Alignment and
Anatomy-Pathology Prompting
Wenting Chen1, Pengyu Wang2, Hui Ren3, Lichao Sun4, Quanzheng Li3,
Yixuan Yuan2∗, and Xiang Li3⋆
1City University of Hong Kong 2The Chinese University... |
synthetic_cpt | 4 | Increasing_Diversity_While_Maintaining_Accuracy_Text_Data_Generation_with_Large_Language_Models_and_Human_Interventions.pdf | 4
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DATTA: Towards Diversity Adaptive Test-Time
Adaptation in Dynamic Wild World
Chuyang Ye1*, Dongyan Wei1*, Zhendong Liu1, Yuanyi Pang1, Yixi Lin1,
Jiarong Liao1, Qinting Jiang2, Xianghua Fu1, Qing Li1, and Jingyan Jiang1((cid:12))
1 Shenzhen Tech... |
synthetic_cpt | 1 | Search_Query_Spell_Correction_with_Weak_Supervision_in_E-commerce.pdf | Spelling Correction with Denoising Transformer
Alex Kuznetsov
HubSpot, Inc.
Dublin, Ireland
akuznetsov@hubspot.com
Hector Urdiales
HubSpot, Inc.
Dublin, Ireland
hector@hubspot.com
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We present a novel method of performing
spelling correc... |
synthetic_cpt | 6 | Smaller_Weaker_Yet_Better_Training_LLM_Reasoners_via_Compute-Optimal_Sampling.pdf | 0
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Polygon Vertex Extremality and Decomposition of Polygons
Wiktor J. Mogilski
Abstract
In this paper, we show that if we decompose a polygon into two smaller polygons, then by comparing
the number of extremal vertices in the original polygon v... |
synthetic_cpt | 1 | Full-dose_PET_Synthesis_from_Low-dose_PET_Using_High-efficiency_Diffusion_Denoising_Probabilistic_Model.pdf | 8
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DISTRIBUTIONS OF FULL AND NON-FULL WORDS IN
BETA-EXPANSIONS
YAO-QIANG LI AND BING LI
∗
Abstract. The structures of full words and non-full for β-expansions are completely characterized
in this paper. We obtain the precise lengths of all th... |
synthetic_cpt | 1 | Towards_Robust_Evaluation_of_Unlearning_in_LLMs_via_Data_Transformations.pdf | Abhinav Joshi♣
Sriram Vema⋄
Towards Robust Evaluation of Unlearning in LLMs via Data
Transformations
Shaswati Saha⋄
Harsh Jhamtani¶
Ashutosh Modi♣
♣Indian Institute of Technology, Kanpur
¶Microsoft, ⋄University of Maryland Baltimore County
hjhamtani@microsoft.com, {ssaha3,sriramv1,manas}@umbc.edu,
{ajoshi,divyaksh,ash... |
synthetic_cpt | 2 | ASVD_Activation-aware_Singular_Value_Decomposition_for_Compressing_Large_Language_Models.pdf | 4
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ASVD: ACTIVATION-AWARE SINGULAR VALUE DE-
COMPOSITION FOR COMPRESSING LARGE LANGUAGE
MODELS
Zhihang Yuan∗
Houmo AI
hahnyuan@gmail.com
Yuzhang Shang∗
Illinois Institute of Technology
yshang4@hawk.iit.edu
Yue Song
University of Trento
yue.song@uni... |
synthetic_cpt | 6 | Small_Language_Model_as_Data_Prospector_for_Large_Language_Model.pdf | Small Language Model as Data Prospector for Large Language Model
Shiwen Ni1*, Haihong Wu1,2*, Di Yang1,2, Qiang Qu1, Hamid Alinejad-Rokny3, Min Yang1,4†
1Shenzhen Key Laboratory for High Performance Data Mining,
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences
2University of Science and Technol... |
synthetic_cpt | 4 | Neural_Data_Augmentation_via_Example_Extrapolation.pdf | Neural Data Augmentation via Example Extrapolation
Kenton Lee ∗ Kelvin Guu ∗ Luheng He ∗ Timothy Dozat ∗ Hyung Won Chung ∗
{kentonl, kguu, luheng, tdozat, hwchung}@google.com
Google Research
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Abstract
In many applications of machine learning,
cer... |
synthetic_cpt | 2 | Matchmaker_Self-Improving_Large_Language_Model_Programs_for_Schema_Matching.pdf | Journal of Artificial Intelligence Research 29 (2007) 269-307
Submitted 8/06; published 7/07
Semantic Matchmaking as Non-Monotonic Reasoning: A
Description Logic Approach
Tommaso Di Noia
Eugenio Di Sciascio
SisInfLab - Politecnico di Bari, Bari, Italy
Francesco M. Donini
Universit`a della Tuscia, Viterbo, Italy
t.d... |
synthetic_cpt | 1 | Assessing_privacy_and_quality_of_synthetic_health_data.pdf | Article
Generating Synthetic Health Sensor Data for Privacy-Preserving
Wearable Stress Detection
Lucas Lange *
, Nils Wenzlitschke and Erhard Rahm
ScaDS.AI Dresden/Leipzig, Leipzig University, Augustusplatz 10, 04109 Leipzig, Germany;
nw20hewo@studserv.uni-leipzig.de (N.W.); rahm@informatik.uni-leipzig.de (E.R.)
* C... |
synthetic_cpt | 2 | Parameter-Efficient_Legal_Domain_Adaptation.pdf | 2
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Statefinder Parameters for Different Dark Energy Models with
Variable G Correction in Kaluza-Klein Cosmology
Shuvendu Chakraborty1
∗, Ujjal Debnath2
†, Mubasher Jamil3
‡ and Ratbay Myrzakulov4,5
1Department of Mathematics, Seacom... |
synthetic_cpt | 2 | Climate_Change_from_Large_Language_Models.pdf | JOURNAL OF IEEE
1
Climate Change from Large Language Models
Hongyin Zhu, Prayag Tiwari
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Abstract—Climate change poses grave challenges, demanding
widespread understanding and low-carbon lifestyle awareness.
Large language models (LLMs) offer a po... |
synthetic_cpt | 7 | CodecLM_Aligning_Language_Models_with_Tailored_Synthetic_Data.pdf | CodecLM: Aligning Language Models with Tailored Synthetic Data
Zifeng Wang†, Chun-Liang Li†, Vincent Perot∗, Long T. Le†,
Jin Miao‡, Zizhao Zhang‡, Chen-Yu Lee†, Tomas Pfister†
†Google Cloud AI Research, ‡Google Cloud AI, ∗Google Research
{zifengw, chunliang, vperot, longtle,
jinmiao, zizhaoz, chenyulee, tpfister}@goo... |
synthetic_cpt | 4 | Leveraging_Large_Language_Models_for_Code-Mixed_Data_Augmentation_in_Sentiment_Analysis.pdf | 4
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MULTI-PROGRAMMING LANGUAGE ENSEMBLE FOR
CODE GENERATION IN LARGE LANGUAGE MODEL
Tengfei Xue, Xuefeng Li, Tahir Azim, Roman Smirnov, Jianhui Yu,
Arash Sadrieh, and Babak Pahlavan
NinjaTech AI
ABSTRACT
Large language models (LLMs) have significantl... |
synthetic_cpt | 4 | Self-ICL_Zero-Shot_In-Context_Learning_with_Self-Generated_Demonstrations.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 | 1 | Benchmarking_Sim2Real_Gap_High-fidelity_Digital_Twinning_of_Agile_Manufacturing.pdf | Bridging the Sim2Real gap with CARE:
Supervised Detection Adaptation with Conditional Alignment and Reweighting
Viraj Prabhu 1 David Acuna 2 Andrew Liao 2 Rafid Mahmood 2 Marc T. Law 2
Judy Hoffman 1 Sanja Fidler 2 James Lucas 2
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Sim2Real ... |
synthetic_cpt | 1 | Stacking_Small_Language_Models_for_Generalizability.pdf | 4
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STACKING SMALL LANGUAGE MODELS FOR GENER-
ALIZABILITY
Laurence Liang ∗
McGill University
ABSTRACT
Recent advances show that large language models (LLMs) generalize strong per-
formance across different natural language benchmarks. However, the l... |
synthetic_cpt | 1 | Synthetic_Data_Generation_for_Steel_Defect_Detection_and_Classification_Using_Deep_Learning.pdf | Deep Learning Based Steel Pipe Weld Defect Detection
Dingming Yanga , Yanrong Cuia* , Zeyu Yub and Hongqiang Yuanc
aSchool of Computer Science, Yangtze University, Jingzhou 434023, China;
bSchool of Electronics & Information, Yangtze University, Jingzhou 434023, China;
cSchool of Urban Construction, Yangtze Uni... |
synthetic_cpt | 2 | SA-DS_A_Dataset_for_Large_Language_Model-Driven_AI_Accelerator_Design_Generation.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 | Examplar-Based_Speechwaveform_Generation_for_Text-To-Speech.pdf | Stamp processing with examplar features
Yash Bhalgat
Mandar Kulkarni
Shirish Karande
TCS Innovation Labs, Pune, India
Sachin Lodha
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Abstract—Document digitization is becoming increasingly cru-
cial. In this work, we propose a shape based appro... |
synthetic_cpt | 1 | Generalizable_No-Reference_Image_Quality_Assessment_via_Deep_Meta-Learning.pdf | 4
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Generalizability of Memorization Neural Networks
Lijia Yu1, Xiao-Shan Gao2,3,
∗ , Lijun Zhang1,3, Yibo Miao2,3
1 Key Laboratory of System Software CAS and
State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Science... |
synthetic_cpt | 1 | Benchmarking_and_Analyzing_In-context_Learning_Fine-tuning_and_Supervised_Learning_for_Biomedical_Knowledge_Curation_a_focused_study_on_chemical_entities_of_biological_interest.pdf | Mapping global dynamics of benchmark creation
and saturation in artificial intelligence
Simon Ott1,*, Adriano Barbosa-Silva1,2*, Kathrin Blagec1, Jan Brauner3,4 and
Matthias Samwald1,§
1 Institute of Artificial Intelligence, Medical University of Vienna. Währingerstraße 25a, 1090,
Vienna, Austria.
2 ITTM S.A.—Inform... |
synthetic_cpt | 7 | Self-Evolved_Reward_Learning_for_LLMs.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 | 1 | Automatic_Variation_of_the_Degree_of_Articulation_in_New_HMM-Based_Voices.pdf | Analysis and Synthesis of Hypo and Hyperarticulated Speech
Benjamin Picart, Thomas Drugman, Thierry Dutoit
TCTS Lab, Facult´e Polytechnique (FPMs), University of Mons (UMons), Belgium
{benjamin.picart,thomas.drugman,thierry.dutoit}@umons.ac.be
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synthetic_cpt | 3 | Explicit_Diversity_Conditions_for_Effective_Question_Answer_Generation_with_Large_Language_Models.pdf | Explicit Diversity Conditions for Effective Question Answer Generation
with Large Language Models
Vikas Yadav†, Hyuk Joon Kwon‡, Vijay Srinivasan‡, Hongxia Jin‡
ServiceNow†, Samsung Research America‡, USA
vikas.yadav@servicenow.com, bluecube246@gmail.com,
v.srinivasan,hongxia.jin}@samsung.com
Abstract
Question Answe... |
synthetic_cpt | 6 | Rule-based_Data_Selection_for_Large_Language_Models.pdf | 1
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Type of Leibniz Rule on Riemann-Liouville Variable-Order
Fractional Integral and Derivative Operator
Dagnachew Jenbera,⋆, Mollalign Hailleb
aDepartment of Mathematics, Addis Ababa Science and Technology University, Addis Ababa, Ethiopia
De... |
synthetic_cpt | 6 | Clinical_Camel_An_Open-Source_Expert-Level_Medical_Language_Model_with_Dialogue-Based_Knowledge_Encoding.pdf | Clinical Camel: An Open Expert-Level Medical
Language Model with Dialogue-Based Knowledge
Encoding
Augustin Toma1,2,∗
Patrick R. Lawler3,4,5
Jimmy Ba1,6 Rahul G. Krishnan1,6,7
Barry B Rubin3 Bo Wang1,3,6,7,8,∗,†
1Vector Institute for Artificial Intelligence, Toronto, Canada
2Department of Medical Biophysics, Univer... |
synthetic_cpt | 1 | A_Parallel_Grammar_for_Simulation-Driven_Mechanical_Design_Synthesis.pdf | Basic Classes of Grammars with Prohibition
Mark Burgin
University of California, Los Angeles
Los Angeles, CA 90095, USA
ABSTRACT
A practical tool for natural language modeling and development of human-machine
interaction is developed in the context of formal grammars and languages. A n... |
synthetic_cpt | 2 | Evaluation_Metrics_in_the_Era_of_GPT-4_Reliably_Evaluating_Large_Language_Models_on_Sequence_to_Sequence_Tasks.pdf | Bridging History with AI: A Comparative Evaluation of GPT-
3.5, GPT-4, and Google-BARD in Predictive Accuracy and Fact-
Checking
Davut Emre TAŞAR1
Karabuk University
Computer Engineering
Karabük, Turkey
2228126453@ogrenci.karabuk.edu.tr
ORCID:0000-0002-7788-0478
Ceren ÖCAL TAŞAR1
Independent Researcher
İzm... |
synthetic_cpt | 2 | InsCL_A_Data-efficient_Continual_Learning_Paradigm_for_Fine-tuning_Large_Language_Models_with_Instructions.pdf | InsCL: A Data-efficient Continual Learning Paradigm for Fine-tuning
Large Language Models with Instructions
Yifan Wang1∗, Yafei Liu2∗, Chufan Shi1, Haoling Li1,
Chen Chen2, Haonan Lu2, Yujiu Yang1†
2 OPPO AI Center
1 Tsinghua University
{wangyifa22,scf22,li-hl23}@mails.tsinghua.edu.cn
{liuyafei,chenchen4,luhaonan}@o... |
synthetic_cpt | 3 | DeepSeekMath_Pushing_the_Limits_of_Mathematical_Reasoning_in_Open_Language_Models.pdf | DeepSeekMath: Pushing the Limits of Mathematical
Reasoning in Open Language Models
Zhihong Shao1,2∗†, Peiyi Wang1,3∗†, Qihao Zhu1,3∗†, Runxin Xu1, Junxiao Song1
Xiao Bi1, Haowei Zhang1, Mingchuan Zhang1, Y.K. Li1, Y. Wu1, Daya Guo1∗
1DeepSeek-AI, 2Tsinghua University, 3Peking University
{zhihongshao,wangpeiyi,zhuqh,... |
synthetic_cpt | 7 | Making_Large_Language_Models_Better_Data_Creators.pdf | 4
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Proxona: Leveraging LLM-Driven Personas to Enhance Creators’
Understanding of Their Audience
Yoonseo Choi
yoonseo.choi@kaist.ac.kr
School of Computing, KAIST
Republic of Korea
Eun Jeong Kang
ek646@cornell.edu
Information Science, Cornell
Univers... |
synthetic_cpt | 1 | Comparative_Analysis_of_News_Articles_Summarization_using_LLMs.pdf | Embrace Divergence for Richer Insights:
A Multi-document Summarization Benchmark and a Case Study on
Summarizing Diverse Information from News Articles
Kung-Hsiang Huang1∗
Philippe Laban2 Alexander R. Fabbri2
Prafulla Kumar Choubey2
Shafiq Joty2 Caiming Xiong2 Chien-Sheng Wu2
1University of Illinois Urbana-Champaign... |
synthetic_cpt | 2 | ToolLLM_Facilitating_Large_Language_Models_to_Master_16000+_Real-world_APIs.pdf | AnyTool: Self-Reflective, Hierarchical Agents for Large-Scale API Calls
Yu Du1 *
1Tsinghua University
Fangyun Wei2 * †
2Microsoft Research Asia
Hongyang Zhang3
3University of Waterloo
duyu20@mails.tsinghua.edu.cn fawe@microsoft.com hongyang.zhang@uwaterloo.ca
* Equal contribution
† Corresponding author
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synthetic_cpt | 2 | Large_Language_Models_are_not_Fair_Evaluators.pdf | 3
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Selecting Shots for Demographic Fairness in Few-Shot Learning
with Large Language Models
Carlos Aguirre, Kuleen Sasse, Isabel Cachola and Mark Dredze
Center for Language and Speech Processing
Johns Hopkins University
caguirre@cs.jhu.edu
Abstract
... |
synthetic_cpt | 6 | WANLI_Worker_and_AI_Collaboration_for_Natural_Language_Inference_Dataset_Creation.pdf | Extremes of locally stationary Gaussian and chi fields on
manifolds
Wanli Qiao
Department of Statistics
George Mason University
4400 University Drive, MS 4A7
Fairfax, VA 22030
USA
Email: wqiao@gmu.edu
May 15, 2020
Abstract
(0, 1], let
Depending on a parameter h
fields indexed by compact manifolds
study the asymptoti... |
synthetic_cpt | 2 | Generating_Realistic_Tabular_Data_with_Large_Language_Models.pdf | TabuLa: Harnessing Language Models for Tabular Data Synthesis
Zilong Zhao∗
Technical University of Munich
Munich, Germany
zilong.zhao@tum.de
Robert Birke
University of Turin
Turin, Italy
robert.birke@unito.it
Lydia Y. Chen
Delft University of Technology
Delft, Netherlands
lydiaychen@ieee.org
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synthetic_cpt | 1 | Call_for_Papers_-_The_BabyLM_Challenge_Sample-efficient_pretraining_on_a_developmentally_plausible_corpus.pdf | Scalable Call Graph Constructor for Maven
1st Mehdi Keshani
Technical University of Delft, m.keshani@tudelft.nl
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various applications including security vulnerability detection.
Despite ... |
synthetic_cpt | 3 | Adapting_Language_Models_via_Token_Translation.pdf | 4
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Adapting Language Models via Token Translation
Zhili Feng
Carnegie Mellon University
Tanya Marwah
Carnegie Mellon University
Nicolò Fusi
Microsoft Research
David Alvarez-Melis
Microsoft Research
Lester Mackey
Microsoft Research
Abstract
Modern... |
synthetic_cpt | 1 | Efficient_Vision-Language_Pretraining_with_Visual_Concepts_and_Hierarchical_Alignment.pdf | 3
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CONTRASTIVE ALIGNMENT OF VISION TO LANGUAGE
THROUGH PARAMETER-EFFICIENT TRANSFER LEARN-
ING
Zaid Khan, Yun Fu
Northeastern University, Boston, USA
{khan.za, y.fu}@northeastern.edu
ABSTRACT
Contrast... |
synthetic_cpt | 3 | Distilling_Named_Entity_Recognition_Models_for_Endangered_Species_from_Large_Language_Models.pdf | Distilling Named Entity Recognition Models for Endangered Species
from Large Language Models
Jesse Atuhurra
Seiveright Cargill Dujohn
Hidetaka Kamigaito
Hiroyuki Shindo
Taro Watanabe
Division of Information Science, NAIST
{atuhurra.jesse.ag2, seiveright.cargill_dujohn.sf4, kamigaito.h, shindo, taro} @naist.ac.jp
... |
synthetic_cpt | 1 | A_Synthetic_Corpus_Generation_Method_for_Neural_Vocoder_Training.pdf | Relational Data Selection for Data Augmentation of Speaker-dependent
Multi-band MelGAN Vocoder
Yi-Chiao Wu1, Cheng-Hung Hu2, Hung-Shin Lee2, Yu-Huai Peng2, Wen-Chin Huang1, Yu Tsao2,
Hsin-Min Wang2, and Tomoki Toda1
1Nagoya University, Japan
2Academia Sinica, Taiwan
yichiao.wu@g.sp.m.is.nagoya-u.ac.jp, tomoki@icts.na... |
synthetic_cpt | 1 | Evaluating_Large_Language_Models_Trained_on_Code.pdf | 9
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Language Modeling for Code-Switching:
Evaluation, Integration of Monolingual Data, and Discriminative Training
Hila Gonen1 and Yoav Goldberg1,2
1Department of Computer Science, Bar-Ilan University
2Allen Institute for Artificial Intelligence
{hilag... |
synthetic_cpt | 3 | Select_High-quality_Synthetic_QA_Pairs_to_Augment_Training_Data_in_MRC_under_the_Reward_Guidance_of_Generative_Language_Models.pdf | Noname manuscript No.
(will be inserted by the editor)
Selective Inference via Marginal Screening for High
Dimensional Classification
Yuta Umezu · Ichiro Takeuchi
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Received: date / Accepted: date
Abstract Post-selection inference is a stati... |
synthetic_cpt | 3 | The_Potential_and_Limitations_of_Large_Language_Models_for_Text_Classification_through_Synthetic_Data_Generation.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 | 2 | Evaluating_the_Impact_of_Compression_Techniques_on_Task-Specific_Performance_of_Large_Language_Models.pdf | 4
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Evaluating the Impact of Compression Techniques on
Task-Specific Performance of Large Language Models
Bishwash Khanal1 and Jeffery M. Capone2
1bishwash.khanal@optiml.org
2jeff.capone@optiml.org
OptiML Org, California, USA
Abstract
Large languag... |
synthetic_cpt | 2 | Reframing_Instructional_Prompts_to_GPTk’s_Language.pdf | Reframing Instructional Prompts to GPTk’s Language
ACL 2022 Findings
Swaroop Mishra3 Daniel Khashabi1 Chitta Baral3 Yejin Choi1,2 Hannaneh Hajishirzi1,2
1Allen Institute for AI
2University of Washington 3Arizona State University
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Wha... |
synthetic_cpt | 1 | Parrot_Mind_Towards_Explaining_the_Complex_Task_Reasoning_of_Pretrained_Large_Language_Models_with_Template-Content_Structure.pdf | 3
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Predictive Minds: LLMs As Atypical Active Inference
Agents
Jan Kulveit1∗
Clem von Stengel1
Roman Leventov2
1 Alignment of Complex Systems Research Group, Center for Theoretical Study, Charles University
2 Gaia Consortium
Abstract
Large langua... |
synthetic_cpt | 2 | SVD-LLM_Truncation-aware_Singular_Value_Decomposition_for_Large_Language_Model_Compression.pdf | (IJACSA) International Journal of Advanced Computer Science and Applications,
Vol. 3, No. 7, 2012
SVD Based Image Processing Applications: State of
The Art, Contributions and Research Challenges
Rowayda A. Sadek*
Computer Engineering Department, College of Engineering and Technology, Arab Academy for Science
T... |
synthetic_cpt | 1 | An_Annotation_Saved_is_an_Annotation_Earned_Using_Fully_Synthetic_Training_for_Object_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 | 1 | Towards_Self-Explainability_of_Deep_Neural_Networks_with_Heatmap_Captioning_and_Large-Language_Models.pdf | 4
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SELF-SIMILARITY IN A THIN FILM MUSKAT PROBLEM
PHILIPPE LAURENÇOT AND BOGDAN–VASILE MATIOC
Abstract. The large time behavior of non-negative weak solutions to a thin film ap-
proximation of the two-phase Muskat problem is studied. A classificatio... |
synthetic_cpt | 2 | An_Empirical_Study_of_Instruction-tuning_Large_Language_Models_in_Chinese.pdf | Ada-Instruct: Adapting Instruction Generators for Complex Reasoning
Wanyun Cui and Qianle Wang
Shanghai University of Finance and Economics
cui.wanyun@sufe.edu.cn, wql20000111@stu.sufe.edu.cn
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Instructions augmentation is a crucial step fo... |
synthetic_cpt | 2 | OmniQuant_Omnidirectionally_Calibrated_Quantization_for_Large_Language_Models.pdf | 4
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OMNIQUANT: OMNIDIRECTIONALLY CALIBRATED
QUANTIZATION FOR LARGE LANGUAGE MODELS
Wenqi Shao†1, Mengzhao Chen†1, Zhaoyang Zhang3, Peng Xu1,2, Lirui Zhao1,
Zhiqian Li2, Kaipeng Zhang1, Peng Gao1, Yu Qiao... |
synthetic_cpt | 4 | T-REG_Preference_Optimization_with_Token-Level_Reward_Regularization.pdf | 2
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EXISTENCE AND CONVERGENCE RESULTS FOR
INFINITE DIMENSIONAL NONLINEAR STOCHASTIC
EQUATIONS WITH MULTIPLICATIVE NOISE
VIOREL BARBU, ZDZIS LAW BRZE´ZNIAK, ERIKA HAUSENBLAS,
AND LUCIANO TUBARO
2 PN
j=1 Bn
j (t)Xn(t)dβn
j=1(Bn
j (t) + fn(t) dt,... |
synthetic_cpt | 2 | Efficient_Vision-Language_pre-training_via_domain-specific_learning_for_human_activities.pdf | 3
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CONTRASTIVE ALIGNMENT OF VISION TO LANGUAGE
THROUGH PARAMETER-EFFICIENT TRANSFER LEARN-
ING
Zaid Khan, Yun Fu
Northeastern University, Boston, USA
{khan.za, y.fu}@northeastern.edu
ABSTRACT
Contrast... |
synthetic_cpt | 3 | RuAG_Learned-rule-augmented_Generation_for_Large_Language_Models.pdf | 4
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Preprint.
RuAG:
FOR LARGE LANGUAGE MODELS
LEARNED-RULE-AUGMENTED GENERATION
Yudi Zhang1∗
, Pei Xiao2*, Lu Wang3, Chaoyun Zhang3, Meng Fang4, Yali Du5, Yevgeniy
Puzyrev3, Randolph Yao3, Si Qin3, Qingwei Lin3, Mykola Pechenizkiy1, Dongmei Zhang3,
S... |
synthetic_cpt | 1 | DACL_Disfluency_Augmented_Curriculum_Learning_for_Fluent_Text_Generation.pdf | Towards Domain-Agnostic Contrastive Learning
Vikas Verma 1 2 Minh-Thang Luong 1 Kenji Kawaguchi 3 Hieu Pham 1 Quoc V. Le 1
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Despite recent successes, most contrastive self-
supervised learning methods are domain-specific,
relying heavil... |
synthetic_cpt | 1 | 3D_GAN_image_synthesis_and_dataset_quality_assessment_for_bacterial_biofilm.pdf | LCUTS: LINEAR CLUSTERING OF BACTERIA USING RECURSIVE GRAPH CUTS
J. Wang †, T. Batabyal †, M. Zhang ‡, J. Zhang ‡, A. Aziz ‡, A. Gahlmann ‡ and S. T. Acton †
†Department of Electrical & Computer Engineering and ‡Department of Chemistry
University of Virginia, Charlottesville, VA 22904, USA
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synthetic_cpt | 2 | CPTQuant_-_A_Novel_Mixed_Precision_Post-Training_Quantization_Techniques_for_Large_Language_Models.pdf | CPTQuant - A Novel Mixed Precision Post-Training Quantization
Techniques for Large Language Models
Amitash Nanda
UC San Diego, ECE
La Jolla, CA, USA
ananda@ucsd.edu
Sree Bhargavi Balija
UC San Diego, ECE
La Jolla, CA, USA
sbalija@ucsd.edu
Debashis Sahoo
UC San Diego, CSE
La Jolla, CA, USA
dsahoo@ucsd.edu
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synthetic_cpt | 1 | MAPL_Parameter-Efficient_Adaptation_of_Unimodal_Pre-Trained_Models_for_Vision-Language_Few-Shot_Prompting.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 | 3 | Self-Generated_Critiques_Boost_Reward_Modeling_for_Language_Models.pdf | 1
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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 | Gradient_Boosting_Trees_and_Large_Language_Models_for_Tabular_Data_Few-Shot_Learning.pdf | TabLLM: Few-shot Classification of Tabular Data with Large Language
Models
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Stefan Hegselmann1,2 Alejandro Buendia1 Hunter Lang1 Monica Agrawal1 Xiaoyi Jiang2 David Sontag1
1 MIT CSAIL 2 University of M¨unster
Abstract
We study the application of... |
synthetic_cpt | 1 | Parameter-Efficient_Quantized_Mixture-of-Experts_Meets_Vision-Language_Instruction_Tuning_for_Semiconductor_Electron_Micrograph_Analysis.pdf | 2
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Statefinder Parameters for Different Dark Energy Models with
Variable G Correction in Kaluza-Klein Cosmology
Shuvendu Chakraborty1
∗, Ujjal Debnath2
†, Mubasher Jamil3
‡ and Ratbay Myrzakulov4,5
1Department of Mathematics, Seacom... |
synthetic_cpt | 1 | MiAMix_Enhancing_Image_Classification_through_a_Multi-stage_Augmented_Mixed_Sample_Data_Augmentation_Method.pdf | 3
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MiAMix: Enhancing Image Classification through a
Multi-stage Augmented Mixed Sample Data
Augmentation Method
Wen Liang
Google Inc.
Mountain View, CA 94043
liangwen@google.com
Youzhi Liang
Department of Computer Science
Stanford University
Stanfo... |
synthetic_cpt | 2 | Trusting_Your_Evidence_Hallucinate_Less_with_Context-aware_Decoding.pdf | Trusting Your AI Agent Emotionally and Cognitively: Development and
Validation of a Semantic Differential Scale for AI Trust
Ruoxi Shang1, Gary Hsieh1, Chirag Shah1
1University of Washington
rxshang@uw.edu, garyhs@uw.edu, chirags@uw.edu
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synthetic_cpt | 1 | DoDo_Learning_Domain-Demographic_Transfer_in_Language_Models_for_Detecting_Abuse_Targeted_at_Public_Figures.pdf | DODO : Dynamic Contextual Compression for Decoder-only LMs
Guanghui Qinη∗
Corby Rossetµ
Ethan C. Chauµ
Nikhil Raoµ
Benjamin Van Durmeη,µ
ηJohns Hopkins University
µMicrosoft
{gqin2,vandurme}@jhu.edu
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Transformer-based language mo... |
synthetic_cpt | 2 | Zero-Shot_Dense_Retrieval_with_Embeddings_from_Relevance_Feedback.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 | MetricX-23_The_Google_Submission_to_the_WMT_2023_Metrics_Shared_Task.pdf | MetricX-24: The Google Submission to the WMT 2024
Metrics Shared Task
Juraj Juraska, Daniel Deutsch, Mara Finkelstein and Markus Freitag
Google
{jjuraska,dandeutsch,marafin,freitag}@google.com
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In this paper, we present the MetricX-24 sub-... |
synthetic_cpt | 1 | Advancing_Single-_and_Multi-task_Text_Classification_through_Large_Language_Model_Fine-tuning.pdf | 8
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Reflected Advanced Backward Stochastic Differential
Equations with Default
N. Agram1,2, S. Labed2, B. Mansouri2 & M. A. Saouli2
20 March 2018
Abstract
We are interested on reflected advanced backward stochastic differential equations
(RABSDE... |
synthetic_cpt | 3 | Pretraining_Language_Models_with_Human_Preferences.pdf | Pretraining Language Models with Human Preferences
Tomasz Korbak 1 2 3 Kejian Shi 2 Angelica Chen 2 Rasika Bhalerao 4 Christopher L. Buckley 1 Jason Phang 2
Samuel R. Bowman 2 5 Ethan Perez 2 3 5
Abstract
Language models (LMs) are pretrained to imitate
internet text, including content that would vio-
late human pref... |
synthetic_cpt | 2 | Switch_Transformers_Scaling_to_Trillion_Parameter_Models_with_Simple_and_Efficient_Sparsity.pdf | Crosstalk-free Conjugate Networks for Optical
Multicast Switching
Yun Deng, Student Member, IEEE, Tony T. LEE, Fellow, IEEE
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Abstract— High-speed photonic switching networks can switch
optical signals at the rate of several terabits per second. ... |
synthetic_cpt | 2 | CrossIn_An_Efficient_Instruction_Tuning_Approach_for_Cross-Lingual_Knowledge_Alignment.pdf | CrossIn: An Efficient Instruction Tuning Approach for Cross-Lingual
Knowledge Alignment
Geyu Lin♡, Bin Wang♡, ♢, Zhengyuan Liu♡, ♢, Nancy F. Chen♡, ♢, †
♡Institute for Infocomm Research (I2R), A*STAR, Singapore
♢CNRS@CREATE, Singapore
†Centre for Frontier AI Research (CFAR), A*STAR, Singapore
lin geyu@i2r.a-star.edu.s... |
synthetic_cpt | 8 | Not_All_LLM-Generated_Data_Are_Equal_Rethinking_Data_Weighting_in_Text_Classification.pdf | 4
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COGNITIVE PHANTOMS IN LLMS THROUGH THE LENS OF
LATENT VARIABLES
Sanne Peereboom1, Inga Schwabe1 & Bennett Kleinberg1,2
1Department of Methodology and Statistics
Tilburg University
Tilburg, the Netherlands
2Department of Security and Crime Scienc... |
synthetic_cpt | 1 | A_Comparative_Study_between_Full-Parameter_and_LoRA-based_Fine-Tuning_on_Chinese_Instruction_Data_for_Instruction_Following_Large_Language_Model.pdf | Numerical studies of Casimir interactions
S. Pasquali, A. C. Maggs
Laboratoire de Physico-Chime Th´eorique, Gulliver, CNRS-ESPCI, 10 rue Vauquelin, 75231 Paris Cedex 05, France.
We study numerically the Casimir interaction between dielectrics in both two and three dimensions. We
demonstrate how sparse matrix factoriz... |
synthetic_cpt | 1 | CodeBLEU_a_Method_for_Automatic_Evaluation_of_Code_Synthesis.pdf | CodeBLEU: a Method for Automatic Evaluation of Code Synthesis
Shuo Ren1, Daya Guo2, Shuai Lu3, Long Zhou4, Shujie Liu4,
Duyu Tang4, Neel Sundaresan4, Ming Zhou4, Ambrosio Blanco4, Shuai Ma1
1SKLSDE Lab, Beihang University; Beijing Advanced Innovation Center for Big Data and Brain Computing
2Sun Yat-sen University 3Pek... |
synthetic_cpt | 3 | ZIP-FIT_Embedding-Free_Data_Selection_via_Compression-Based_Alignment.pdf | 4
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Zip-zip Trees: Making Zip Trees More Balanced,
Biased, Compact, or Persistent⋆
Ofek Gila1*[0009−0005−5931−771X], Michael T. Goodrich1*[0000−0002−8943−191X],
and Robert E. Tarjan2*[0000−0001−7505−5768]
1 University of California, Irvine CA 92697, US... |
synthetic_cpt | 8 | SynthVLM_High-Efficiency_and_High-Quality_Synthetic_Data_for_Vision_Language_Models.pdf | SynthVLM: High-Efficiency and High-Quality Synthetic Data for
Vision Language Models
Zheng Liu†♠, Hao Liang†♠, Xijie Huang♠, Wentao Xiong♠, Qinhan Yu♠, Linzhuang Sun♦, Chong
Chen♥, Conghui He♣, Bin Cui♠, Wentao Zhang♠
♠Peking University ♥Huawei Cloud BU ♣Shanghai AI Laboratory ♦University of Chinese Academy of Science... |
synthetic_cpt | 2 | Uncertainty_as_a_Predictor_Leveraging_Self-Supervised_Learning_for_Zero-Shot_MOS_Prediction.pdf | 4
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Beyond Conformal Predictors: Adaptive Conformal
Inference with Confidence Predictors
Johan Hallberg Szabadv´arya,b,∗, Tuwe L¨ofstr¨omb
aDepartment of Mathematics, Stockholm University, Stockholm, Sweden
bDepartment of Computing, J¨onk¨oping ... |
synthetic_cpt | 1 | Self-Attention-Based_Edge_Computing_Model_for_Synthesis_Image_to_Text_through_Next-Generation_AI_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 | 4 | MATES_Model-Aware_Data_Selection_for_Efficient_Pretraining_with_Data_Influence_Models.pdf | 7
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Quadratic matings and ray connections
Wolf Jung
Gesamtschule Brand, 52078 Aachen, Germany,
and Jacobs University, 28759 Bremen, Germany.
E-mail: jung@mndynamics.com
Abstract
A topological mating is a map defined by gluing together the filled... |
synthetic_cpt | 4 | Deep_Learning_on_a_Data_Diet_Finding_Important_Examples_Early_in_Training.pdf | 6
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Can a CNN Recognize Catalan Diet?
Pedro Herruzoa), Marc Bola˜nosb) and Petia Radevac)
Universitat de Barcelona. Barcelona, Spain.
Computer Vision Center. Bellaterra, Spain.
a)pherrusa7@alumnes.ub.edu
b)marc.bolanos@ub.edu
c)petia.ivanova@ub.ed... |
synthetic_cpt | 2 | Pruner-Zero_Evolving_Symbolic_Pruning_Metric_from_scratch_for_Large_Language_Models.pdf | 4
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Pruner: A Speculative Exploration Mechanism to
Accelerate Tensor Program Tuning
Liang Qiao∗
University of Science and Technology
of China
Hefei, China
ql1an9@mail.ustc.edu.cn
Jun Shi
University of Science and Technology
of China
Hefei, China
sh... |
synthetic_cpt | 2 | Mini_But_Mighty_Efficient_Multilingual_Pretraining_with_Linguistically-Informed_Data_Selection.pdf | Mini but Mighty: Finetuning ViTs with Mini Adapters
Imad Eddine Marouf
Enzo Tartaglione
LTCI, T´el´ecom-Paris, Institut Polytechnique de Paris, France
imad.marouf@ip-paris.fr
St´ephane Lathuili`ere
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Abstract
Vision Transformers (ViTs) have becom... |
synthetic_cpt | 3 | The_Effect_of_Synthetic_Voice_Data_Augmentation_on_Spoken_Language_Identification_on_Indian_Languages.pdf | 4
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Spoken Language Corpora Augmentation with
Domain-Specific Voice-Cloned Speech
Mateusz Czy˙znikiewicz, Łukasz Bondaruk,
Jakub Kubiak, Adam Wi ˛acek, Łukasz Degórski
Samsung R&D Institute Poland
Plac Europejski 1
00-844 Warszawa, Poland
Email: ... |
synthetic_cpt | 4 | Chain_of_Hindsight_Aligns_Language_Models_with_Feedback.pdf | 3
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Chain of Hindsight aligns Language
Models with Feedback
Hao Liu
UC Berkeley
hao.liu@berkeley.edu
Carmelo Sferrazza
UC Berkeley
csferrazza@berkeley.edu
Pieter Abbeel
UC Berkeley
pabbeel@cs.berkeley.edu
Abstract
Learning from human preferences ... |
synthetic_cpt | 2 | The_Llama_3_Herd_of_Models.pdf | Herding LLaMaS: Using LLMs as an OS Module
Aditya K Kamath∗
University of Washington
Seattle, Washington, USA
akkamath@uw.edu
Sujay Yadalam∗
University of Wisconsin–Madison
Madison, Wisconsin, USA
sujayyadalam@cs.wisc.edu
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1 INTRODUCTION
Compute... |
synthetic_cpt | 4 | Generative_Adapter_Contextualizing_Language_Models_in_Parameters_with_A_Single_Forward_Pass.pdf | 4
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Dealing with Drift of Adaptation Spaces in Learning-based
Self-Adaptive Systems using Lifelong Self-Adaptation
OMID GHEIBI, Katholieke Universiteit Leuven, Belgium
DANNY WEYNS, Linnaeus University, Sweden, Katholieke Universiteit Leuven, Belgium... |
synthetic_cpt | 7 | A_Little_Help_Goes_a_Long_Way_Efficient_LLM_Training_by_Leveraging_Small_LMs.pdf | A little goes a long way: Improving toxic language classification despite
data scarcity
Mika Juuti1, Tommi Gr¨ondahl2, Adrian Flanagan3, N. Asokan1,2
University of Waterloo1
Aalto University2
Huawei Technologies Oy (Finland) Co Ltd3
mika.juuti@kela.fi, tommi.grondahl@aalto.fi
adrian.flanagan@huawei.com, asokan@acm.org
... |
synthetic_cpt | 4 | Synthesize_Partition_then_Adapt_Eliciting_Diverse_Samples_from_Foundation_Models.pdf | Robust AI-Synthesized Speech Detection Using
Feature Decomposition Learning and Synthesizer
Feature Augmentation
Kuiyuan Zhang, Zhongyun Hua, Yushu Zhang, Yifang Guo, and Tao Xiang
1
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Abstract—AI-synthesized speech, also known as deepfake
speech... |
synthetic_cpt | 1 | SubLIME_Less_is_More_for_LLM_Evaluation.pdf | Astronomy&Astrophysicsmanuscript no. Dzes2e
July 10, 2020
c(cid:13)ESO 2020
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Evaporative cooling of icy interstellar grains
II. Key parameters
Juris Kalv¯ans and Juris Roberts Kalnin
Engineering Research Institute "Ventspils Interna... |
synthetic_cpt | 8 | Self-Boosting_Large_Language_Models_with_Synthetic_Preference_Data.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 | 1 | Training_Data_Augmentation_for_Deep_Learning_RF_Systems.pdf | Spectro-Temporal RF Identification using Deep
Learning
Hai N. Nguyen, Marinos Vomvas, Triet Vo-Huu, Guevara Noubir
{nguyen.hai,m.vomvas,vohuu.t,g.noubir}@northeastern.edu
Cybersecurity and Privacy Institute
Northeastern University
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RF e... |
synthetic_cpt | 2 | QADYNAMICS_Training_Dynamics-Driven_Synthetic_QA_Diagnostic_for_Zero-Shot_Commonsense_Question_Answering.pdf | QADYNAMICS: Training Dynamics-Driven Synthetic QA Diagnostic for
Zero-Shot Commonsense Question Answering
∗
∗
, Weiqi Wang
Haochen Shi
, Tianqing Fang, Baixuan Xu, Wenxuan Ding,
Xin Liu, Yangqiu Song
Department of Computer Science and Engineering, HKUST, Hong Kong SAR, China
hshiah@connect.ust.hk, {wwangbw, tfangaa,... |
synthetic_cpt | 2 | GSM-Plus_A_Comprehensive_Benchmark_for_Evaluating_the_Robustness_of_LLMs_as_Mathematical_Problem_Solvers.pdf | Gamow shell model description of proton scattering on 18Ne
Y. Jaganathen
Department of Physics and Astronomy, University of Tennessee, Knoxville, Tennessee 37996, USA and
Joint Institute of Nuclear Physics and Applications,
Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA
N. Michel and M. P loszajczak
G... |
synthetic_cpt | 1 | The_Unreasonable_Effectiveness_of_Large_Language-Vision_Models_for_Source-free_Video_Domain_Adaptation.pdf | Large Language Models Are Unconscious of Unreasonability
in Math Problems
Jingyuan Ma1 , Damai Dai1 , Lei Sha2, Zhifang Sui1
1National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University
2Institute of Artificial Intelligence, Beihang University
{mjy20020227@163.com}
Abs... |
synthetic_cpt | 2 | Multitask_Prompted_Training_Enables_Zero-Shot_Task_Generalization.pdf | SPT: Semi-Parametric Prompt Tuning for Multitask Prompted Learning
M Saiful Bari∗¶ †, Aston Zhang§ †, Shuai Zheng§, Xingjian Shi§,
Yi Zhu§, Shafiq Joty¶, Mu Li§,
¶Nanyang Technological University §Amazon Web Services
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Abstract
Pre-trained large lan... |
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