Fetching the paper…
Reading the bibliography…
The rapid advancement of Large Language Models (LLMs) has significantly influenced various domains, leveraging their exceptional few-shot and zero-shot learning capabilities.
Feature selection and feature extraction for text categorization
D. D. Lewis · 1992
Earlier work this paper cites.
Regression shrinkage and selection via the lasso
R. Tibshirani · 1996
Earlier work this paper cites.
Feature selection for classification
M. Dash and H. Liu · 1997
Earlier work this paper cites.
Wrappers for feature subset selection
R. Kohavi and G. H. John · 1997
Earlier work this paper cites.
Assessment and comparison of prognostic classification schemes for survival data
E. Graf, C. Schmoor, W. Sauerbrei, and M. Schumacher · 1999
Earlier work this paper cites.
Pattern classification
P. E. Hart, D. G. Stork, R. O. Duda, et al · 2000
Earlier work this paper cites.
Gene selection for cancer classification using support vector machines
I. Guyon, J. Weston, S. Barnhill, and V. Vapnik · 2002
Earlier work this paper cites.
An introduction to variable and feature selection
I. Guyon and A. Elisseeff · 2003
Earlier work this paper cites.
Least angle regression
B. Efron, T. Hastie, I. Johnstone, and R. Tibshirani · 2004
Earlier work this paper cites.
Minimum redundancy feature selection from microarray gene expression data
C. Ding and H. Peng · 2005
Earlier work this paper cites.
Model selection and estimation in regression with grouped variables
M. Yuan and Y. Lin · 2006
Earlier work this paper cites.
Uci machine learning repository, 2007
A. Asuncion, D. Newman, et al · 2007
Earlier work this paper cites.
Communities and Crime
M. Redmond · 2009
Earlier work this paper cites.
Generalized fisher score for feature selection
Q. Gu, Z. Li, and J. Han · 2011
Earlier work this paper cites.
A survey on filter techniques for feature selection in gene expression microarray analysis
C. Lazar, J. Taminau, S. Meganck, D. Steenhoff, A. Coletta, C. Molter, V. de Schaetzen, R. Duque, H. Bersini, and A. Nowe · 2012
Earlier work this paper cites.
Feature selection via dependence maximization
L. Song, A. Smola, A. Gretton, J. Bedo, and K. Borgwardt · 2012
Earlier work this paper cites.
A survey on feature selection methods
G. Chandrashekar and F. Sahin · 2014
Earlier work this paper cites.
Sequential lasso cum ebic for feature selection with ultra-high dimensional feature space
S. Luo and Z. Chen · 2014
Earlier work this paper cites.
A data-driven approach to predict the success of bank telemarketing
S. Moro, P. Cortez, and P. Rita · 2014
Earlier work this paper cites.
Review the cancer genome atlas (tcga): an immeasurable source of knowledge
K. Tomczak, P. Czerwińska, and M. Wiznerowicz · 2015
Earlier work this paper cites.
Feature selection: A data perspective
J. Li, K. Cheng, S. Wang, F. Morstatter, R. P. Trevino, J. Tang, and H. Liu · 2017
Earlier work this paper cites.
Priority-lasso: a simple hierarchical approach to the prediction of clinical outcome using multi-omics data
S. Klau, V. Jurinovic, R. Hornung, T. Herold, and A.-L. Boulesteix · 2018
Earlier work this paper cites.
Correlated differential privacy: Feature selection in machine learning
T. Zhang, T. Zhu, P. Xiong, H. Huo, Z. Tari, and W. Zhou · 2019
Earlier work this paper cites.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
Earlier work this paper cites.
Myocardial infarction complications
S. Golovenkin, V. Shulman, D. Rossiev, P. Shesternya, S. Nikulina, Y. Orlova, and V. Voino-Yasenetsky · 2020
Cited alongside, same era.
Effective ways to build and evaluate individual survival distributions
H. Haider, B. Hoehn, S. Davis, and R. Greiner · 2020
Cited alongside, same era.
Well-tuned simple nets excel on tabular datasets
A. Kadra, M. Lindauer, F. Hutter, and J. Grabocka · 2021
Cited alongside, same era.
Lmpriors: Pre-trained language models as task-specific priors
K. Choi, C. Cundy, S. Srivastava, and S. Ermon · 2022
Cited alongside, same era.
Holistic evaluation of language models
P. Liang, R. Bommasani, T. Lee, D. Tsipras, D. Soylu, M. Yasunaga, Y. Zhang, D. Narayanan, Y. Wu, A. Kumar, et al · 2022
Cited alongside, same era.
Introducing chatgpt
OpenAI · 2022
Benchmarking large language models in retrieval-augmented generation
J. Chen, H. Lin, X. Han, and L. Sun · 2024
Closest in time.
Tabular data augmentation for machine learning: Progress and prospects of embracing generative ai
L. Cui, H. Li, K. Chen, L. Shou, and G. Chen · 2024
Closest in time.
Large language models (llms) on tabular data: Prediction, generation, and understanding-a survey
X. Fang, W. Xu, F. A. Tan, J. Zhang, Z. Hu, Y. J. Qi, S. Nickleach, D. Socolinsky, S. Sengamedu, C. Faloutsos, et al · 2024
Closest in time.
Large language models are zero-shot time series forecasters
N. Gruver, M. Finzi, S. Qiu, and A. G. Wilson · 2024
Closest in time.
Large language models can automatically engineer features for few-shot tabular learning
S. Han, J. Yoon, S. O. Arik, and T. Pfister · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou, et al · 2022
Cited alongside, same era.
Survboard: standardised benchmarking for multi-omics cancer survival models
D. Wissel, N. Janakarajan, A. Grover, E. Toniato, M. R. Martínez, and V. Boeva · 2022
Cited alongside, same era.
J. Achiam, S. Adler, S. Agarwal, L. Ahmad, I. Akkaya, F. L. Aleman, D. Almeida, J. Altenschmidt, S. Altman, S. Anadkat, et al · 2023
Cited alongside, same era.
A survey on evaluation of large language models
Y. Chang, X. Wang, J. Wang, Y. Wu, L. Yang, K. Zhu, H. Chen, X. Yi, C. Wang, Y. Wang, et al · 2023
Cited alongside, same era.
Z. Dong, T. Tang, J. Li, W. X. Zhao, and J.-R. Wen · 2023
Cited alongside, same era.
Retrieval-augmented generation for large language models: A survey
Y. Gao, Y. Xiong, X. Gao, K. Jia, J. Pan, Y. Bi, Y. Dai, J. Sun, and H. Wang · 2023
Cited alongside, same era.
S. Hong, Y. Lin, B. Liu, B. Wu, D. Li, J. Chen, J. Zhang, J. Wang, L. Zhang, M. Zhuge, et al · 2024
Closest in time.
Llm-select: Feature selection with large language models
D. P. Jeong, Z. C. Lipton, and P. Ravikumar · 2024
Closest in time.
Contextualization distillation from large language model for knowledge graph completion
D. Li, Z. Tan, T. Chen, and H. Liu · 2024
Closest in time.
D. Li, S. Yang, Z. Tan, J. Y. Baik, S. Yun, J. Lee, A. Chacko, B. Hou, D. Duong-Tran, Y. Ding, et al · 2024
Closest in time.
Facial affective behavior analysis with instruction tuning
Y. Li, A. Dao, W. Bao, Z. Tan, T. Chen, H. Liu, and Y. Kong · 2024
Closest in time.
Lost in the middle: How language models use long contexts
N. F. Liu, K. Lin, J. Hewitt, A. Paranjape, M. Bevilacqua, F. Petroni, and P. Liang · 2024
Closest in time.
Ice-search: A language model-driven feature selection approach
S. Liu, F. Lvu, X. Liu, et al · 2024
Closest in time.
Mmfakebench: A mixed-source multimodal misinformation detection benchmark for lvlms
X. Liu, Z. Li, P. Li, S. Xia, X. Cui, L. Huang, H. Huang, W. Deng, and Z. He · 2024
Closest in time.
H. Mao, Z. Chen, W. Tang, J. Zhao, Y. Ma, T. Zhao, N. Shah, M. Galkin, and J. Tang · 2024
Closest in time.
Unifying large language models and knowledge graphs: A roadmap
S. Pan, L. Luo, Y. Wang, C. Chen, J. Wang, and X. Wu · 2024
Closest in time.
Toolformer: Language models can teach themselves to use tools
T. Schick, J. Dwivedi-Yu, R. Dessì, R. Raileanu, M. Lomeli, E. Hambro, L. Zettlemoyer, N. Cancedda, and T. Scialom · 2024
Closest in time.
Large language models for data annotation: A survey
Z. Tan, A. Beigi, S. Wang, R. Guo, A. Bhattacharjee, B. Jiang, M. Karami, J. Li, L. Cheng, and H. Liu · 2024
Closest in time.
Tuning-free accountable intervention for llm deployment–a metacognitive approach
Z. Tan, J. Peng, T. Chen, and H. Liu · 2024
Closest in time.
Can llms learn from previous mistakes? investigating llms’ errors to boost for reasoning
Y. Tong, D. Li, S. Wang, Y. Wang, F. Teng, and J. Shang · 2024
Closest in time.
Automl-agent: A multi-agent llm framework for full-pipeline automl
P. Trirat, W. Jeong, and S. J. Hwang · 2024
Closest in time.
A survey on large language model based autonomous agents
L. Wang, C. Ma, X. Feng, Z. Zhang, H. Yang, J. Zhang, Z. Chen, J. Tang, X. Chen, Y. Lin, et al · 2024
Closest in time.
Large language model enhanced knowledge representation learning: A survey
X. Wang, Z. Chen, H. Wang, Z. Li, W. Guo, et al · 2024
Closest in time.
Opengraph: Towards open graph foundation models
L. Xia, B. Kao, and C. Huang · 2024
Closest in time.
Gpt4tools: Teaching large language model to use tools via self-instruction
R. Yang, L. Song, Y. Li, S. Zhao, Y. Ge, X. Li, and Y. Shan · 2024
Closest in time.