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Recent advancements in Large Language Models (LLMs) have demonstrated exceptional capabilities in complex tasks like machine translation, commonsense reasoning, and language understanding.
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“Knowledge discovery on RFM model using Bernoulli sequence”
I-Cheng Yeh, King-Jang Yang and Tao-Ming Ting · 2009
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Matthew Zeiler and Rob Fergus · 2014
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“Sentiment Labelled Sentences”, UCI Machine Learning Repository, 2015
Dimitrios Kotzias · 2015
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““Why should I trust you?” Explaining the predictions of any classifier”
Marco Ribeiro, Sameer Singh and Carlos Guestrin · 2016
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“Towards a rigorous science of interpretable machine learning”
Finale Doshi-Velez and Been Kim · 2017
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“A Unified Approach to Interpreting Model Predictions”
Scott Lundberg and Su-In Lee · 2017
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“Smoothgrad: Removing noise by adding noise”
Daniel Smilkov et al · 2017
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“Axiomatic Attribution for Deep Networks”
Mukund Sundararajan, Ankur Taly and Qiqi Yan · 2017
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“Learning to Generate Reviews and Discovering Sentiment”, 2017
Alec Radford, Rafal Jozefowicz and Ilya Sutskever · 2017
Cited alongside, same era.
“Attention is all you need”
Ashish Vaswani et al · 2017
Cited alongside, same era.
“Learning important features through propagating activation differences”
Avanti Shrikumar, Peyton Greenside and Anshul Kundaje · 2017
Cited alongside, same era.
“How Important Is a Neuron?” arXiv:1805.12233 [cs, stat]
Kedar Dhamdhere, Mukund Sundararajan and Qiqi Yan · 2018
Cited alongside, same era.
“Publicly available clinical BERT embeddings”
Emily Alsentzer et al · 2019
Cited alongside, same era.
“Explainable Machine Learning Challenge”
“Emergent abilities of large language models”
Jason Wei et al · 2022
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“What learning algorithm is in-context learning? investigations with linear models”
Ekin Akyürek et al · 2022
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“The Disagreement Problem in Explainable Machine Learning: A Practitioner’s Perspective”
Satyapriya Krishna et al · 2022
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“Openxai: Towards a transparent evaluation of model explanations”
Chirag Agarwal et al · 2022
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“GenSLMs: Genome-scale language models reveal SARS-CoV-2 evolutionary dynamics”
Maxim Zvyagin et al · 2023
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FICO · 2019
Cited alongside, same era.
“Language models are few-shot learners”
Tom Brown et al · 2020
Cited alongside, same era.
“BioBERT: a pre-trained biomedical language representation model for biomedical text mining”
Jinhyuk Lee et al · 2020
Cited alongside, same era.
“Adult Income Dataset” Accessed: 2020-01-01, https://www.kaggle.com/wenruliu/adult-income-dataset
Kaggle · 2020
Cited alongside, same era.
“Default of Credit Card Clients Data Set” Accessed: 2020-01-01, https://archive.ics.uci.edu/ml/datasets/default+of+credit+card+clients
UCI · 2020
Cited alongside, same era.
Pengfei Liu et al · 2023
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“Tabllm: Few-shot classification of tabular data with large language models”
Stefan Hegselmann et al · 2023
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“CancerGPT for few shot drug pair synergy prediction using large pretrained language models”
Tianhao Li et al · 2024
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“How We Analyzed the COMPAS Recidivism Algorithm” Accessed: 2024-06-14, https://www.propublica.org/article/how-we-analyzed-the-compas-recidivism-algorithm
ProPublica · 2024
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