Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have gained popularity in various fields for their exceptional capability of generating human-like text.
A value for n-person games
L. S. Shapley et al · 1953
Earlier work this paper cites.
Principles of mixed-initiative user interfaces
E. Horvitz · 1999
Earlier work this paper cites.
Mixed-initiative visual analytics using task-driven recommendations
K. Cook, N. Cramer, D. Israel, M. Wolverton, J. Bruce, R. Burtner, and A. Endert · 2015
Earlier work this paper cites.
Toward theoretical techniques for measuring the use of human effort in visual analytic systems
R. J. Crouser, L. Franklin, A. Endert, and K. Cook · 2016
Earlier work this paper cites.
"why should i trust you?": Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Earlier work this paper cites.
Comparing visual-interactive labeling with active learning: An experimental study
J. Bernard, M. Hutter, M. Zeppelzauer, D. Fellner, and M. Sedlmair · 2017
Earlier work this paper cites.
Deep reinforcement learning from human preferences
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei · 2017
Earlier work this paper cites.
Analysis of various decision tree algorithms for classification in data mining
B. Gupta, A. Rawat, A. Jain, A. Arora, and N. Dhami · 2017
Earlier work this paper cites.
A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
Earlier work this paper cites.
Identifying computer-generated text using statistical analysis
H.-Q. Nguyen-Son, N.-D. T. Tieu, H. H. Nguyen, J. Yamagishi, and I. E. Zen · 2017
Earlier work this paper cites.
Podium: Ranking data using mixed-initiative visual analytics
E. Wall, S. Das, R. Chawla, B. Kalidindi, E. T. Brown, and A. Endert · 2017
Earlier work this paper cites.
Peeking inside the black-box: A survey on explainable artificial intelligence (xai)
A. Adadi and M. Berrada · 2018
Earlier work this paper cites.
Vial: a unified process for visual interactive labeling
J. Bernard, M. Zeppelzauer, M. Sedlmair, and W. Aigner · 2018
Earlier work this paper cites.
Regressionexplorer: Interactive exploration of logistic regression models with subgroup analysis
D. Dingen, M. van’t Veer, P. Houthuizen, E. H. J. Mestrom, E. H. Korsten, A. R. Bouwman, and J. van Wijk · 2018
Earlier work this paper cites.
Explainable artificial intelligence: A survey
F. K. Došilović, M. Brčić, and N. Hlupić · 2018
Earlier work this paper cites.
The exploratory labeling assistant: Mixed-initiative label curation with large document collections
C. Felix, A. Dasgupta, and E. Bertini · 2018
Earlier work this paper cites.
Umap: Uniform manifold approximation and projection for dimension reduction
L. McInnes, J. Healy, and J. Melville · 2018
Earlier work this paper cites.
Anchors: High-precision model-agnostic explanations
M. T. Ribeiro, S. Singh, and C. Guestrin · 2018
Earlier work this paper cites.
Real or fake? learning to discriminate machine from human generated text
A. Bakhtin, S. Gross, M. Ott, Y. Deng, M. Ranzato, and A. Szlam · 2019
Earlier work this paper cites.
SciBERT: A pretrained language model for scientific text
I. Beltagy, K. Lo, and A. Cohan · 2019
Earlier work this paper cites.
Fairvis: Visual analytics for discovering intersectional bias in machine learning
Á. A. Cabrera, W. Epperson, F. Hohman, M. Kahng, J. Morgenstern, and D. H. Chau · 2019
Earlier work this paper cites.
Machine learning interpretability: A survey on methods and metrics
D. V. Carvalho, E. M. Pereira, and J. S. Cardoso · 2019
Earlier work this paper cites.
Aila: Attentive interactive labeling assistant for document classification through attention-based deep neural networks
M. Choi, C. Park, S. Yang, Y. Kim, J. Choo, and S. R. Hong · 2019
Earlier work this paper cites.
Techniques for interpretable machine learning
M. Du, N. Liu, and X. Hu · 2019
Earlier work this paper cites.
A scalable decision-tree-based method to explain interactions in dyadic data
C. Eiras-Franco, B. Guijarro-Berdiñas, A. Alonso-Betanzos, and A. Bahamonde · 2019
Earlier work this paper cites.
Gltr: Statistical detection and visualization of generated text
S. Gehrmann, H. Strobelt, and A. M. Rush · 2019
Cited alongside, same era.
The curious case of neural text degeneration
A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi · 2019
Cited alongside, same era.
Evolving rule-based explainable artificial intelligence for unmanned aerial vehicles
B. M. Keneni, D. Kaur, A. Al Bataineh, V. K. Devabhaktuni, A. Y. Javaid, J. D. Zaientz, and R. P. Marinier · 2019
Cited alongside, same era.
Fake news detection on social media using geometric deep learning
F. Monti, F. Frasca, D. Eynard, D. Mannion, and M. M. Bronstein · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al · 2019
Cited alongside, same era.
The datascope: A mixed-initiative architecture for data labeling
A. Alsaid and J. D. Lee · 2022
Later among the works it cites.
Strategyatlas: Strategy analysis for machine learning interpretability
D. Collaris and J. Van Wijk · 2022
Later among the works it cites.
Machine generated text: A comprehensive survey of threat models and detection methods
E. Crothers, N. Japkowicz, and H. Viktor · 2022
Later among the works it cites.
Adversarial robustness of neural-statistical features in detection of generative transformers
E. Crothers, N. Japkowicz, H. Viktor, and P. Branco · 2022
Later among the works it cites.
Is gpt-3 text indistinguishable from human text? scarecrow: A framework for scrutinizing machine text
Y. Dou, M. Forbes, R. Koncel-Kedziorski, N. A. Smith, and Y. Choi · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
I. Solaiman, M. Brundage, J. Clark, A. Askell, A. Herbert-Voss, J. Wu, A. Radford, G. Krueger, J. W. Kim, S. Kreps, et al · 2019
Cited alongside, same era.
Defending against neural fake news
R. Zellers, A. Holtzman, H. Rashkin, Y. Bisk, A. Farhadi, F. Roesner, and Y. Choi · 2019
Cited alongside, same era.
Dece: Decision explorer with counterfactual explanations for machine learning models
F. Cheng, Y. Ming, and H. Qu · 2020
Cited alongside, same era.
A survey on ensemble learning
X. Dong, Z. Yu, W. Cao, Y. Shi, and Q. Ma · 2020
Cited alongside, same era.
Automatic detection of generated text is easiest when humans are fooled
D. Ippolito, D. Duckworth, C. Callison-Burch, and D. Eck · 2020
Cited alongside, same era.
Automatic detection of machine generated text: A critical survey
G. Jawahar, M. Abdul-Mageed, and L. V. Lakshmanan · 2020
Cited alongside, same era.
Interpretable machine learning
C. Molnar · 2020
Cited alongside, same era.
Human-centered explainable ai (hcxai): Beyond opening the black-box of ai
U. Ehsan, P. Wintersberger, Q. V. Liao, E. A. Watkins, C. Manger, H. Daumé III, A. Riener, and M. O. Riedl · 2022
Later among the works it cites.
The development of language
J. B. Gleason and N. B. Ratner · 2022
Later among the works it cites.
Detecting and understanding textual deepfakes in online reviews
P. Kowalczyk, M. Röder, A. Dürr, and F. Thiesse · 2022
Later among the works it cites.
The threat of offensive ai to organizations
Y. Mirsky, A. Demontis, J. Kotak, R. Shankar, D. Gelei, L. Yang, X. Zhang, M. Pintor, W. Lee, Y. Elovici, and B. Biggio · 2022
Later among the works it cites.
Threat scenarios and best practices to detect neural fake news
A. Pagnoni, M. Graciarena, and Y. Tsvetkov · 2022
Later among the works it cites.
A typology of guidance tasks in mixed-initiative visual analytics environments
I. Pérez-Messina, D. Ceneda, M. El-Assady, S. Miksch, and F. Sperrle · 2022
Later among the works it cites.
Cross-domain detection of gpt-2-generated technical text
J. Rodriguez, T. Hay, D. Gros, Z. Shamsi, and R. Srinivasan · 2022
Later among the works it cites.
Detecting computer-generated disinformation
H. Stiff and F. Johansson · 2022
Later among the works it cites.
Interactive and visual prompt engineering for ad-hoc task adaptation with large language models
H. Strobelt, A. Webson, V. Sanh, B. Hoover, J. Beyer, H. Pfister, and A. M. Rush · 2022
Later among the works it cites.
Acl 2023 policy on ai writing assistance
ACL · 2023
Closest in time.
Real or fake text? investigating human ability to detect boundaries between human-written and machine-generated text
L. Dugan, D. Ippolito, A. Kirubarajan, S. Shi, C. Callison-Burch, P. Zhou, A. Zhu, J. Hu, J. Pujara, X. Ren, et al · 2023
Closest in time.
Grammarly
Grammarly · 2023
Closest in time.
How close is chatgpt to human experts? comparison corpus, evaluation, and detection
B. Guo, X. Zhang, Z. Wang, M. Jiang, J. Nie, Y. Ding, J. Yue, and Y. Wu · 2023
Closest in time.
Clarification on large language model policy llm
ICML · 2023
Closest in time.
A watermark for large language models
J. Kirchenbauer, J. Geiping, Y. Wen, J. Katz, I. Miers, and T. Goldstein · 2023
Closest in time.
Y. Ma, J. Liu, and F. Yi · 2023
Closest in time.
Detectgpt: Zero-shot machine-generated text detection using probability curvature
E. Mitchell, Y. Lee, A. Khazatsky, C. D. Manning, and C. Finn · 2023
Closest in time.
S. Mitrović, D. Andreoletti, and O. Ayoub · 2023
Closest in time.
Deepfake text detection: Limitations and opportunities
J. Pu, Z. Sarwar, S. M. Abdullah, A. Rehman, Y. Kim, P. Bhattacharya, M. Javed, and B. Viswanath · 2023
Closest in time.