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We examine the ability of large language models (LLMs) to generate salient (interesting) negative statements about real-world entities; an emerging research topic of the last few years.
Wikidata: a free collaborative knowledge base,
D. Vrandečić, M. Krötzsch, · 2014
Earlier work this paper cites.
Commonsense properties from query logs and question answering forums,
J. Romero, S. Razniewski, K. Pal, J. Z. Pan, A. Sakhadeo, G. Weikum, · 2019
Earlier work this paper cites.
Mining an ”anti-knowledge base” from Wikipedia updates with applications to fact checking and beyond,
G. Karagiannis, I. Trummer, S. Jo, S. Khandelwal, X. Wang, C. Yu, · 2019
Earlier work this paper cites.
Language models as knowledge bases?,
F. Petroni, T. Rocktäschel, S. Riedel, P. Lewis, A. Bakhtin, Y. Wu, A. Miller, · 2019
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding,
J. Devlin, M.-W. Chang, K. Lee, K. Toutanova, · 2019
Earlier work this paper cites.
Language Models are Unsupervised Multitask Learners,
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, · 2019
Earlier work this paper cites.
Enriching knowledge bases with interesting negative statements,
H. Arnaout, S. Razniewski, G. Weikum, · 2020
Cited alongside, same era.
Language models as fact checkers?,
N. Lee, B. Z. Li, S. Wang, W.-t. Yih, H. Ma, M. Khabsa, · 2020
Cited alongside, same era.
Negated and misprimed probes for pretrained language models: Birds can talk, but cannot fly,
N. Kassner, H. Schütze, · 2020
Cited alongside, same era.
NegatER: Unsupervised Discovery of Negatives in Commonsense Knowledge Bases,
T. Safavi, J. Zhu, D. Koutra, · 2021
Cited alongside, same era.
UnCommonSense: Informative negative knowledge about everyday concepts,
H. Arnaout, S. Razniewski, G. Weikum, J. Z. Pan, · 2022
Cited alongside, same era.
OpenAI, Introducing chatgpt, https://openai.com/blog/chatgpt , 2022
2022
Cited alongside, same era.
Refined commonsense knowledge from large-scale web contents,
T. Nguyen, S. Razniewski, J. Romero, G. Weikum, · 2022
Later among the works it cites.
Completeness, recall, and negation in open-world knowledge bases: A survey,
S. Razniewski, H. Arnaout, S. Ghosh, F. Suchanek, · 2023
Closest in time.
Say what you mean! large language models speak too positively about negative commonsense knowledge,
J. Chen, W. Shi, Z. Fu, S. Cheng, L. Li, Y. Xiao, · 2023
Closest in time.
R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, T. B. Hashimoto, Alpaca: A strong, replicable instruction-following model, https://crfm.stanford.edu/2023/03/13/alpaca.html , 2023
2023
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Can large language models truly understand prompts? a case study with negated prompts,
J. Jang, S. Ye, M. Seo, · 2023
Closest in time.
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