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Large language models and AI chatbots have been at the forefront of democratizing artificial intelligence.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al., 2020 · 1901
Earlier work this paper cites.
BERT: Pre-training of deep bidirectional transformers for language understanding, in: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), Association for Computational Linguistics, Minneapolis, Minnesota. pp. 4171–4186
Devlin, J., Chang, M.W., Lee, K., Toutanova, K., 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al., 2019 · 2019
Earlier work this paper cites.
Cyber hygiene: The concept, its measure, and its initial tests
Vishwanath, A., Neo, L.S., Goh, P., Lee, S., Khader, M., Ong, G., Chin, J., 2020 · 2020
Earlier work this paper cites.
On the dangers of stochastic parrots: Can language models be too big?, in: Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp. 610–623
Bender, E.M., Gebru, T., McMillan-Major, A., Shmitchell, S., 2021 · 2021
Earlier work this paper cites.
On the opportunities and risks of foundation models
Bommasani, R., Hudson, D.A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M.S., Bohg, J., Bosselut, A., Brunskill, E., et al., 2021 · 2021
Earlier work this paper cites.
Extracting training data from large language models., in: USENIX Security Symposium
Carlini, N., Tramer, F., Wallace, E., Jagielski, M., Herbert-Voss, A., Lee, K., Roberts, A., Brown, T.B., Song, D., Erlingsson, U., et al., 2021 · 2021
Earlier work this paper cites.
The race to understand the exhilarating, dangerous world of language ai
Hao, K., 2021 · 2021
Earlier work this paper cites.
Towards understanding and mitigating social biases in language models, in: Meila, M., Zhang, T. (Eds.), Proceedings of the 38th International Conference on Machine Learning, PMLR. pp. 6565–6576
Liang, P.P., Wu, C., Morency, L.P., Salakhutdinov, R., 2021 · 2021
Earlier work this paper cites.
Zero-shot text-to-image generation, in: International Conference on Machine Learning, PMLR. pp. 8821–8831
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I., 2021 · 2021
Earlier work this paper cites.
Design guidelines for prompt engineering text-to-image generative models, in: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, pp. 1–23
Liu, V., Chilton, L.B., 2022 · 2022
Earlier work this paper cites.
Introducing chatgpt
OpenAI, 2022 · 2022
Earlier work this paper cites.
Ignore previous prompt: Attack techniques for language models
Perez, F., Ribeiro, I., 2022 · 2022
Earlier work this paper cites.
Declassifying the responsible disclosure of the prompt injection attack vulnerability of gpt-3
Preamble, 2022 · 2022
Cited alongside, same era.
Large pre-trained language models contain human-like biases of what is right and wrong to do
Schramowski, P., Turan, C., Andersen, N., Rothkopf, C.A., Kersting, K., 2022 · 2022
Cited alongside, same era.
Lamda: Language models for dialog applications
Thoppilan, R., De Freitas, D., Hall, J., Shazeer, N., Kulshreshtha, A., Cheng, H.T., Jin, A., Bos, T., Baker, L., Du, Y., et al., 2022 · 2022
Cited alongside, same era.
Prompt injection attacks against gpt-3
Willison, S., 2022 · 2022
Cited alongside, same era.
Sparks of artificial general intelligence: Early experiments with gpt-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y.T., Li, Y., Lundberg, S., et al., 2023 · 2023
Cited alongside, same era.
Use of llms for illicit purposes: Threats, prevention measures, and vulnerabilities
Mozes, M., He, X., Kleinberg, B., Griffin, L.D., 2023 · 2023
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I built a zero day virus with undetectable exfiltration using only chatgpt prompts
Mulgrew, A., 2023 · 2023
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OpenAI, 2023 · 2023
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An important next step on our ai journey
Pichai, S., 2023 · 2023
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Qiu, H., Zhang, S., Li, A., He, H., Lan, Z., 2023 · 2023
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The security hole at the heart of chatgpt and bing
Burgess, M., 2023 · 2023
Cited alongside, same era.
Ai-powered bing chat spills its secrets via prompt injection attack
Edwards, B., 2023 · 2023
Cited alongside, same era.
Greshake, K., Abdelnabi, S., Mishra, S., Endres, C., Holz, T., Fritz, M., 2023 · 2023
Cited alongside, same era.
From chatgpt to threatgpt: Impact of generative ai in cybersecurity and privacy
Gupta, M., Akiri, C., Aryal, K., Parker, E., Praharaj, L., 2023 · 2023
Cited alongside, same era.
Three ways ai chatbots are a security disaster
Heikkilä, M., 2023 · 2023
Cited alongside, same era.
Exploiting programmatic behavior of llms: Dual-use through standard security attacks
Kang, D., Li, X., Stoica, I., Guestrin, C., Zaharia, M., Hashimoto, T., 2023 · 2023
Cited alongside, same era.
What are large language models used for?
Lee, A., 2023 · 2023
Cited alongside, same era.
Shen, X., Chen, Z., Backes, M., Shen, Y., Zhang, Y., 2023 · 2023
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Llama: Open and efficient foundation language models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al., 2023 · 2023
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Meta’s powerful ai language model has leaked online — what happens now?
Vincent, J., 2023 · 2023
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Virtual prompt injection for instruction-tuned large language models
Yan, J., Yadav, V., Li, S., Chen, L., Tang, Z., Wang, H., Srinivasan, V., Ren, X., Jin, H., 2023 · 2023
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Is chatgpt fair for recommendation? evaluating fairness in large language model recommendation
Zhang, J., Bao, K., Zhang, Y., Wang, W., Feng, F., He, X., 2023 · 2023
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Exploring ai ethics of chatgpt: A diagnostic analysis
Zhuo, T.Y., Huang, Y., Chen, C., Xing, Z., 2023 · 2023
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Universal and transferable adversarial attacks on aligned language models
Zou, A., Wang, Z., Kolter, J.Z., Fredrikson, M., 2023 · 2023
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