Built on

C.-Y. Lin, “ROUGE: A package for automatic evaluation of summaries,” in Text Summarization Branches Out . Barcelona, Spain: Association for Computational Linguistics, Jul. 2004, pp. 74–81. [Online]. Available: https://www.aclweb.org/anthology/W04-1013
2004
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv:1412.6572 , 2014
Original
2014
Earlier work this paper cites.
Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey et al. , “Google’s neural machine translation system: Bridging the gap between human and machine translation,” arXiv:1609.08144 , 2016
Original
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
V. Zhong, C. Xiong, and R. Socher, “Seq2sql: Generating structured queries from natural language using reinforcement learning,” CoRR , vol. abs/1709.00103, 2017
Original
2017
Earlier work this paper cites.
M. Joshi, E. Choi, D. Weld, and L. Zettlemoyer, “TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Vancouver, Canada: Association for Computational Linguistics, Jul. 2017, pp. 1601–1611. [Online]. Available: https://aclanthology.org/P17-1147
2017
Earlier work this paper cites.
A. Fan, M. Lewis, and Y. Dauphin, “Hierarchical neural story generation,” arXiv:1805.04833 , 2018
Original
2018
Earlier work this paper cites.
J. Howard and S. Ruder, “Universal language model fine-tuning for text classification,” arXiv:1801.06146 , 2018
Original
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
T. Yu, R. Zhang, K. Yang, M. Yasunaga, D. Wang, Z. Li, J. Ma, I. Li, Q. Yao, S. Roman et al. , “Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task,” arXiv:1809.08887 , 2018
Original
2018
Earlier work this paper cites.
X. V. Lin, C. Wang, L. Zettlemoyer, and M. D. Ernst, “NL2Bash: A corpus and semantic parser for natural language interface to the linux operating system,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) . Miyazaki, Japan: European Language Resources Association (ELRA), May 2018. [Online]. Available: https://aclanthology.org/L18-1491
2018
Earlier work this paper cites.
A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi, “The curious case of neural text degeneration,” arXiv:1904.09751 , 2019
Original
2019
Earlier work this paper cites.
B. Gliwa, I. Mochol, M. Biesek, and A. Wawer, “SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization,” in Proceedings of the 2nd Workshop on New Frontiers in Summarization . Hong Kong, China: Association for Computational Linguistics, Nov. 2019, pp. 70–79. [Online]. Available: https://www.aclweb.org/anthology/D19-5409
2019
Earlier work this paper cites.
Y. Bisk, R. Zellers, J. Gao, Y. Choi et al. , “Piqa: Reasoning about physical commonsense in natural language,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 05, 2020, pp. 7432–7439
2020
Earlier work this paper cites.
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language understanding,” arXiv:2009.03300 , 2020
Original
2020
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” arXiv:2106.09685 , 2021
Original
2021
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M. Agarwal, T. Chakraborti, Q. Fu, D. Gros, X. V. Lin, J. Maene, K. Talamadupula, Z. Teng, and J. White, “Neurips 2020 nlc2cmd competition: Translating natural language to bash commands,” in NeurIPS 2020 Competition and Demonstration Track . PMLR, 2021, pp. 302–324
2021
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K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano et al. , “Training verifiers to solve math word problems,” arXiv:2110.14168 , 2021
Original
2021
Earlier work this paper cites.
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman et al. , “Evaluating large language models trained on code,” arXiv:2107.03374 , 2021
Original
2021
Earlier work this paper cites.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in neural information processing systems , vol. 35, pp. 27 730–27 744, 2022
2022
Earlier work this paper cites.
C. Burns, H. Ye, D. Klein, and J. Steinhardt, “Discovering latent knowledge in language models without supervision,” arXiv:2212.03827 , 2022
Original
2022
Earlier work this paper cites.
P. Gupta, C. Jiao, Y.-T. Yeh, S. Mehri, M. Eskenazi, and J. P. Bigham, “Instructdial: Improving zero and few-shot generalization in dialogue through instruction tuning,” arXiv:2205.12673 , 2022
Original
2022
Earlier work this paper cites.
F. Shi, D. Fried, M. Ghazvininejad, L. Zettlemoyer, and S. I. Wang, “Natural language to code translation with execution,” arXiv:2204.11454 , 2022
Original
2022
Earlier work this paper cites.

Similar

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X. Qi, A. Panda, K. Lyu, X. Ma, S. Roy, A. Beirami, P. Mittal, and P. Henderson, “Safety alignment should be made more than just a few tokens deep,” arXiv:2406.05946 , 2024
Original
2024
Later among the works it cites.
K. Lyu, H. Zhao, X. Gu, D. Yu, A. Goyal, and S. Arora, “Keeping llms aligned after fine-tuning: The crucial role of prompt templates,” arXiv:2402.18540 , 2024
Original
2024
Later among the works it cites.
J. Wang, J. Li, Y. Li, X. Qi, M. Chen, J. Hu, Y. Li, B. Li, and C. Xiao, “Mitigating fine-tuning jailbreak attack with backdoor enhanced alignment,” arXiv:2402.14968 , 2024
Original
2024
Later among the works it cites.
C.-Y. Hsu, Y.-L. Tsai, C.-H. Lin, P.-Y. Chen, C.-M. Yu, and C.-Y. Huang, “Safe lora: the silver lining of reducing safety risks when fine-tuning large language models,” arXiv:2405.16833 , 2024
Original
2024
Later among the works it cites.

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Reading the bibliography…

2025

Alleviating the Fear of Losing Alignment in LLM Fine-tuning

Yang, Kang, Tao, Guanhong, Chen, Xun et al.

Understand

Large language models (LLMs) have demonstrated revolutionary capabilities in understanding complex contexts and performing a wide range of tasks.

However, LLMs can also answer questions that are unethical or harmful, raising concerns about their applications.
To regulate LLMs' responses to such questions, a training strategy called \textit{alignment} can help.
Yet, alignment can be unexpectedly compromised when fine-tuning an LLM for downstream tasks.

Built on

C.-Y. Lin, “ROUGE: A package for automatic evaluation of summaries,” in Text Summarization Branches Out . Barcelona, Spain: Association for Computational Linguistics, Jul. 2004, pp. 74–81. [Online]. Available: https://www.aclweb.org/anthology/W04-1013
2004
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” arXiv:1412.6572 , 2014
Original
2014
Earlier work this paper cites.
Y. Wu, M. Schuster, Z. Chen, Q. V. Le, M. Norouzi, W. Macherey, M. Krikun, Y. Cao, Q. Gao, K. Macherey et al. , “Google’s neural machine translation system: Bridging the gap between human and machine translation,” arXiv:1609.08144 , 2016
Original
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
V. Zhong, C. Xiong, and R. Socher, “Seq2sql: Generating structured queries from natural language using reinforcement learning,” CoRR , vol. abs/1709.00103, 2017
Original
2017
Earlier work this paper cites.
M. Joshi, E. Choi, D. Weld, and L. Zettlemoyer, “TriviaQA: A large scale distantly supervised challenge dataset for reading comprehension,” in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) . Vancouver, Canada: Association for Computational Linguistics, Jul. 2017, pp. 1601–1611. [Online]. Available: https://aclanthology.org/P17-1147
2017
Earlier work this paper cites.
A. Fan, M. Lewis, and Y. Dauphin, “Hierarchical neural story generation,” arXiv:1805.04833 , 2018
Original
2018
Earlier work this paper cites.
J. Howard and S. Ruder, “Universal language model fine-tuning for text classification,” arXiv:1801.06146 , 2018
Original
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
T. Yu, R. Zhang, K. Yang, M. Yasunaga, D. Wang, Z. Li, J. Ma, I. Li, Q. Yao, S. Roman et al. , “Spider: A large-scale human-labeled dataset for complex and cross-domain semantic parsing and text-to-sql task,” arXiv:1809.08887 , 2018
Original
2018
Earlier work this paper cites.
X. V. Lin, C. Wang, L. Zettlemoyer, and M. D. Ernst, “NL2Bash: A corpus and semantic parser for natural language interface to the linux operating system,” in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) . Miyazaki, Japan: European Language Resources Association (ELRA), May 2018. [Online]. Available: https://aclanthology.org/L18-1491
2018
Earlier work this paper cites.
A. Holtzman, J. Buys, L. Du, M. Forbes, and Y. Choi, “The curious case of neural text degeneration,” arXiv:1904.09751 , 2019
Original
2019
Earlier work this paper cites.
B. Gliwa, I. Mochol, M. Biesek, and A. Wawer, “SAMSum corpus: A human-annotated dialogue dataset for abstractive summarization,” in Proceedings of the 2nd Workshop on New Frontiers in Summarization . Hong Kong, China: Association for Computational Linguistics, Nov. 2019, pp. 70–79. [Online]. Available: https://www.aclweb.org/anthology/D19-5409
2019
Earlier work this paper cites.
Y. Bisk, R. Zellers, J. Gao, Y. Choi et al. , “Piqa: Reasoning about physical commonsense in natural language,” in Proceedings of the AAAI conference on artificial intelligence , vol. 34, no. 05, 2020, pp. 7432–7439
2020
Earlier work this paper cites.
D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language understanding,” arXiv:2009.03300 , 2020
Original
2020
Earlier work this paper cites.
E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” arXiv:2106.09685 , 2021
Original
2021
Earlier work this paper cites.
M. Agarwal, T. Chakraborti, Q. Fu, D. Gros, X. V. Lin, J. Maene, K. Talamadupula, Z. Teng, and J. White, “Neurips 2020 nlc2cmd competition: Translating natural language to bash commands,” in NeurIPS 2020 Competition and Demonstration Track . PMLR, 2021, pp. 302–324
2021
Earlier work this paper cites.
K. Cobbe, V. Kosaraju, M. Bavarian, M. Chen, H. Jun, L. Kaiser, M. Plappert, J. Tworek, J. Hilton, R. Nakano et al. , “Training verifiers to solve math word problems,” arXiv:2110.14168 , 2021
Original
2021
Earlier work this paper cites.
M. Chen, J. Tworek, H. Jun, Q. Yuan, H. P. d. O. Pinto, J. Kaplan, H. Edwards, Y. Burda, N. Joseph, G. Brockman et al. , “Evaluating large language models trained on code,” arXiv:2107.03374 , 2021
Original
2021
Earlier work this paper cites.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray et al. , “Training language models to follow instructions with human feedback,” Advances in neural information processing systems , vol. 35, pp. 27 730–27 744, 2022
2022
Earlier work this paper cites.
C. Burns, H. Ye, D. Klein, and J. Steinhardt, “Discovering latent knowledge in language models without supervision,” arXiv:2212.03827 , 2022
Original
2022
Earlier work this paper cites.
P. Gupta, C. Jiao, Y.-T. Yeh, S. Mehri, M. Eskenazi, and J. P. Bigham, “Instructdial: Improving zero and few-shot generalization in dialogue through instruction tuning,” arXiv:2205.12673 , 2022
Original
2022
Earlier work this paper cites.
F. Shi, D. Fried, M. Ghazvininejad, L. Zettlemoyer, and S. I. Wang, “Natural language to code translation with execution,” arXiv:2204.11454 , 2022
Original
2022
Earlier work this paper cites.

Similar

Then

X. Qi, A. Panda, K. Lyu, X. Ma, S. Roy, A. Beirami, P. Mittal, and P. Henderson, “Safety alignment should be made more than just a few tokens deep,” arXiv:2406.05946 , 2024
Original
2024
Later among the works it cites.
K. Lyu, H. Zhao, X. Gu, D. Yu, A. Goyal, and S. Arora, “Keeping llms aligned after fine-tuning: The crucial role of prompt templates,” arXiv:2402.18540 , 2024
Original
2024
Later among the works it cites.
J. Wang, J. Li, Y. Li, X. Qi, M. Chen, J. Hu, Y. Li, B. Li, and C. Xiao, “Mitigating fine-tuning jailbreak attack with backdoor enhanced alignment,” arXiv:2402.14968 , 2024
Original
2024
Later among the works it cites.
C.-Y. Hsu, Y.-L. Tsai, C.-H. Lin, P.-Y. Chen, C.-M. Yu, and C.-Y. Huang, “Safe lora: the silver lining of reducing safety risks when fine-tuning large language models,” arXiv:2405.16833 , 2024
Original
2024
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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