Built on

T. Chen, B. Xu, C. Zhang, and C. Guestrin, “Training deep nets with sublinear memory cost,” arXiv preprint arXiv:1604.06174
Original
2016
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101
Original
2017
Earlier work this paper cites.
D. Dua, Y. Wang, P. Dasigi, G. Stanovsky, S. Singh, and M. Gardner, “Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs,” 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)
2019
Earlier work this paper cites.
G. Wenzek, M.-A. Lachaux, A. Conneau, V. Chaudhary, F. Guzmán, A. Joulin, and E. Grave, “Ccnet: Extracting high quality monolingual datasets from web crawl data,” arXiv preprint arXiv:1911.00359
Original
2019
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al
2020
Earlier work this paper cites.
J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei, “Scaling laws for neural language models,” arXiv preprint arXiv:2001.08361
Original
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research
2020
Earlier work this paper cites.
L. Gao, S. Biderman, S. Black, L. Golding, T. Hoppe, C. Foster, J. Phang, H. He, A. Thite, N. Nabeshima, et al
Original
2020
Earlier work this paper cites.
J. Rasley, S. Rajbhandari, O. Ruwase, and Y. He, “Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
2020
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
Original
2021
Earlier work this paper cites.

Similar

D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language understanding,” Proceedings of the International Conference on Learning Representations (ICLR)
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Cited alongside, same era.
D. Hendrycks, C. Burns, S. Basart, A. Critch, J. Li, D. Song, and J. Steinhardt, “Aligning ai with shared human values,” Proceedings of the International Conference on Learning Representations (ICLR)
2021
Cited alongside, same era.
K. Lee, D. Ippolito, A. Nystrom, C. Zhang, D. Eck, C. Callison-Burch, and N. Carlini, “Deduplicating training data makes language models better,” arXiv preprint arXiv:2107.06499
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2021
Cited alongside, same era.
A. Srivastava, A. Rastogi, A. Rao, A. A. M. Shoeb, A. Abid, A. Fisch, A. R. Brown, A. Santoro, A. Gupta, A. Garriga-Alonso, et al
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2022
Cited alongside, same era.

Then

OpenAI, “Gpt-4 technical report,” 2023
2023
Closest in time.
A. Gudibande, E. Wallace, C. Snell, X. Geng, H. Liu, P. Abbeel, S. Levine, and D. Song, “The false promise of imitating proprietary llms,” arXiv preprint arXiv:2305.15717
Original
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Closest in time.
Y. K. Chia, P. Hong, L. Bing, and S. Poria, “Instructeval: Towards holistic evaluation of instruction-tuned large language models,” arXiv preprint arXiv:2306.04757
Original
2023
Closest in time.
G. Penedo, Q. Malartic, D. Hesslow, R. Cojocaru, A. Cappelli, H. Alobeidli, B. Pannier, E. Almazrouei, and J. Launay, “The refinedweb dataset for falcon llm: Outperforming curated corpora with web data, and web data only,” arXiv preprint arXiv:2306.01116
Original
2023
Closest in time.
C. Zhou, P. Liu, P. Xu, S. Iyer, J. Sun, Y. Mao, X. Ma, A. Efrat, P. Yu, L. Yu, et al
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2023

The Poison of Alignment

Bekbayev, Aibek, Chun, Sungbae, Dulat, Yerzat et al.

Understand

From the perspective of content safety issues, alignment has shown to limit large language models' (LLMs) harmful content generation.

This intentional method of reinforcing models to not respond to certain user inputs seem to be present in many modern open-source instruction tuning datasets such as OpenAssistant or Guanaco.
We introduce a novel insight to an instruction-tuned model's performance affected by the presence of alignment in supervised fine-tuning dataset.
To be specific, we noticed that alignment acts as if it is poisoning the instruction dataset.

Built on

T. Chen, B. Xu, C. Zhang, and C. Guestrin, “Training deep nets with sublinear memory cost,” arXiv preprint arXiv:1604.06174
Original
2016
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” arXiv preprint arXiv:1711.05101
Original
2017
Earlier work this paper cites.
D. Dua, Y. Wang, P. Dasigi, G. Stanovsky, S. Singh, and M. Gardner, “Drop: A reading comprehension benchmark requiring discrete reasoning over paragraphs,” 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)
2019
Earlier work this paper cites.
G. Wenzek, M.-A. Lachaux, A. Conneau, V. Chaudhary, F. Guzmán, A. Joulin, and E. Grave, “Ccnet: Extracting high quality monolingual datasets from web crawl data,” arXiv preprint arXiv:1911.00359
Original
2019
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al
2020
Earlier work this paper cites.
J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei, “Scaling laws for neural language models,” arXiv preprint arXiv:2001.08361
Original
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research
2020
Earlier work this paper cites.
L. Gao, S. Biderman, S. Black, L. Golding, T. Hoppe, C. Foster, J. Phang, H. He, A. Thite, N. Nabeshima, et al
Original
2020
Earlier work this paper cites.
J. Rasley, S. Rajbhandari, O. Ruwase, and Y. He, “Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters,” in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
2020
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
Original
2021
Earlier work this paper cites.

Similar

D. Hendrycks, C. Burns, S. Basart, A. Zou, M. Mazeika, D. Song, and J. Steinhardt, “Measuring massive multitask language understanding,” Proceedings of the International Conference on Learning Representations (ICLR)
2021
Cited alongside, same era.
D. Hendrycks, C. Burns, S. Basart, A. Critch, J. Li, D. Song, and J. Steinhardt, “Aligning ai with shared human values,” Proceedings of the International Conference on Learning Representations (ICLR)
2021
Cited alongside, same era.
K. Lee, D. Ippolito, A. Nystrom, C. Zhang, D. Eck, C. Callison-Burch, and N. Carlini, “Deduplicating training data makes language models better,” arXiv preprint arXiv:2107.06499
Original
2021
Cited alongside, same era.
A. Srivastava, A. Rastogi, A. Rao, A. A. M. Shoeb, A. Abid, A. Fisch, A. R. Brown, A. Santoro, A. Gupta, A. Garriga-Alonso, et al
Original
2022
Cited alongside, same era.

Then

OpenAI, “Gpt-4 technical report,” 2023
2023
Closest in time.
A. Gudibande, E. Wallace, C. Snell, X. Geng, H. Liu, P. Abbeel, S. Levine, and D. Song, “The false promise of imitating proprietary llms,” arXiv preprint arXiv:2305.15717
Original
2023
Closest in time.
Y. K. Chia, P. Hong, L. Bing, and S. Poria, “Instructeval: Towards holistic evaluation of instruction-tuned large language models,” arXiv preprint arXiv:2306.04757
Original
2023
Closest in time.
G. Penedo, Q. Malartic, D. Hesslow, R. Cojocaru, A. Cappelli, H. Alobeidli, B. Pannier, E. Almazrouei, and J. Launay, “The refinedweb dataset for falcon llm: Outperforming curated corpora with web data, and web data only,” arXiv preprint arXiv:2306.01116
Original
2023
Closest in time.
C. Zhou, P. Liu, P. Xu, S. Iyer, J. Sun, Y. Mao, X. Ma, A. Efrat, P. Yu, L. Yu, et al
Original

Beyond the bibliography

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

Open on alphaXiv

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L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al

2022

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B. Lefaudeux, F. Massa, D. Liskovich, W. Xiong, V. Caggiano, S. Naren, M. Xu, J. Hu, M. Tintore, S. Zhang, P. Labatut, and D. Haziza, “xformers: A modular and hackable transformer modelling library.” https://github.com/facebookresearch/xformers , 2022

2022

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W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez, et al

2023

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R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto, “Stanford alpaca: An instruction-following llama model.” https://github.com/tatsu-lab/stanford_alpaca , 2023

2023

Cited alongside, same era.

S. Mukherjee, A. Mitra, G. Jawahar, S. Agarwal, H. Palangi, and A. Awadallah, “Orca: Progressive learning from complex explanation traces of GPT-4,” arXiv preprint arXiv:2306.02707

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2023

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2023

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H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, et al

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2023

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