Built on

Kohonen, T.: Correlation matrix memories. IEEE Transactions on Computers C-21
1972
Earlier work this paper cites.
Roemmele, M., Bejan, C.A., Gordon, A.S.: Choice of plausible alternatives: An evaluation of commonsense causal reasoning. In: 2011 AAAI Spring Symposium Series (2011)
2011
Earlier work this paper cites.
Bolukbasi, T., Chang, K.W., Zou, J.Y., Saligrama, V., Kalai, A.T.: Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Advances in neural information processing systems 29
2016
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Paperno, D., Kruszewski, G., Lazaridou, A., Pham, Q.N., Bernardi, R., Pezzelle, S., Baroni, M., Boleda, G., Fernández, R.: The lambada dataset: Word prediction requiring a broad discourse context. arXiv preprint arXiv:1606.06031 (2016)
Original
2016
Earlier work this paper cites.
Caliskan, A., Bryson, J.J., Narayanan, A.: Semantics derived automatically from language corpora contain human-like biases. Science 356
2017
Earlier work this paper cites.
Garg, N., Schiebinger, L., Jurafsky, D., Zou, J.: Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences 115
2018
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Rudinger, R., Naradowsky, J., Leonard, B., Van Durme, B.: Gender bias in coreference resolution. arXiv preprint arXiv:1804.09301 (2018)
Original
2018
Earlier work this paper cites.
Talmor, A., Herzig, J., Lourie, N., Berant, J.: Commonsenseqa: A question answering challenge targeting commonsense knowledge. arXiv preprint arXiv:1811.00937 (2018)
Original
2018
Earlier work this paper cites.
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: Glue: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461 (2018)
Original
2018
Earlier work this paper cites.
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: Gender bias in coreference resolution: Evaluation and debiasing methods. arXiv preprint arXiv:1804.06876 (2018)
Original
2018
Earlier work this paper cites.
May, C., Wang, A., Bordia, S., Bowman, S.R., Rudinger, R.: On measuring social biases in sentence encoders. arXiv preprint arXiv:1903.10561 (2019)
Original
2019
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Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2019), https://api.semanticscholar.org/CorpusID:160025533
2019
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Sap, M., Rashkin, H., Chen, D., LeBras, R., Choi, Y.: Socialiqa: Commonsense reasoning about social interactions. arXiv preprint arXiv:1904.09728 (2019)
Original
2019
Earlier work this paper cites.

Similar

Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.W., Wang, W.Y.: Mitigating gender bias in natural language processing: Literature review. arXiv preprint arXiv:1906.08976 (2019)
Original
2019
Cited alongside, same era.
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., Choi, Y.: Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830 (2019)
Original
2019
Cited alongside, same era.
Ziegler, D.M., Stiennon, N., Wu, J., Brown, T.B., Radford, A., Amodei, D., Christiano, P., Irving, G.: Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593 (2019)
Original
2019
Cited alongside, same era.
Zmigrod, R., Mielke, S.J., Wallach, H., Cotterell, R.: Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 1651–1661 (2019)
2019

Then

Meng, K., Sharma, A.S., Andonian, A., Belinkov, Y., Bau, D.: Mass-editing memory in a transformer. arXiv preprint arXiv:2210.07229 (2022)
Original
2022
Later among the works it cites.
Ramamurthy, R., Ammanabrolu, P., Brantley, K., Hessel, J., Sifa, R., Bauckhage, C., Hajishirzi, H., Choi, Y.: Is reinforcement learning (not) for natural language processing?: Benchmarks, baselines, and building blocks for natural language policy optimization. arXiv preprint arXiv:2210.01241 (2022)
Original
2022
Later among the works it cites.
Cheng, S., Tian, B., Liu, Q., Chen, X., Wang, Y., Chen, H., Zhang, N.: Can we edit multimodal large language models? arXiv preprint arXiv:2310.08475 (2023)
Original
2023
Later among the works it cites.
Cheng, S., Zhang, N., Tian, B., Dai, Z., Xiong, F., Guo, W., Chen, H.: Editing language model-based knowledge graph embeddings. arXiv preprint arXiv:2301.10405 (2023)
Original
2023
Later among the works it cites.

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2024

Locating and Mitigating Gender Bias in Large Language Models

Cai, Yuchen, Cao, Ding, Guo, Rongxi et al.

Understand

Large language models(LLM) are pre-trained on extensive corpora to learn facts and human cognition which contain human preferences.

However, this process can inadvertently lead to these models acquiring biases and stereotypes prevalent in society.
Prior research has typically tackled the issue of bias through a one-dimensional perspective, concentrating either on locating or mitigating it.
This limited perspective has created obstacles in facilitating research on bias to synergistically complement and progressively build upon one another.

Built on

Kohonen, T.: Correlation matrix memories. IEEE Transactions on Computers C-21
1972
Earlier work this paper cites.
Roemmele, M., Bejan, C.A., Gordon, A.S.: Choice of plausible alternatives: An evaluation of commonsense causal reasoning. In: 2011 AAAI Spring Symposium Series (2011)
2011
Earlier work this paper cites.
Bolukbasi, T., Chang, K.W., Zou, J.Y., Saligrama, V., Kalai, A.T.: Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
Paperno, D., Kruszewski, G., Lazaridou, A., Pham, Q.N., Bernardi, R., Pezzelle, S., Baroni, M., Boleda, G., Fernández, R.: The lambada dataset: Word prediction requiring a broad discourse context. arXiv preprint arXiv:1606.06031 (2016)
Original
2016
Earlier work this paper cites.
Caliskan, A., Bryson, J.J., Narayanan, A.: Semantics derived automatically from language corpora contain human-like biases. Science 356
2017
Earlier work this paper cites.
Garg, N., Schiebinger, L., Jurafsky, D., Zou, J.: Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences 115
2018
Earlier work this paper cites.
Rudinger, R., Naradowsky, J., Leonard, B., Van Durme, B.: Gender bias in coreference resolution. arXiv preprint arXiv:1804.09301 (2018)
Original
2018
Earlier work this paper cites.
Talmor, A., Herzig, J., Lourie, N., Berant, J.: Commonsenseqa: A question answering challenge targeting commonsense knowledge. arXiv preprint arXiv:1811.00937 (2018)
Original
2018
Earlier work this paper cites.
Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.R.: Glue: A multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461 (2018)
Original
2018
Earlier work this paper cites.
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., Chang, K.W.: Gender bias in coreference resolution: Evaluation and debiasing methods. arXiv preprint arXiv:1804.06876 (2018)
Original
2018
Earlier work this paper cites.
May, C., Wang, A., Bordia, S., Bowman, S.R., Rudinger, R.: On measuring social biases in sentence encoders. arXiv preprint arXiv:1903.10561 (2019)
Original
2019
Earlier work this paper cites.
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I.: Language models are unsupervised multitask learners (2019), https://api.semanticscholar.org/CorpusID:160025533
2019
Earlier work this paper cites.
Sap, M., Rashkin, H., Chen, D., LeBras, R., Choi, Y.: Socialiqa: Commonsense reasoning about social interactions. arXiv preprint arXiv:1904.09728 (2019)
Original
2019
Earlier work this paper cites.

Similar

Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.W., Wang, W.Y.: Mitigating gender bias in natural language processing: Literature review. arXiv preprint arXiv:1906.08976 (2019)
Original
2019
Cited alongside, same era.
Zellers, R., Holtzman, A., Bisk, Y., Farhadi, A., Choi, Y.: Hellaswag: Can a machine really finish your sentence? arXiv preprint arXiv:1905.07830 (2019)
Original
2019
Cited alongside, same era.
Ziegler, D.M., Stiennon, N., Wu, J., Brown, T.B., Radford, A., Amodei, D., Christiano, P., Irving, G.: Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593 (2019)
Original
2019
Cited alongside, same era.
Zmigrod, R., Mielke, S.J., Wallach, H., Cotterell, R.: Counterfactual data augmentation for mitigating gender stereotypes in languages with rich morphology. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. pp. 1651–1661 (2019)
2019

Then

Meng, K., Sharma, A.S., Andonian, A., Belinkov, Y., Bau, D.: Mass-editing memory in a transformer. arXiv preprint arXiv:2210.07229 (2022)
Original
2022
Later among the works it cites.
Ramamurthy, R., Ammanabrolu, P., Brantley, K., Hessel, J., Sifa, R., Bauckhage, C., Hajishirzi, H., Choi, Y.: Is reinforcement learning (not) for natural language processing?: Benchmarks, baselines, and building blocks for natural language policy optimization. arXiv preprint arXiv:2210.01241 (2022)
Original
2022
Later among the works it cites.
Cheng, S., Tian, B., Liu, Q., Chen, X., Wang, Y., Chen, H., Zhang, N.: Can we edit multimodal large language models? arXiv preprint arXiv:2310.08475 (2023)
Original
2023
Later among the works it cites.
Cheng, S., Zhang, N., Tian, B., Dai, Z., Xiong, F., Guo, W., Chen, H.: Editing language model-based knowledge graph embeddings. arXiv preprint arXiv:2301.10405 (2023)
Original
2023
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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Cited alongside, same era.

Bisk, Y., Zellers, R., Gao, J., Choi, Y., et al.: Piqa: Reasoning about physical commonsense in natural language. In: Proceedings of the AAAI conference on artificial intelligence. vol. 34, pp. 7432–7439 (2020)

2020

Cited alongside, same era.

Vig, J., Gehrmann, S., Belinkov, Y., Qian, S., Nevo, D., Sakenis, S., Huang, J., Singer, Y., Shieber, S.: Causal mediation analysis for interpreting neural nlp: The case of gender bias. arXiv preprint arXiv:2004.12265 (2020)

Original

2020

Cited alongside, same era.

Webster, K., Wang, X., Tenney, I., Beutel, A., Pitler, E., Pavlick, E., Chen, J., Chi, E., Petrov, S.: Measuring and reducing gendered correlations in pre-trained models. arXiv preprint arXiv:2010.06032 (2020)

Original

2020

Cited alongside, same era.

Dai, D., Dong, L., Hao, Y., Sui, Z., Wei, F.: Knowledge neurons in pretrained transformers. ArXiv abs/2104.08696

Original

2021

Cited alongside, same era.

Wang, B., Komatsuzaki, A.: Gpt-j-6b: A 6 billion parameter autoregressive language model (2021)

2021

Cited alongside, same era.

Cohen, D., Ryu, M., Chow, Y., Keller, O., Greenberg, I., Hassidim, A., Fink, M., Matias, Y., Szpektor, I., Boutilier, C., et al.: Dynamic planning in open-ended dialogue using reinforcement learning. arXiv preprint arXiv:2208.02294 (2022)

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2022

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Geva, M., Caciularu, A., Wang, K., Goldberg, Y.: Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space. ArXiv abs/2203.14680

Original

2022

Cited alongside, same era.

Guo, Y., Yang, Y., Abbasi, A.: Auto-debias: Debiasing masked language models with automated biased prompts. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). pp. 1012–1023 (2022)

2022

Cited alongside, same era.

Choi, J.H., Hickman, K.E., Monahan, A., Schwarcz, D.: Chatgpt goes to law school. Available at SSRN (2023)

2023