Release Strategies and the Social Impacts of Language Models

Large language models have a range of beneficial uses: they can assist in prose, poetry, and programming; analyze dataset biases; and more. However, their flexibility and generative capabilities also raise misuse concerns. This report discusses OpenAI's work related to the release of its GPT-2 language model. It discusses staged release, which allows time between model releases to conduct risk and benefit analyses as model sizes increased. It also discusses ongoing partnership-based research and provides recommendations for better coordination and responsible publication in AI.

[1]  Quoc V. Le,et al.  Semi-supervised Sequence Learning , 2015, NIPS.

[2]  Dirk Hovy,et al.  The Social Impact of Natural Language Processing , 2016, ACL.

[3]  Perry R. Hinton Implicit stereotypes and the predictive brain: cognition and culture in “biased” person perception , 2017, Palgrave Communications.

[4]  R. Guilbeault No . 2017 . 5 Computational Propaganda in the United States of America : Manufacturing Consensus Online , 2017 .

[5]  Imran Awan Cyber-Extremism: Isis and the Power of Social Media , 2017, Society.

[6]  Arvind Narayanan,et al.  Semantics derived automatically from language corpora contain human-like biases , 2016, Science.

[7]  Sheng Yu,et al.  Generation of Synthetic Electronic Medical Record Text , 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM).

[8]  Sebastian Ruder,et al.  Universal Language Model Fine-tuning for Text Classification , 2018, ACL.

[9]  K. Cox,et al.  Social Media in Africa: A Double-Edged Sword for Security and Development , 2018 .

[10]  Emily M. Bender,et al.  Data Statements for Natural Language Processing: Toward Mitigating System Bias and Enabling Better Science , 2018, TACL.

[11]  Jess Whittlestone,et al.  The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions , 2019, AIES.

[12]  Peter Szolovits,et al.  Clinically Accurate Chest X-Ray Report Generation , 2019, MLHC.

[13]  Jason Weston,et al.  What makes a good conversation? How controllable attributes affect human judgments , 2019, NAACL.

[14]  Alexander M. Rush,et al.  GLTR: Statistical Detection and Visualization of Generated Text , 2019, ACL.

[15]  Jayadev Bhaskaran,et al.  Good Secretaries, Bad Truck Drivers? Occupational Gender Stereotypes in Sentiment Analysis , 2019, Proceedings of the First Workshop on Gender Bias in Natural Language Processing.

[16]  Ilya Sutskever,et al.  Language Models are Unsupervised Multitask Learners , 2019 .

[17]  Dimitrios Alikaniotis,et al.  The Unreasonable Effectiveness of Transformer Language Models in Grammatical Error Correction , 2019, BEA@ACL.

[18]  Ivan Vulić,et al.  Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems , 2019, EMNLP.

[19]  Ali Farhadi,et al.  Defending Against Neural Fake News , 2019, NeurIPS.

[20]  Natalia Criado,et al.  Attesting Biases and Discrimination using Language Semantics , 2019, ArXiv.

[21]  Kyle Lo,et al.  SciBERT: Pretrained Contextualized Embeddings for Scientific Text , 2019, ArXiv.

[22]  Erik T. Mueller,et al.  Multi-turn Dialogue Response Generation with Autoregressive Transformer Models , 2019, ArXiv.

[23]  Cao Xiao,et al.  EEGtoText: Learning to Write Medical Reports from EEG Recordings , 2019, MLHC.

[24]  Filippo Menczer,et al.  Arming the public with artificial intelligence to counter social bots , 2019, Human Behavior and Emerging Technologies.

[25]  Jess Whittlestone,et al.  Reducing malicious use of synthetic media research: Considerations and potential release practices for machine learning , 2019, ArXiv.

[26]  Marc'Aurelio Ranzato,et al.  Real or Fake? Learning to Discriminate Machine from Human Generated Text , 2019, ArXiv.

[27]  Junichi Yamagishi,et al.  Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection , 2019, AINA.

[28]  Rik van Noord,et al.  Fair Is Better than Sensational: Man Is to Doctor as Woman Is to Doctor , 2019, CL.

[29]  Yejin Choi,et al.  The Curious Case of Neural Text Degeneration , 2019, ICLR.

[30]  Iyad Rahwan,et al.  Human detection of machine-manipulated media , 2019, Commun. ACM.

[31]  Mark Amerika Talk to Transformer , 2021 .