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We introduce {\em generative monoculture}, a behavior observed in large language models (LLMs) characterized by a significant narrowing of model output diversity relative to available training data for a given task: for example, generating only positive book reviews for books with a mixed reception.
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Saul Schleimer, Daniel S Wilkerson, and Alex Aiken · 2003
Earlier work this paper cites.
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Topic modeling: beyond bag-of-words
Hanna M Wallach · 2006
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Recursive deep models for semantic compositionality over a sentiment treebank
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A survey of topic modeling in text mining
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The curious case of neural text degeneration
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Persistent anti-muslim bias in large language models
Abubakar Abid, Maheen Farooqi, and James Zou · 2021
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Algorithmic monoculture and social welfare
Jon Kleinberg and Manish Raghavan · 2021
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Extracting training data from large language models
Nicholas Carlini, Florian Tramer, Eric Wallace, Matthew Jagielski, Ariel Herbert-Voss, Katherine Lee, Adam Roberts, Tom Brown, Dawn Song, Ulfar Erlingsson, et al · 2021
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Emergent abilities of large language models
Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, et al · 2022
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Competition-level code generation with alphacode
Yujia Li, David Choi, Junyoung Chung, Nate Kushman, Julian Schrittwieser, Rémi Leblond, Tom Eccles, James Keeling, Felix Gimeno, Agustin Dal Lago, Thomas Hubert, Peter Choy, Cyprien de Masson d’Autume, Igor Babuschkin, Xinyun Chen, Po-Sen Huang, Johannes Welbl, Sven Gowal, Alexey Cherepanov, James Molloy, Daniel J. Mankowitz, Esme Sutherland Robson, Pushmeet Kohli, Nando de Freitas, Koray Kavukcuoglu, and Oriol Vinyals · 2022
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Asleep at the keyboard? assessing the security of github copilot’s code contributions
Hammond Pearce, Baleegh Ahmad, Benjamin Tan, Brendan Dolan-Gavitt, and Ramesh Karri · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Picking on the same person: Does algorithmic monoculture lead to outcome homogenization?
Rishi Bommasani, Kathleen A Creel, Ananya Kumar, Dan Jurafsky, and Percy S Liang · 2022
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Bertopic: Neural topic modeling with a class-based tf-idf procedure
Maarten Grootendorst · 2022
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Generating diverse code explanations using the gpt-3 large language model
Stephen MacNeil, Andrew Tran, Dan Mogil, Seth Bernstein, Erin Ross, and Ziheng Huang · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Holistic evaluation of language models
Rishi Bommasani, Percy Liang, and Tony Lee · 2023
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Decodingtrust: A comprehensive assessment of trustworthiness in gpt models
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, et al · 2023
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HaluEval: A large-scale hallucination evaluation benchmark for large language models
Junyi Li, Xiaoxue Cheng, Xin Zhao, Jian-Yun Nie, and Ji-Rong Wen · 2023
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Siren’s song in the ai ocean: a survey on hallucination in large language models
Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, et al · 2023
Hallucinating law: Legal mistakes with large language models are pervasive, Jan 2024
Matthew Dahl, Varun Magesh, Mirac Suzgun, and Daniel E. Ho · 2024
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The dark side of language models: Exploring the potential of llms in multimedia disinformation generation and dissemination
Dipto Barman, Ziyi Guo, and Owen Conlan · 2024
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Exploring the deceptive power of llm-generated fake news: A study of real-world detection challenges
Yanshen Sun, Jianfeng He, Limeng Cui, Shuo Lei, and Chang-Tien Lu · 2024
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A roadmap to pluralistic alignment
Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, et al · 2024
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Perils of self-feedback: Self-bias amplifies in large language models
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Unveiling the implicit toxicity in large language models
Jiaxin Wen, Pei Ke, Hao Sun, Zhexin Zhang, Chengfei Li, Jinfeng Bai, and Minlie Huang · 2023
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Do users write more insecure code with ai assistants?
Neil Perry, Megha Srivastava, Deepak Kumar, and Dan Boneh · 2023
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Whose opinions do language models reflect?
Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, and Tatsunori Hashimoto · 2023
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Does writing with language models reduce content diversity?
Vishakh Padmakumar and He He · 2023
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Proving test set contamination in black box language models
Yonatan Oren, Nicole Meister, Niladri Chatterji, Faisal Ladhak, and Tatsunori B Hashimoto · 2023
Cited alongside, same era.
Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr · 2023
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Wenda Xu, Guanglei Zhu, Xuandong Zhao, Liangming Pan, Lei Li, and William Yang Wang · 2024
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blingenf/copydetect: Code plagiarism detection tool
Bryson Lingenfelter · 2024
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In-context impersonation reveals large language models’ strengths and biases
Leonard Salewski, Stephan Alaniz, Isabel Rio-Torto, Eric Schulz, and Zeynep Akata · 2024
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distilbert/distilbert-base-uncased-finetuned-sst-2-english
HuggingFace · 2024
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Maartengr/bertopic_wikipedia
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Codeforces
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Claude-3 language model
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Effibench: Benchmarking the efficiency of automatically generated code
Dong Huang, Jie M Zhang, Yuhao Qing, and Heming Cui · 2024
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Using an llm to help with code understanding
Daye Nam, Andrew Macvean, Vincent Hellendoorn, Bogdan Vasilescu, and Brad Myers · 2024
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sentence-transformers/all-minilm-l6-v2
HuggingFace · 2024
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Understanding the effects of RLHF on LLM generalisation and diversity
Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, and Roberta Raileanu · 2024
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Web chat default temperature for gpt-3.5 and 4
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