Skip to main content We gratefully acknowledge support from the Simons Foundation, member institutions , and all contributors. Donate > cs > arXiv:2204.02311 Help | Advanced Search All fields Title Author Abstract Comments Journal reference ACM classification MSC classification Report number arXiv identifier DOI ORCID arXiv author ID Help pages Full text Search open search GO open navigation menu quick links Login Help Pages About --> Computer Science > Computation and Language arXiv:2204.02311 (cs) [Submitted on 5 Apr 2022 ( v1 ), last revised 5 Oct 2022 (this version, v5)] Title: PaLM: Scaling Language Modeling with Pathways Authors: Aakanksha Chowdhery , Sharan Narang , Jacob Devlin , Maarten Bosma , Gaurav Mishra , Adam Roberts , Paul Barham , Hyung Won Chung , Charles Sutton , Sebastian Gehrmann , Parker Schuh , Kensen Shi , Sasha Tsvyashchenko , Joshua Maynez , Abhishek Rao , Parker Barnes , Yi Tay , Noam Shazeer , Vinodkumar Prabhakaran , Emily Reif , Nan Du , Ben Hutchinson , Reiner Pope , James Bradbury , Jacob Austin , Michael Isard , Guy Gur-Ari , Pengcheng Yin , Toju Duke , Anselm Levskaya , Sanjay Ghemawat , Sunipa Dev , Henryk Michalewski , Xavier Garcia , Vedant Misra , Kevin Robinson , Liam Fedus , Denny Zhou , Daphne Ippolito , David Luan , Hyeontaek Lim , Barret Zoph , Alexander Spiridonov , Ryan Sepassi , David Dohan , Shivani Agrawal , Mark Omernick , Andrew M. Dai , Thanumalayan Sankaranarayana Pillai , Marie Pellat , Aitor Lewkowycz , Erica Moreira , Rewon Child , Oleksandr Polozov , Katherine Lee , Zongwei Zhou , Xuezhi Wang , Brennan Saeta , Mark Diaz , Orhan Firat , Michele Catasta , Jason Wei , Kathy Meier-Hellstern , Douglas Eck , Jeff Dean , Slav Petrov , Noah Fiedel View a PDF of the paper titled PaLM: Scaling Language Modeling with Pathways, by Aakanksha Chowdhery and 66 other authors View PDF Abstract: Large language models have been shown to achieve remarkable performance across a variety of natural language tasks using few-shot learning, which drastically reduces the number of task-specific training examples needed to adapt the model to a particular application. To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM. We trained PaLM on 6144 TPU v4 chips using Pathways, a new ML system which enables highly efficient training across multiple TPU Pods. We demonstrate continued benefits of scaling by achieving state-of-the-art few-shot learning results on hundreds of language understanding and generation benchmarks. On a number of these tasks, PaLM 540B achieves breakthrough performance, outperforming the finetuned state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark. A significant number of BIG-bench tasks showed discontinuous improvements from model scale, meaning that performance steeply increased as we scaled to our largest model. PaLM also has strong capabilities in multilingual tasks and source code generation, which we demonstrate on a wide array of benchmarks. We additionally provide a comprehensive analysis on bias and toxicity, and study the extent of training data memorization with respect to model scale. Finally, we discuss the ethical considerations related to large language models and discuss potential mitigation strategies. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2204.02311 [cs.CL] (or arXiv:2204.02311v5 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2204.02311 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Aakanksha Chowdhery [ view email ] [v1] Tue, 5 Apr 2022 16:11:45 UTC (1,444 KB) [v2] Thu, 7 Apr 2022 16:38:01 UTC (1,445 KB) [v3] Tue, 19 Apr 2022 05:28:38 UTC (1,445 KB) [v4] Thu, 29 Sep 2022 13:22:22 UTC (1,479 KB) [v5] Wed, 5 Oct 2022 06:02:24 UTC (1,473 KB) Full-text links: Access Paper: View a PDF of the paper titled PaLM: Scaling Language Modeling with Pathways, by Aakanksha Chowdhery and 66 other authors View PDF TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2022-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar 11 blog links ( what is this? ) export BibTeX citation Loading... BibTeX formatted citation &times; loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) Links to Code Toggle Papers with Code ( What is Papers with Code? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) About Help contact arXiv Click here to contact arXiv Contact subscribe to arXiv mailings Click here to subscribe Subscribe Copyright Privacy Policy Web Accessibility Assistance arXiv Operational Status