Timezone: »
Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation. While recent works have demonstrated successes on controlling simple sentence attributes (e.g., sentiment), there has been little progress on complex, fine-grained controls (e.g., syntactic structure). To address this challenge, we develop a new non-autoregressive language model based on continuous diffusions that we call Diffusion-LM. Building upon the recent successes of diffusion models in continuous domains, Diffusion-LM iteratively denoises a sequence of Gaussian vectors into word vectors, yielding a sequence of intermediate latent variables. The continuous, hierarchical nature of these intermediate variables enables a simple gradient-based algorithm to perform complex, controllable generation tasks. We demonstrate successful control of Diffusion-LM for six challenging fine-grained control tasks, significantly outperforming prior work.
Author Information
Xiang Li (Stanford University)
John Thickstun (Stanford University)
Ishaan Gulrajani (MIT)
Percy Liang (Stanford University)

Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research spans machine learning and natural language processing, with the goal of developing trustworthy agents that can communicate effectively with people and improve over time through interaction. Specific topics include question answering, dialogue, program induction, interactive learning, and reliable machine learning. His awards include the IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).
Tatsunori Hashimoto (Stanford)
More from the Same Authors
-
2020 : Invited Talk 8 Presentation - Percy Liang - Semantic Parsing for Natural Language Interfaces »
Percy Liang -
2021 : Ensembles and Cocktails: Robust Finetuning for Natural Language Generation »
John Hewitt · Xiang Li · Sang Michael Xie · Benjamin Newman · Percy Liang -
2022 : A Closer Look at the Calibration of Differential Private Learners »
Hanlin Zhang · Xuechen (Chen) Li · Prithviraj Sen · Salim Roukos · Tatsunori Hashimoto -
2022 : Out-of-Distribution Robustness via Targeted Augmentations »
Irena Gao · Shiori Sagawa · Pang Wei Koh · Tatsunori Hashimoto · Percy Liang -
2022 : Surgical Fine-Tuning Improves Adaptation to Distribution Shifts »
Yoonho Lee · Annie Chen · Fahim Tajwar · Ananya Kumar · Huaxiu Yao · Percy Liang · Chelsea Finn -
2022 : Data Feedback Loops: Model-driven Amplification of Dataset Biases »
Rohan Taori · Tatsunori Hashimoto -
2022 : Undersampling is a Minimax Optimal Robustness Intervention in Nonparametric Classification »
Niladri S. Chatterji · Saminul Haque · Tatsunori Hashimoto -
2022 : Surgical Fine-Tuning Improves Adaptation to Distribution Shifts »
Yoonho Lee · Annie Chen · Fahim Tajwar · Ananya Kumar · Huaxiu Yao · Percy Liang · Chelsea Finn -
2023 Poster: Data Selection for Language Models via Importance Resampling »
Sang Michael Xie · Shibani Santurkar · Tengyu Ma · Percy Liang -
2023 Poster: PRODIGY: Enabling In-context Learning Over Graphs »
Qian Huang · Hongyu Ren · Peng Chen · Gregor Kržmanc · Daniel Zeng · Percy Liang · Jure Leskovec -
2023 Poster: DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining »
Sang Michael Xie · Hieu Pham · Xuanyi Dong · Nan Du · Hanxiao Liu · Yifeng Lu · Percy Liang · Quoc V Le · Tengyu Ma · Adams Wei Yu -
2023 Poster: Likelihood-Based Diffusion Language Models »
Ishaan Gulrajani · Tatsunori Hashimoto -
2023 Poster: MoCa: Measuring Human-Language Model Alignment on Causal and Moral Judgment Tasks »
Allen Nie · Yuhui Zhang · Atharva Shailesh Amdekar · Chris Piech · Tatsunori Hashimoto · Tobias Gerstenberg -
2023 Poster: Cheaply Evaluating Inference Efficiency Metrics for Autoregressive Transformer APIs »
Deepak Narayanan · Keshav Santhanam · Peter Henderson · Rishi Bommasani · Tony Lee · Percy Liang -
2023 Poster: Lexinvariant Language Models »
Qian Huang · Eric Zelikman · Sarah Chen · Yuhuai Wu · Gregory Valiant · Percy Liang -
2023 Poster: Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes »
Connor Toups · Rishi Bommasani · Kathleen Creel · Sarah Bana · Dan Jurafsky · Percy Liang -
2023 Poster: AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback »
Yann Dubois · Xuechen Li · Rohan Taori · Tianyi Zhang · Ishaan Gulrajani · Jimmy Ba · Carlos Guestrin · Percy Liang · Tatsunori Hashimoto -
2023 Poster: Learning to Compress Prompts with Gist Tokens »
Jesse Mu · Xiang Li · Noah Goodman -
2023 Poster: Holistic Evaluation of Text-to-Image Models »
Tony Lee · Michihiro Yasunaga · Chenlin Meng · Yifan Mai · Joon Sung Park · Agrim Gupta · Yunzhi Zhang · Deepak Narayanan · Hannah Teufel · Marco Bellagente · Minguk Kang · Taesung Park · Jure Leskovec · Jun-Yan Zhu · Fei-Fei Li · Jiajun Wu · Stefano Ermon · Percy Liang -
2023 Workshop: Workshop on Distribution Shifts: New Frontiers with Foundation Models »
Rebecca Roelofs · Fanny Yang · Hongseok Namkoong · Masashi Sugiyama · Jacob Eisenstein · Pang Wei Koh · Shiori Sagawa · Tatsunori Hashimoto · Yoonho Lee -
2022 Panel: Panel 4A-3: QUARK: Controllable Text… & Diffusion-LM Improves Controllable… »
Xiang Li · Ximing Lu -
2022 : Data Feedback Loops: Model-driven Amplification of Dataset Biases »
Rohan Taori · Tatsunori Hashimoto -
2022 : Fine-Tuning without Distortion: Improving Robustness to Distribution Shifts »
Percy Liang · Ananya Kumar -
2022 Workshop: MATH-AI: Toward Human-Level Mathematical Reasoning »
Pan Lu · Swaroop Mishra · Sean Welleck · Yuhuai Wu · Hannaneh Hajishirzi · Percy Liang -
2022 Poster: What Can Transformers Learn In-Context? A Case Study of Simple Function Classes »
Shivam Garg · Dimitris Tsipras · Percy Liang · Gregory Valiant -
2022 Poster: Insights into Pre-training via Simpler Synthetic Tasks »
Yuhuai Wu · Felix Li · Percy Liang -
2022 Poster: When Does Differentially Private Learning Not Suffer in High Dimensions? »
Xuechen Li · Daogao Liu · Tatsunori Hashimoto · Huseyin A. Inan · Janardhan Kulkarni · Yin-Tat Lee · Abhradeep Guha Thakurta -
2022 Poster: Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits »
Tong Mu · Yash Chandak · Tatsunori Hashimoto · Emma Brunskill -
2022 Poster: Deep Bidirectional Language-Knowledge Graph Pretraining »
Michihiro Yasunaga · Antoine Bosselut · Hongyu Ren · Xikun Zhang · Christopher D Manning · Percy Liang · Jure Leskovec -
2022 Poster: Decentralized Training of Foundation Models in Heterogeneous Environments »
Binhang Yuan · Yongjun He · Jared Davis · Tianyi Zhang · Tri Dao · Beidi Chen · Percy Liang · Christopher Ré · Ce Zhang -
2022 Poster: Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? »
Rishi Bommasani · Kathleen A. Creel · Ananya Kumar · Dan Jurafsky · Percy Liang -
2022 Poster: Improving Self-Supervised Learning by Characterizing Idealized Representations »
Yann Dubois · Stefano Ermon · Tatsunori Hashimoto · Percy Liang -
2021 : Panel: Future directions for tackling distribution shifts »
Tatsunori Hashimoto · Jamie Morgenstern · Judy Hoffman · Andrew Beck -
2021 Workshop: Distribution shifts: connecting methods and applications (DistShift) »
Shiori Sagawa · Pang Wei Koh · Fanny Yang · Hongseok Namkoong · Jiashi Feng · Kate Saenko · Percy Liang · Sarah Bird · Sergey Levine -
2021 Workshop: CtrlGen: Controllable Generative Modeling in Language and Vision »
Steven Y. Feng · Dor Arad Hudson · Tatsunori Hashimoto · DONGYEOP Kang · Varun Prashant Gangal · Anusha Balakrishnan · Joel Tetreault -
2021 Poster: MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers »
Krishna Pillutla · Swabha Swayamdipta · Rowan Zellers · John Thickstun · Sean Welleck · Yejin Choi · Zaid Harchaoui -
2021 Oral: MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers »
Krishna Pillutla · Swabha Swayamdipta · Rowan Zellers · John Thickstun · Sean Welleck · Yejin Choi · Zaid Harchaoui -
2020 : Invited Talk 8 Q/A - Percy Liang »
Percy Liang -
2020 Poster: Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming »
Sumanth Dathathri · Krishnamurthy Dvijotham · Alexey Kurakin · Aditi Raghunathan · Jonathan Uesato · Rudy Bunel · Shreya Shankar · Jacob Steinhardt · Ian Goodfellow · Percy Liang · Pushmeet Kohli -
2019 : Extended Poster Session »
Travis LaCroix · Marie Ossenkopf · Mina Lee · Nicole Fitzgerald · Daniela Mihai · Jonathon Hare · Ali Zaidi · Alexander Cowen-Rivers · Alana Marzoev · Eugene Kharitonov · Luyao Yuan · Tomasz Korbak · Paul Pu Liang · Yi Ren · Roberto Dessì · Peter Potash · Shangmin Guo · Tatsunori Hashimoto · Percy Liang · Julian Zubek · Zipeng Fu · Song-Chun Zhu · Adam Lerer -
2019 Poster: SPoC: Search-based Pseudocode to Code »
Sumith Kulal · Panupong Pasupat · Kartik Chandra · Mina Lee · Oded Padon · Alex Aiken · Percy Liang -
2019 Poster: On the Accuracy of Influence Functions for Measuring Group Effects »
Pang Wei Koh · Kai-Siang Ang · Hubert Teo · Percy Liang -
2019 Poster: Verified Uncertainty Calibration »
Ananya Kumar · Percy Liang · Tengyu Ma -
2019 Spotlight: Verified Uncertainty Calibration »
Ananya Kumar · Percy Liang · Tengyu Ma -
2018 : Natural Language Supervision »
Percy Liang -
2018 Poster: Uncertainty Sampling is Preconditioned Stochastic Gradient Descent on Zero-One Loss »
Stephen Mussmann · Percy Liang -
2018 Poster: Semidefinite relaxations for certifying robustness to adversarial examples »
Aditi Raghunathan · Jacob Steinhardt · Percy Liang -
2018 Poster: A Retrieve-and-Edit Framework for Predicting Structured Outputs »
Tatsunori Hashimoto · Kelvin Guu · Yonatan Oren · Percy Liang -
2018 Oral: A Retrieve-and-Edit Framework for Predicting Structured Outputs »
Tatsunori Hashimoto · Kelvin Guu · Yonatan Oren · Percy Liang -
2017 : (Invited Talk) Percy Liang: Learning with Adversaries and Collaborators »
Percy Liang -
2017 Workshop: Machine Learning and Computer Security »
Jacob Steinhardt · Nicolas Papernot · Bo Li · Chang Liu · Percy Liang · Dawn Song -
2017 Demonstration: Babble Labble: Learning from Natural Language Explanations »
Braden Hancock · Paroma Varma · Percy Liang · Christopher Ré · Stephanie Wang -
2017 Poster: Improved Training of Wasserstein GANs »
Ishaan Gulrajani · Faruk Ahmed · Martin Arjovsky · Vincent Dumoulin · Aaron Courville -
2017 Poster: Learning Overcomplete HMMs »
Vatsal Sharan · Sham Kakade · Percy Liang · Gregory Valiant -
2017 Poster: Certified Defenses for Data Poisoning Attacks »
Jacob Steinhardt · Pang Wei Koh · Percy Liang -
2017 Poster: Unsupervised Transformation Learning via Convex Relaxations »
Tatsunori Hashimoto · Percy Liang · John Duchi -
2016 Workshop: Deep Learning for Action and Interaction »
Chelsea Finn · Raia Hadsell · David Held · Sergey Levine · Percy Liang -
2016 Workshop: Nonconvex Optimization for Machine Learning: Theory and Practice »
Hossein Mobahi · Anima Anandkumar · Percy Liang · Stefanie Jegelka · Anna Choromanska -
2016 Workshop: Reliable Machine Learning in the Wild »
Dylan Hadfield-Menell · Adrian Weller · David Duvenaud · Jacob Steinhardt · Percy Liang -
2016 Poster: Unsupervised Risk Estimation Using Only Conditional Independence Structure »
Jacob Steinhardt · Percy Liang -
2015 : Sharing the "How" (and not the "What") »
Percy Liang -
2015 Workshop: Non-convex Optimization for Machine Learning: Theory and Practice »
Anima Anandkumar · Niranjan Uma Naresh · Kamalika Chaudhuri · Percy Liang · Sewoong Oh -
2015 Demonstration: CodaLab Worksheets for Reproducible, Executable Papers »
Percy Liang · Evelyne Viegas -
2015 Poster: On-the-Job Learning with Bayesian Decision Theory »
Keenon Werling · Arun Tejasvi Chaganty · Percy Liang · Christopher Manning -
2015 Spotlight: On-the-Job Learning with Bayesian Decision Theory »
Keenon Werling · Arun Tejasvi Chaganty · Percy Liang · Christopher Manning -
2015 Poster: Estimating Mixture Models via Mixtures of Polynomials »
Sida Wang · Arun Tejasvi Chaganty · Percy Liang -
2015 Poster: Learning with Relaxed Supervision »
Jacob Steinhardt · Percy Liang -
2015 Poster: Calibrated Structured Prediction »
Volodymyr Kuleshov · Percy Liang -
2014 Workshop: Challenges in Machine Learning workshop (CiML 2014) »
Isabelle Guyon · Evelyne Viegas · Percy Liang · Olga Russakovsky · Rinat Sergeev · Gábor Melis · Michele Sebag · Gustavo Stolovitzky · Jaume Bacardit · Michael S Kim · Ben Hamner -
2014 Poster: Altitude Training: Strong Bounds for Single-Layer Dropout »
Stefan Wager · William S Fithian · Sida Wang · Percy Liang -
2014 Poster: Simple MAP Inference via Low-Rank Relaxations »
Roy Frostig · Sida Wang · Percy Liang · Christopher D Manning -
2013 Poster: Dropout Training as Adaptive Regularization »
Stefan Wager · Sida Wang · Percy Liang -
2013 Spotlight: Dropout Training as Adaptive Regularization »
Stefan Wager · Sida Wang · Percy Liang -
2012 Poster: Identifiability and Unmixing of Latent Parse Trees »
Percy Liang · Sham M Kakade · Daniel Hsu -
2009 Workshop: The Generative and Discriminative Learning Interface »
Simon Lacoste-Julien · Percy Liang · Guillaume Bouchard -
2009 Poster: Asymptotically Optimal Regularization in Smooth Parametric Models »
Percy Liang · Francis Bach · Guillaume Bouchard · Michael Jordan -
2008 Workshop: Speech and Language: Unsupervised Latent-Variable Models »
Slav Petrov · Aria Haghighi · Percy Liang · Dan Klein -
2007 Poster: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
2007 Spotlight: Agreement-Based Learning »
Percy Liang · Dan Klein · Michael Jordan -
2007 Poster: A Probabilistic Approach to Language Change »
Alexandre Bouchard-Côté · Percy Liang · Tom Griffiths · Dan Klein