Search Results for author: Percy Liang

Found 223 papers, 160 papers with code

Overparameterization hurts worst-group accuracy with spurious correlations

no code implementations ICML 2020 Shiori Sagawa, aditi raghunathan, Pang Wei Koh, Percy Liang

Increasing model capacity well beyond the point of zero training error has been observed to improve average test accuracy.

The Foundation Model Transparency Index v1.1: May 2024

no code implementations17 Jul 2024 Rishi Bommasani, Kevin Klyman, Sayash Kapoor, Shayne Longpre, Betty Xiong, Nestor Maslej, Percy Liang

To characterize the status quo, the Foundation Model Transparency Index was launched in October 2023 to measure the transparency of leading foundation model developers.

AutoBencher: Creating Salient, Novel, Difficult Datasets for Language Models

1 code implementation11 Jul 2024 Xiang Lisa Li, Evan Zheran Liu, Percy Liang, Tatsunori Hashimoto

In this paper, we present three desiderata for a good benchmark for language models: (i) salience (e. g., knowledge about World War II is more salient than a random day in history), (ii) novelty (i. e., the benchmark reveals new trends in model rankings not shown by previous benchmarks), and (iii) difficulty (i. e., the benchmark should be difficult for existing models, leaving headroom for future improvement).

Language Modelling Math +2

AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies

no code implementations25 Jun 2024 Yi Zeng, Kevin Klyman, Andy Zhou, Yu Yang, Minzhou Pan, Ruoxi Jia, Dawn Song, Percy Liang, Bo Li

We present a comprehensive AI risk taxonomy derived from eight government policies from the European Union, United States, and China and 16 company policies worldwide, making a significant step towards establishing a unified language for generative AI safety evaluation.

OpenVLA: An Open-Source Vision-Language-Action Model

no code implementations13 Jun 2024 Moo Jin Kim, Karl Pertsch, Siddharth Karamcheti, Ted Xiao, Ashwin Balakrishna, Suraj Nair, Rafael Rafailov, Ethan Foster, Grace Lam, Pannag Sanketi, Quan Vuong, Thomas Kollar, Benjamin Burchfiel, Russ Tedrake, Dorsa Sadigh, Sergey Levine, Percy Liang, Chelsea Finn

Large policies pretrained on a combination of Internet-scale vision-language data and diverse robot demonstrations have the potential to change how we teach robots new skills: rather than training new behaviors from scratch, we can fine-tune such vision-language-action (VLA) models to obtain robust, generalizable policies for visuomotor control.

Imitation Learning Language Modelling +1

BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments

1 code implementation27 May 2024 Yusuf Roohani, Jian Vora, Qian Huang, Zachary Steinhart, Alexander Marson, Percy Liang, Jure Leskovec

Agents based on large language models have shown great potential in accelerating scientific discovery by leveraging their rich background knowledge and reasoning capabilities.

AI Agent Bayesian Optimization

Introducing v0.5 of the AI Safety Benchmark from MLCommons

1 code implementation18 Apr 2024 Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed, Victor Akinwande, Namir Al-Nuaimi, Najla Alfaraj, Elie Alhajjar, Lora Aroyo, Trupti Bavalatti, Max Bartolo, Borhane Blili-Hamelin, Kurt Bollacker, Rishi Bomassani, Marisa Ferrara Boston, Siméon Campos, Kal Chakra, Canyu Chen, Cody Coleman, Zacharie Delpierre Coudert, Leon Derczynski, Debojyoti Dutta, Ian Eisenberg, James Ezick, Heather Frase, Brian Fuller, Ram Gandikota, Agasthya Gangavarapu, Ananya Gangavarapu, James Gealy, Rajat Ghosh, James Goel, Usman Gohar, Sujata Goswami, Scott A. Hale, Wiebke Hutiri, Joseph Marvin Imperial, Surgan Jandial, Nick Judd, Felix Juefei-Xu, Foutse khomh, Bhavya Kailkhura, Hannah Rose Kirk, Kevin Klyman, Chris Knotz, Michael Kuchnik, Shachi H. Kumar, Srijan Kumar, Chris Lengerich, Bo Li, Zeyi Liao, Eileen Peters Long, Victor Lu, Sarah Luger, Yifan Mai, Priyanka Mary Mammen, Kelvin Manyeki, Sean McGregor, Virendra Mehta, Shafee Mohammed, Emanuel Moss, Lama Nachman, Dinesh Jinenhally Naganna, Amin Nikanjam, Besmira Nushi, Luis Oala, Iftach Orr, Alicia Parrish, Cigdem Patlak, William Pietri, Forough Poursabzi-Sangdeh, Eleonora Presani, Fabrizio Puletti, Paul Röttger, Saurav Sahay, Tim Santos, Nino Scherrer, Alice Schoenauer Sebag, Patrick Schramowski, Abolfazl Shahbazi, Vin Sharma, Xudong Shen, Vamsi Sistla, Leonard Tang, Davide Testuggine, Vithursan Thangarasa, Elizabeth Anne Watkins, Rebecca Weiss, Chris Welty, Tyler Wilbers, Adina Williams, Carole-Jean Wu, Poonam Yadav, Xianjun Yang, Yi Zeng, Wenhui Zhang, Fedor Zhdanov, Jiacheng Zhu, Percy Liang, Peter Mattson, Joaquin Vanschoren

We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.

Length-Controlled AlpacaEval: A Simple Way to Debias Automatic Evaluators

1 code implementation6 Apr 2024 Yann Dubois, Balázs Galambosi, Percy Liang, Tatsunori B. Hashimoto

Even simple, known confounders such as preference for longer outputs remain in existing automated evaluation metrics.

Chatbot counterfactual

Foundation Model Transparency Reports

no code implementations26 Feb 2024 Rishi Bommasani, Kevin Klyman, Shayne Longpre, Betty Xiong, Sayash Kapoor, Nestor Maslej, Arvind Narayanan, Percy Liang

Foundation models are critical digital technologies with sweeping societal impact that necessitates transparency.

Prismatic VLMs: Investigating the Design Space of Visually-Conditioned Language Models

2 code implementations12 Feb 2024 Siddharth Karamcheti, Suraj Nair, Ashwin Balakrishna, Percy Liang, Thomas Kollar, Dorsa Sadigh

Visually-conditioned language models (VLMs) have seen growing adoption in applications such as visual dialogue, scene understanding, and robotic task planning; adoption that has fueled a wealth of new models such as LLaVa, InstructBLIP, and PaLI-3.

Hallucination Object Localization +3

Model Editing with Canonical Examples

1 code implementation9 Feb 2024 John Hewitt, Sarah Chen, Lanruo Lora Xie, Edward Adams, Percy Liang, Christopher D. Manning

The Backpack defines a large bank of sense vectors--a decomposition of the different uses of each word--which are weighted and summed to form the output logits of the model.

Language Modelling Model Editing

On the Learnability of Watermarks for Language Models

1 code implementation7 Dec 2023 Chenchen Gu, Xiang Lisa Li, Percy Liang, Tatsunori Hashimoto

Watermarking of language model outputs enables statistical detection of model-generated text, which can mitigate harms and misuses of language models.

Decoder Language Modelling

Llamas Know What GPTs Don't Show: Surrogate Models for Confidence Estimation

no code implementations15 Nov 2023 Vaishnavi Shrivastava, Percy Liang, Ananya Kumar

To maintain user trust, large language models (LLMs) should signal low confidence on examples where they are incorrect, instead of misleading the user.

Question Answering

The Foundation Model Transparency Index

1 code implementation19 Oct 2023 Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, Percy Liang

We score 10 major foundation model developers (e. g. OpenAI, Google, Meta) against the 100 indicators to assess their transparency.

MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation

1 code implementation5 Oct 2023 Qian Huang, Jian Vora, Percy Liang, Jure Leskovec

A central aspect of machine learning research is experimentation, the process of designing and running experiments, analyzing the results, and iterating towards some positive outcome (e. g., improving accuracy).

Benchmarking Decision Making +1

Benchmarking and Improving Generator-Validator Consistency of Language Models

no code implementations3 Oct 2023 Xiang Lisa Li, Vaishnavi Shrivastava, Siyan Li, Tatsunori Hashimoto, Percy Liang

To improve the consistency of LMs, we propose to finetune on the filtered generator and validator responses that are GV-consistent, and call this approach consistency fine-tuning.

Benchmarking Instruction Following +1

Large Language Models as Analogical Reasoners

no code implementations3 Oct 2023 Michihiro Yasunaga, Xinyun Chen, Yujia Li, Panupong Pasupat, Jure Leskovec, Percy Liang, Ed H. Chi, Denny Zhou

Chain-of-thought (CoT) prompting for language models demonstrates impressive performance across reasoning tasks, but typically needs labeled exemplars of the reasoning process.

Code Generation GSM8K +1

Robust Distortion-free Watermarks for Language Models

2 code implementations28 Jul 2023 Rohith Kuditipudi, John Thickstun, Tatsunori Hashimoto, Percy Liang

We generate watermarked text by mapping a sequence of random numbers -- which we compute using a randomized watermark key -- to a sample from the language model.

Language Modelling

Ecosystem-level Analysis of Deployed Machine Learning Reveals Homogeneous Outcomes

no code implementations NeurIPS 2023 Connor Toups, Rishi Bommasani, Kathleen A. Creel, Sarah H. Bana, Dan Jurafsky, Percy Liang

In practice, the societal impact of machine learning is determined by the surrounding context of machine learning deployments.

Lost in the Middle: How Language Models Use Long Contexts

4 code implementations6 Jul 2023 Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, Percy Liang

While recent language models have the ability to take long contexts as input, relatively little is known about how well they use longer context.

Language Modelling Position +2

Anticipatory Music Transformer

no code implementations14 Jun 2023 John Thickstun, David Hall, Chris Donahue, Percy Liang

We achieve this by interleaving sequences of events and controls, such that controls appear following stopping times in the event sequence.

Music Generation

One-sided Matrix Completion from Two Observations Per Row

no code implementations6 Jun 2023 Steven Cao, Percy Liang, Gregory Valiant

We propose a natural algorithm that involves imputing the missing values of the matrix $X^TX$ and show that even with only two observations per row in $X$, we can provably recover $X^TX$ as long as we have at least $\Omega(r^2 d \log d)$ rows, where $r$ is the rank and $d$ is the number of columns.

Matrix Completion

Has the Machine Learning Review Process Become More Arbitrary as the Field Has Grown? The NeurIPS 2021 Consistency Experiment

no code implementations5 Jun 2023 Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan

We present the NeurIPS 2021 consistency experiment, a larger-scale variant of the 2014 NeurIPS experiment in which 10% of conference submissions were reviewed by two independent committees to quantify the randomness in the review process.

Beyond Positive Scaling: How Negation Impacts Scaling Trends of Language Models

1 code implementation27 May 2023 Yuhui Zhang, Michihiro Yasunaga, Zhengping Zhou, Jeff Z. HaoChen, James Zou, Percy Liang, Serena Yeung

Language models have been shown to exhibit positive scaling, where performance improves as models are scaled up in terms of size, compute, or data.

Negation Question Answering +1

Backpack Language Models

1 code implementation26 May 2023 John Hewitt, John Thickstun, Christopher D. Manning, Percy Liang

We can interpret a sense vector by inspecting its (non-contextual, linear) projection onto the output space, and intervene on these interpretable hooks to change the model's behavior in predictable ways.

Language Modelling Text Generation +1

Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training

4 code implementations23 May 2023 Hong Liu, Zhiyuan Li, David Hall, Percy Liang, Tengyu Ma

Given the massive cost of language model pre-training, a non-trivial improvement of the optimization algorithm would lead to a material reduction on the time and cost of training.

Language Modelling Stochastic Optimization

AlpacaFarm: A Simulation Framework for Methods that Learn from Human Feedback

2 code implementations NeurIPS 2023 Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, Tatsunori B. Hashimoto

As a demonstration of the research possible in AlpacaFarm, we find that methods that use a reward model can substantially improve over supervised fine-tuning and that our reference PPO implementation leads to a +10% improvement in win-rate against Davinci003.

Instruction Following

PRODIGY: Enabling In-context Learning Over Graphs

no code implementations NeurIPS 2023 Qian Huang, Hongyu Ren, Peng Chen, Gregor Kržmanc, Daniel Zeng, Percy Liang, Jure Leskovec

In-context learning is the ability of a pretrained model to adapt to novel and diverse downstream tasks by conditioning on prompt examples, without optimizing any parameters.

Graph Neural Network In-Context Learning +1

DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining

2 code implementations NeurIPS 2023 Sang Michael Xie, Hieu Pham, Xuanyi Dong, Nan Du, Hanxiao Liu, Yifeng Lu, Percy Liang, Quoc V. Le, Tengyu Ma, Adams Wei Yu

The mixture proportions of pretraining data domains (e. g., Wikipedia, books, web text) greatly affect language model (LM) performance.

Language Modelling

Evaluating Verifiability in Generative Search Engines

2 code implementations19 Apr 2023 Nelson F. Liu, Tianyi Zhang, Percy Liang

Generative search engines directly generate responses to user queries, along with in-line citations.


Generative Agents: Interactive Simulacra of Human Behavior

7 code implementations7 Apr 2023 Joon Sung Park, Joseph C. O'Brien, Carrie J. Cai, Meredith Ringel Morris, Percy Liang, Michael S. Bernstein

Believable proxies of human behavior can empower interactive applications ranging from immersive environments to rehearsal spaces for interpersonal communication to prototyping tools.

Language Modelling Large Language Model

Whose Opinions Do Language Models Reflect?

1 code implementation30 Mar 2023 Shibani Santurkar, Esin Durmus, Faisal Ladhak, Cinoo Lee, Percy Liang, Tatsunori Hashimoto

Language models (LMs) are increasingly being used in open-ended contexts, where the opinions reflected by LMs in response to subjective queries can have a profound impact, both on user satisfaction, as well as shaping the views of society at large.

Ecosystem Graphs: The Social Footprint of Foundation Models

no code implementations28 Mar 2023 Rishi Bommasani, Dilara Soylu, Thomas I. Liao, Kathleen A. Creel, Percy Liang

Foundation models (e. g. ChatGPT, StableDiffusion) pervasively influence society, warranting immediate social attention.

FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU

1 code implementation13 Mar 2023 Ying Sheng, Lianmin Zheng, Binhang Yuan, Zhuohan Li, Max Ryabinin, Daniel Y. Fu, Zhiqiang Xie, Beidi Chen, Clark Barrett, Joseph E. Gonzalez, Percy Liang, Christopher Ré, Ion Stoica, Ce Zhang

As a result, when running OPT-175B on a single 16GB GPU, FlexGen achieves significantly higher throughput compared to state-of-the-art offloading systems, reaching a generation throughput of 1 token/s for the first time with an effective batch size of 144.

Language Modelling Large Language Model

Improving Representational Continuity via Continued Pretraining

1 code implementation26 Feb 2023 Michael Sun, Ananya Kumar, Divyam Madaan, Percy Liang

We consider the continual representation learning setting: sequentially pretrain a model $M'$ on tasks $T_1, \ldots, T_T$, and then adapt $M'$ on a small amount of data from each task $T_i$ to check if it has forgotten information from old tasks.

Continual Learning Representation Learning +1

Language-Driven Representation Learning for Robotics

2 code implementations24 Feb 2023 Siddharth Karamcheti, Suraj Nair, Annie S. Chen, Thomas Kollar, Chelsea Finn, Dorsa Sadigh, Percy Liang

First, we demonstrate that existing representations yield inconsistent results across these tasks: masked autoencoding approaches pick up on low-level spatial features at the cost of high-level semantics, while contrastive learning approaches capture the opposite.

Contrastive Learning Imitation Learning +2

Out-of-Domain Robustness via Targeted Augmentations

1 code implementation23 Feb 2023 Irena Gao, Shiori Sagawa, Pang Wei Koh, Tatsunori Hashimoto, Percy Liang

Models trained on one set of domains often suffer performance drops on unseen domains, e. g., when wildlife monitoring models are deployed in new camera locations.

Data Selection for Language Models via Importance Resampling

1 code implementation NeurIPS 2023 Sang Michael Xie, Shibani Santurkar, Tengyu Ma, Percy Liang

To measure whether hashed n-gram features preserve the aspects of the data that are relevant to the target, we define KL reduction, a data metric that measures the proximity between the selected pretraining data and the target on some feature space.

Evaluating Self-Supervised Learning via Risk Decomposition

1 code implementation6 Feb 2023 Yann Dubois, Tatsunori Hashimoto, Percy Liang

Our decomposition consists of four error components: approximation, representation usability, probe generalization, and encoder generalization.

Representation Learning Self-Supervised Learning

Benchmarking Large Language Models for News Summarization

1 code implementation31 Jan 2023 Tianyi Zhang, Faisal Ladhak, Esin Durmus, Percy Liang, Kathleen McKeown, Tatsunori B. Hashimoto

Large language models (LLMs) have shown promise for automatic summarization but the reasons behind their successes are poorly understood.

Benchmarking News Summarization

"No, to the Right" -- Online Language Corrections for Robotic Manipulation via Shared Autonomy

1 code implementation6 Jan 2023 Yuchen Cui, Siddharth Karamcheti, Raj Palleti, Nidhya Shivakumar, Percy Liang, Dorsa Sadigh

Instead of discrete turn-taking between a human and robot, LILAC splits agency between the human and robot: language is an input to a learned model that produces a meaningful, low-dimensional control space that the human can use to guide the robot.

Instruction Following

Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP

2 code implementations28 Dec 2022 Omar Khattab, Keshav Santhanam, Xiang Lisa Li, David Hall, Percy Liang, Christopher Potts, Matei Zaharia

Retrieval-augmented in-context learning has emerged as a powerful approach for addressing knowledge-intensive tasks using frozen language models (LM) and retrieval models (RM).

In-Context Learning Language Modelling +2

Trustworthy Social Bias Measurement

1 code implementation20 Dec 2022 Rishi Bommasani, Percy Liang

How do we design measures of social bias that we trust?

Evaluating Human-Language Model Interaction

1 code implementation19 Dec 2022 Mina Lee, Megha Srivastava, Amelia Hardy, John Thickstun, Esin Durmus, Ashwin Paranjape, Ines Gerard-Ursin, Xiang Lisa Li, Faisal Ladhak, Frieda Rong, Rose E. Wang, Minae Kwon, Joon Sung Park, Hancheng Cao, Tony Lee, Rishi Bommasani, Michael Bernstein, Percy Liang

To evaluate human-LM interaction, we develop a new framework, Human-AI Language-based Interaction Evaluation (HALIE), that defines the components of interactive systems and dimensions to consider when designing evaluation metrics.

Language Modelling Question Answering

Melody transcription via generative pre-training

1 code implementation4 Dec 2022 Chris Donahue, John Thickstun, Percy Liang

The combination of generative pre-training and a new dataset for this task results in $77$% stronger performance on melody transcription relative to the strongest available baseline.

Chord Recognition Information Retrieval +2

Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?

no code implementations25 Nov 2022 Rishi Bommasani, Kathleen A. Creel, Ananya Kumar, Dan Jurafsky, Percy Liang

As the scope of machine learning broadens, we observe a recurring theme of algorithmic monoculture: the same systems, or systems that share components (e. g. training data), are deployed by multiple decision-makers.


How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?

no code implementations22 Nov 2022 Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, Jennifer Wortman Vaughan, Zhenyu Xue, Hal Daumé III, Emma Pierson, Nihar B. Shah

In a top-tier computer science conference (NeurIPS 2021) with more than 23, 000 submitting authors and 9, 000 submitted papers, we survey the authors on three questions: (i) their predicted probability of acceptance for each of their papers, (ii) their perceived ranking of their own papers based on scientific contribution, and (iii) the change in their perception about their own papers after seeing the reviews.

Retrieval-Augmented Multimodal Language Modeling

no code implementations22 Nov 2022 Michihiro Yasunaga, Armen Aghajanyan, Weijia Shi, Rich James, Jure Leskovec, Percy Liang, Mike Lewis, Luke Zettlemoyer, Wen-tau Yih

To integrate knowledge in a more scalable and modular way, we propose a retrieval-augmented multimodal model, which enables a base multimodal model (generator) to refer to relevant text and images fetched by a retriever from external memory (e. g., documents on the web).

Caption Generation Image Captioning +5

Truncation Sampling as Language Model Desmoothing

1 code implementation27 Oct 2022 John Hewitt, Christopher D. Manning, Percy Liang

In this light, truncation algorithms aim to perform desmoothing, estimating a subset of the support of the true distribution.

Language Modelling

Contrastive Decoding: Open-ended Text Generation as Optimization

2 code implementations27 Oct 2022 Xiang Lisa Li, Ari Holtzman, Daniel Fried, Percy Liang, Jason Eisner, Tatsunori Hashimoto, Luke Zettlemoyer, Mike Lewis

We propose contrastive decoding (CD), a reliable decoding approach that optimizes a contrastive objective subject to a plausibility constraint.

Language Modelling Text Generation

Surgical Fine-Tuning Improves Adaptation to Distribution Shifts

1 code implementation20 Oct 2022 Yoonho Lee, Annie S. Chen, Fahim Tajwar, Ananya Kumar, Huaxiu Yao, Percy Liang, Chelsea Finn

A common approach to transfer learning under distribution shift is to fine-tune the last few layers of a pre-trained model, preserving learned features while also adapting to the new task.

Transfer Learning

Are Sample-Efficient NLP Models More Robust?

no code implementations12 Oct 2022 Nelson F. Liu, Ananya Kumar, Percy Liang, Robin Jia

Recent results in image classification and extractive question answering have observed that pre-trained models trained on less in-distribution data have better out-of-distribution performance.

Extractive Question-Answering Image Classification +2

What Can Transformers Learn In-Context? A Case Study of Simple Function Classes

1 code implementation1 Aug 2022 Shivam Garg, Dimitris Tsipras, Percy Liang, Gregory Valiant

To make progress towards understanding in-context learning, we consider the well-defined problem of training a model to in-context learn a function class (e. g., linear functions): that is, given data derived from some functions in the class, can we train a model to in-context learn "most" functions from this class?

In-Context Learning

Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift

no code implementations18 Jul 2022 Ananya Kumar, Tengyu Ma, Percy Liang, aditi raghunathan

We often see undesirable tradeoffs in robust machine learning where out-of-distribution (OOD) accuracy is at odds with in-distribution (ID) accuracy: a robust classifier obtained via specialized techniques such as removing spurious features often has better OOD but worse ID accuracy compared to a standard classifier trained via ERM.

Is a Caption Worth a Thousand Images? A Controlled Study for Representation Learning

no code implementations15 Jul 2022 Shibani Santurkar, Yann Dubois, Rohan Taori, Percy Liang, Tatsunori Hashimoto

The development of CLIP [Radford et al., 2021] has sparked a debate on whether language supervision can result in vision models with more transferable representations than traditional image-only methods.

Descriptive Representation Learning

Insights into Pre-training via Simpler Synthetic Tasks

1 code implementation21 Jun 2022 Yuhuai Wu, Felix Li, Percy Liang

Second, to our surprise, we find that pre-training on a simple and generic synthetic task defined by the Set function achieves $65\%$ of the benefits, almost matching LIME.

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

4 code implementations9 Jun 2022 Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew Dai, Andrew La, Andrew Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakaş, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartłomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, César Ferri Ramírez, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Chris Waites, Christian Voigt, Christopher D. Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguí González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodola, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A. Chi, Ethan Dyer, Ethan Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martínez-Plumed, Francesca Happé, Francois Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germán Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Wang, Gonzalo Jaimovitch-López, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Shevlin, Hinrich Schütze, Hiromu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B. Simon, James Koppel, James Zheng, James Zou, Jan Kocoń, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jörg Frohberg, Jos Rozen, Jose Hernandez-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, Katja Markert, Kaustubh D. Dhole, Kevin Gimpel, Kevin Omondi, Kory Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras-Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros Colón, Luke Metz, Lütfi Kerem Şenel, Maarten Bosma, Maarten Sap, Maartje ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ramírez Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L. Leavitt, Matthias Hagen, Mátyás Schubert, Medina Orduna Baitemirova, Melody Arnaud, Melvin McElrath, Michael A. Yee, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, Michael Strube, Michał Swędrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T, Nanyun Peng, Nathan A. Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nicole Martinez, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha S. Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Miłkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan LeBras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Ruslan Salakhutdinov, Ryan Chi, Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel S. Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima, Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven T. Piantadosi, Stuart M. Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsu Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay Ramasesh, Vinay Uday Prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Yiding Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, ZiRui Wang, Ziyi Wu

BIG-bench focuses on tasks that are believed to be beyond the capabilities of current language models.

Common Sense Reasoning Math +1

Decentralized Training of Foundation Models in Heterogeneous Environments

1 code implementation2 Jun 2022 Binhang Yuan, Yongjun He, Jared Quincy Davis, Tianyi Zhang, Tri Dao, Beidi Chen, Percy Liang, Christopher Re, Ce Zhang

Our key technical contribution is a scheduling algorithm that allocates different computational "tasklets" in the training of foundation models to a group of decentralized GPU devices connected by a slow heterogeneous network.


Diffusion-LM Improves Controllable Text Generation

1 code implementation27 May 2022 Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, Percy Liang, Tatsunori B. Hashimoto

Controlling the behavior of language models (LMs) without re-training is a major open problem in natural language generation.

Language Modelling Sentence +1

Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation

no code implementations1 Apr 2022 Kendrick Shen, Robbie Jones, Ananya Kumar, Sang Michael Xie, Jeff Z. HaoChen, Tengyu Ma, Percy Liang

We consider unsupervised domain adaptation (UDA), where labeled data from a source domain (e. g., photographs) and unlabeled data from a target domain (e. g., sketches) are used to learn a classifier for the target domain.

Contrastive Learning Unsupervised Domain Adaptation

Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution

3 code implementations21 Feb 2022 Ananya Kumar, aditi raghunathan, Robbie Jones, Tengyu Ma, Percy Liang

However, in this paper, we find that fine-tuning can achieve worse accuracy than linear probing out-of-distribution (OOD) when the pretrained features are good and the distribution shift is large.

GreaseLM: Graph REASoning Enhanced Language Models for Question Answering

1 code implementation21 Jan 2022 Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang, Christopher D. Manning, Jure Leskovec

Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it.

Knowledge Graphs Negation +2

CoAuthor: Designing a Human-AI Collaborative Writing Dataset for Exploring Language Model Capabilities

1 code implementation18 Jan 2022 Mina Lee, Percy Liang, Qian Yang

Large language models (LMs) offer unprecedented language generation capabilities and exciting opportunities for interaction design.

Language Modelling Text Generation

Extending the WILDS Benchmark for Unsupervised Adaptation

1 code implementation ICLR 2022 Shiori Sagawa, Pang Wei Koh, Tony Lee, Irena Gao, Sang Michael Xie, Kendrick Shen, Ananya Kumar, Weihua Hu, Michihiro Yasunaga, Henrik Marklund, Sara Beery, Etienne David, Ian Stavness, Wei Guo, Jure Leskovec, Kate Saenko, Tatsunori Hashimoto, Sergey Levine, Chelsea Finn, Percy Liang

Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can often be obtained from distributions beyond the source distribution as well.

LILA: Language-Informed Latent Actions

1 code implementation5 Nov 2021 Siddharth Karamcheti, Megha Srivastava, Percy Liang, Dorsa Sadigh

We introduce Language-Informed Latent Actions (LILA), a framework for learning natural language interfaces in the context of human-robot collaboration.

Imitation Learning

Large Language Models Can Be Strong Differentially Private Learners

4 code implementations ICLR 2022 Xuechen Li, Florian Tramèr, Percy Liang, Tatsunori Hashimoto

Differentially Private (DP) learning has seen limited success for building large deep learning models of text, and straightforward attempts at applying Differentially Private Stochastic Gradient Descent (DP-SGD) to NLP tasks have resulted in large performance drops and high computational overhead.

How does Contrastive Pre-training Connect Disparate Domains?

no code implementations29 Sep 2021 Kendrick Shen, Robbie Matthew Jones, Ananya Kumar, Sang Michael Xie, Percy Liang

We develop a conceptual model for contrastive learning under domain shifts, where data augmentations form connections between classes and domains that can be far apart.

Contrastive Learning Unsupervised Domain Adaptation

Ensembles and Cocktails: Robust Finetuning for Natural Language Generation

no code implementations29 Sep 2021 John Hewitt, Xiang Lisa Li, Sang Michael Xie, Benjamin Newman, Percy Liang

When finetuning a pretrained language model for natural language generation tasks, one is currently faced with a tradeoff.

Language Modelling Text Generation

Calibrated ensembles - a simple way to mitigate ID-OOD accuracy tradeoffs

no code implementations29 Sep 2021 Ananya Kumar, aditi raghunathan, Tengyu Ma, Percy Liang

We often see undesirable tradeoffs in robust machine learning where out-of-distribution (OOD) accuracy is at odds with in-distribution (ID) accuracy.

GreaseLM: Graph REASoning Enhanced Language Models

no code implementations ICLR 2022 Xikun Zhang, Antoine Bosselut, Michihiro Yasunaga, Hongyu Ren, Percy Liang, Christopher D Manning, Jure Leskovec

Answering complex questions about textual narratives requires reasoning over both stated context and the world knowledge that underlies it.

Knowledge Graphs Negation +2

Conditional probing: measuring usable information beyond a baseline

1 code implementation EMNLP 2021 John Hewitt, Kawin Ethayarajh, Percy Liang, Christopher D. Manning

Probing experiments investigate the extent to which neural representations make properties -- like part-of-speech -- predictable.

Word Embeddings

LM-Critic: Language Models for Unsupervised Grammatical Error Correction

2 code implementations EMNLP 2021 Michihiro Yasunaga, Jure Leskovec, Percy Liang

Training a model for grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs, but manually annotating such pairs can be expensive.

Grammatical Error Correction Language Modelling +2

On the Opportunities and Risks of Foundation Models

2 code implementations16 Aug 2021 Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S. Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, Erik Brynjolfsson, Shyamal Buch, Dallas Card, Rodrigo Castellon, Niladri Chatterji, Annie Chen, Kathleen Creel, Jared Quincy Davis, Dora Demszky, Chris Donahue, Moussa Doumbouya, Esin Durmus, Stefano Ermon, John Etchemendy, Kawin Ethayarajh, Li Fei-Fei, Chelsea Finn, Trevor Gale, Lauren Gillespie, Karan Goel, Noah Goodman, Shelby Grossman, Neel Guha, Tatsunori Hashimoto, Peter Henderson, John Hewitt, Daniel E. Ho, Jenny Hong, Kyle Hsu, Jing Huang, Thomas Icard, Saahil Jain, Dan Jurafsky, Pratyusha Kalluri, Siddharth Karamcheti, Geoff Keeling, Fereshte Khani, Omar Khattab, Pang Wei Koh, Mark Krass, Ranjay Krishna, Rohith Kuditipudi, Ananya Kumar, Faisal Ladhak, Mina Lee, Tony Lee, Jure Leskovec, Isabelle Levent, Xiang Lisa Li, Xuechen Li, Tengyu Ma, Ali Malik, Christopher D. Manning, Suvir Mirchandani, Eric Mitchell, Zanele Munyikwa, Suraj Nair, Avanika Narayan, Deepak Narayanan, Ben Newman, Allen Nie, Juan Carlos Niebles, Hamed Nilforoshan, Julian Nyarko, Giray Ogut, Laurel Orr, Isabel Papadimitriou, Joon Sung Park, Chris Piech, Eva Portelance, Christopher Potts, aditi raghunathan, Rob Reich, Hongyu Ren, Frieda Rong, Yusuf Roohani, Camilo Ruiz, Jack Ryan, Christopher Ré, Dorsa Sadigh, Shiori Sagawa, Keshav Santhanam, Andy Shih, Krishnan Srinivasan, Alex Tamkin, Rohan Taori, Armin W. Thomas, Florian Tramèr, Rose E. Wang, William Wang, Bohan Wu, Jiajun Wu, Yuhuai Wu, Sang Michael Xie, Michihiro Yasunaga, Jiaxuan You, Matei Zaharia, Michael Zhang, Tianyi Zhang, Xikun Zhang, Yuhui Zhang, Lucia Zheng, Kaitlyn Zhou, Percy Liang

AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.

Transfer Learning

Just Train Twice: Improving Group Robustness without Training Group Information

1 code implementation19 Jul 2021 Evan Zheran Liu, Behzad Haghgoo, Annie S. Chen, aditi raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, Chelsea Finn

Standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on certain groups, especially in the presence of spurious correlations between the input and label.

Image Classification Out-of-Distribution Generalization

Codified audio language modeling learns useful representations for music information retrieval

1 code implementation12 Jul 2021 Rodrigo Castellon, Chris Donahue, Percy Liang

Relative to representations from conventional MIR models which are pre-trained on tagging, we find that using representations from Jukebox as input features yields 30% stronger performance on average across four MIR tasks: tagging, genre classification, emotion recognition, and key detection.

Emotion Recognition Genre classification +8

Break-It-Fix-It: Unsupervised Learning for Program Repair

1 code implementation11 Jun 2021 Michihiro Yasunaga, Percy Liang

To bridge this gap, we propose a new training approach, Break-It-Fix-It (BIFI), which has two key ideas: (i) we use the critic to check a fixer's output on real bad inputs and add good (fixed) outputs to the training data, and (ii) we train a breaker to generate realistic bad code from good code.

C++ code Code Repair +4

Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Quality

1 code implementation NAACL 2021 Mina Lee, Chris Donahue, Robin Jia, Alexander Iyabor, Percy Liang

We release a new benchmark for lexical substitution, the task of finding appropriate substitutes for a target word in a context.

QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering

4 code implementations NAACL 2021 Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, Jure Leskovec

The problem of answering questions using knowledge from pre-trained language models (LMs) and knowledge graphs (KGs) presents two challenges: given a QA context (question and answer choice), methods need to (i) identify relevant knowledge from large KGs, and (ii) perform joint reasoning over the QA context and KG.

Graph Representation Learning Knowledge Graphs +5

Do Question Answering Modeling Improvements Hold Across Benchmarks?

no code implementations1 Feb 2021 Nelson F. Liu, Tony Lee, Robin Jia, Percy Liang

Do question answering (QA) modeling improvements (e. g., choice of architecture and training procedure) hold consistently across the diverse landscape of QA benchmarks?

Question Answering

Prefix-Tuning: Optimizing Continuous Prompts for Generation

13 code implementations ACL 2021 Xiang Lisa Li, Percy Liang

Fine-tuning is the de facto way to leverage large pretrained language models to perform downstream tasks.

Language Modelling Table-to-Text Generation

In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution Robustness

1 code implementation ICLR 2021 Sang Michael Xie, Ananya Kumar, Robbie Jones, Fereshte Khani, Tengyu Ma, Percy Liang

To get the best of both worlds, we introduce In-N-Out, which first trains a model with auxiliary inputs and uses it to pseudolabel all the in-distribution inputs, then pre-trains a model on OOD auxiliary outputs and fine-tunes this model with the pseudolabels (self-training).

Time Series Time Series Analysis +1

Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionately

1 code implementation7 Dec 2020 Fereshte Khani, Percy Liang

The presence of spurious features interferes with the goal of obtaining robust models that perform well across many groups within the population.

Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases

1 code implementation16 Nov 2020 Yu Gu, Sue Kase, Michelle Vanni, Brian Sadler, Percy Liang, Xifeng Yan, Yu Su

To facilitate the development of KBQA models with stronger generalization, we construct and release a new large-scale, high-quality dataset with 64, 331 questions, GrailQA, and provide evaluation settings for all three levels of generalization.

Knowledge Base Question Answering

Selective Classification Can Magnify Disparities Across Groups

1 code implementation ICLR 2021 Erik Jones, Shiori Sagawa, Pang Wei Koh, Ananya Kumar, Percy Liang

In this paper, we find that while selective classification can improve average accuracies, it can simultaneously magnify existing accuracy disparities between various groups within a population, especially in the presence of spurious correlations.

Classification General Classification

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming

2 code implementations NeurIPS 2020 Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin, aditi raghunathan, Jonathan Uesato, Rudy Bunel, Shreya Shankar, Jacob Steinhardt, Ian Goodfellow, Percy Liang, Pushmeet Kohli

In this work, we propose a first-order dual SDP algorithm that (1) requires memory only linear in the total number of network activations, (2) only requires a fixed number of forward/backward passes through the network per iteration.


The EOS Decision and Length Extrapolation

1 code implementation EMNLP (BlackboxNLP) 2020 Benjamin Newman, John Hewitt, Percy Liang, Christopher D. Manning

Extrapolation to unseen sequence lengths is a challenge for neural generative models of language.

Learning Adaptive Language Interfaces through Decomposition

no code implementations EMNLP (intexsempar) 2020 Siddharth Karamcheti, Dorsa Sadigh, Percy Liang

Our goal is to create an interactive natural language interface that efficiently and reliably learns from users to complete tasks in simulated robotics settings.

Semantic Parsing

On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks

1 code implementation Findings of the Association for Computational Linguistics 2020 Stephen Mussmann, Robin Jia, Percy Liang

Many pairwise classification tasks, such as paraphrase detection and open-domain question answering, naturally have extreme label imbalance (e. g., $99. 99\%$ of examples are negatives).

Active Learning Open-Domain Question Answering +1

Simplifying Models with Unlabeled Output Data

no code implementations28 Sep 2020 Sang Michael Xie, Tengyu Ma, Percy Liang

We focus on prediction problems with high-dimensional outputs that are subject to output validity constraints, e. g. a pseudocode-to-code translation task where the code must compile.

Code Translation Image Generation +2

Decoupling Exploration and Exploitation for Meta-Reinforcement Learning without Sacrifices

2 code implementations6 Aug 2020 Evan Zheran Liu, aditi raghunathan, Percy Liang, Chelsea Finn

Learning a new task often requires both exploring to gather task-relevant information and exploiting this information to solve the task.

Meta Reinforcement Learning reinforcement-learning +2

Robustness to Spurious Correlations via Human Annotations

1 code implementation ICML 2020 Megha Srivastava, Tatsunori Hashimoto, Percy Liang

The reliability of machine learning systems critically assumes that the associations between features and labels remain similar between training and test distributions.

Common Sense Reasoning

Learning Abstract Models for Strategic Exploration and Fast Reward Transfer

1 code implementation12 Jul 2020 Evan Zheran Liu, Ramtin Keramati, Sudarshan Seshadri, Kelvin Guu, Panupong Pasupat, Emma Brunskill, Percy Liang

Model-based reinforcement learning (RL) is appealing because (i) it enables planning and thus more strategic exploration, and (ii) by decoupling dynamics from rewards, it enables fast transfer to new reward functions.

Model-based Reinforcement Learning Montezuma's Revenge +2

Concept Bottleneck Models

4 code implementations ICML 2020 Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, Percy Liang

We seek to learn models that we can interact with using high-level concepts: if the model did not think there was a bone spur in the x-ray, would it still predict severe arthritis?

Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization

2 code implementations29 Jun 2020 Sang Michael Xie, Tengyu Ma, Percy Liang

Empirically, we show that composed fine-tuning improves over standard fine-tuning on two pseudocode-to-code translation datasets (3% and 6% relative).

Code Translation Denoising +2

Selective Question Answering under Domain Shift

2 code implementations ACL 2020 Amita Kamath, Robin Jia, Percy Liang

In this work, we propose the setting of selective question answering under domain shift, in which a QA model is tested on a mixture of in-domain and out-of-domain data, and must answer (i. e., not abstain on) as many questions as possible while maintaining high accuracy.

Question Answering

Explore then Execute: Adapting without Rewards via Factorized Meta-Reinforcement Learning

no code implementations ICML Workshop LifelongML 2020 Evan Zheran Liu, aditi raghunathan, Percy Liang, Chelsea Finn

In principle, meta-reinforcement learning approaches can exploit this shared structure, but in practice, they fail to adapt to new environments when adaptation requires targeted exploration (e. g., exploring the cabinets to find ingredients in a new kitchen).

Meta Reinforcement Learning reinforcement-learning +2

Graph-based, Self-Supervised Program Repair from Diagnostic Feedback

2 code implementations ICML 2020 Michihiro Yasunaga, Percy Liang

Second, we present a self-supervised learning paradigm for program repair that leverages unlabeled programs available online to create a large amount of extra program repair examples, which we use to pre-train our models.

Code Generation Graph Learning +3

Enabling Language Models to Fill in the Blanks

3 code implementations ACL 2020 Chris Donahue, Mina Lee, Percy Liang

We show that this approach, which we call infilling by language modeling, can enable LMs to infill entire sentences effectively on three different domains: short stories, scientific abstracts, and lyrics.

Language Modelling Text Infilling

An Investigation of Why Overparameterization Exacerbates Spurious Correlations

3 code implementations9 May 2020 Shiori Sagawa, aditi raghunathan, Pang Wei Koh, Percy Liang

We study why overparameterization -- increasing model size well beyond the point of zero training error -- can hurt test error on minority groups despite improving average test error when there are spurious correlations in the data.

Inductive Bias

ExpBERT: Representation Engineering with Natural Language Explanations

2 code implementations ACL 2020 Shikhar Murty, Pang Wei Koh, Percy Liang

Suppose we want to specify the inductive bias that married couples typically go on honeymoons for the task of extracting pairs of spouses from text.

Inductive Bias Relation Extraction +1

Distributionally Robust Neural Networks

1 code implementation ICLR 2020 Shiori Sagawa*, Pang Wei Koh*, Tatsunori B. Hashimoto, Percy Liang

Distributionally robust optimization (DRO) allows us to learn models that instead minimize the worst-case training loss over a set of pre-defined groups.

L2 Regularization Natural Language Inference +1

Understanding Self-Training for Gradual Domain Adaptation

2 code implementations ICML 2020 Ananya Kumar, Tengyu Ma, Percy Liang

Machine learning systems must adapt to data distributions that evolve over time, in applications ranging from sensor networks and self-driving car perception modules to brain-machine interfaces.

Unsupervised Domain Adaptation

Understanding and Mitigating the Tradeoff Between Robustness and Accuracy

1 code implementation ICML 2020 Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, Percy Liang

In this work, we precisely characterize the effect of augmentation on the standard error in linear regression when the optimal linear predictor has zero standard and robust error.


Feature Noise Induces Loss Discrepancy Across Groups

1 code implementation ICML 2020 Fereshte Khani, Percy Liang

Our main result is that even when there is no information deficiency specific to one group (e. g., both groups have infinite data), adding the same amount of feature noise to all individuals leads to loss discrepancy.


Learning Autocomplete Systems as a Communication Game

1 code implementation16 Nov 2019 Mina Lee, Tatsunori B. Hashimoto, Percy Liang

We study textual autocomplete---the task of predicting a full sentence from a partial sentence---as a human-machine communication game.