Publications
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
Our teams aspire to make discoveries that impact everyone, and core to our approach is sharing our research and tools to fuel progress in the field.
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1 - 15 of 10093 publications
PaLI-X: On Scaling up a Multilingual Vision and Language Model
Josip Djolonga
Piotr Padlewski
Basil Mustafa
Carlos Riquelme
Sebastian Goodman
Yi Tay
Siamak Shakeri
Daniel Salz
Michael Tschannen
Mandar Joshi
Filip Pavetić
Gang Li
Anurag Arnab
Yuanzhong Xu
Keran Rong
Neil Houlsby
Computer Vision and Pattern Recognition Conference (CVPR) (2024)
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We explore the boundaries of scaling up a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-range of varied and complex tasks, including multiple image-based captioning and question-answering tasks, image-based document understanding and few-shot (in-context) learning, as well as object detection, video question answering, and video captioning. Our model advances the state-of-the-art on most vision-and-language benchmarks considered (20+ of them). Finally, we observe emerging capabilities, such as complex counting and multilingual object detection, tasks that are not explicitly in the training mix.
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Bridging the Preference Gap between Retrievers and LLMs
Zixuan Ke
Qiaozhu Mei
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (2024) (to appear)
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Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the performance by locating relevant information and placing it into the context window of the LLM. However, the relationship between retrievers and LLM in a RAG is still under-investigated. Most existing work treats the retriever and the LLM as independent components and leaves a gap between retrieving human-"friendly" information and assembling a LLM-"friendly" context. In this work, we examine a novel bridge mechanism. We validate the ranking and selection assumptions of retrievers in the context of RAG and propose a framework that chains together supervised and reinforcement learning to train a bridge model that optimizes the connection between the retriever and the LLM. Empirical results demonstrate the effectiveness of our method in both question-answering and personalized generation tasks.
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Stable quantum-correlated many-body states through engineered dissipation
Xiao Mi
Alexios Michailidis
Sara Shabani
Jerome Lloyd
Rajeev Acharya
Igor Aleiner
Trond Andersen
Markus Ansmann
Frank Arute
Kunal Arya
Juan Atalaya
Gina Bortoli
Alexandre Bourassa
Leon Brill
Michael Broughton
Bob Buckley
Tim Burger
Nicholas Bushnell
Jimmy Chen
Benjamin Chiaro
Desmond Chik
Charina Chou
Josh Cogan
Roberto Collins
Paul Conner
William Courtney
Alex Crook
Ben Curtin
Alejo Grajales Dau
Dripto Debroy
Agustin Di Paolo
ILYA Drozdov
Andrew Dunsworth
Lara Faoro
Edward Farhi
Reza Fatemi
Vinicius Ferreira
Ebrahim Forati
Brooks Foxen
Élie Genois
William Giang
Dar Gilboa
Raja Gosula
Steve Habegger
Michael Hamilton
Monica Hansen
Sean Harrington
Paula Heu
Markus Hoffmann
Trent Huang
Ashley Huff
Bill Huggins
Sergei Isakov
Justin Iveland
Cody Jones
Pavol Juhas
Kostyantyn Kechedzhi
Marika Kieferova
Alexei Kitaev
Andrey Klots
Alexander Korotkov
Fedor Kostritsa
John Mark Kreikebaum
Dave Landhuis
Pavel Laptev
Kim Ming Lau
Lily Laws
Joonho Lee
Kenny Lee
Yuri Lensky
Alexander Lill
Wayne Liu
Orion Martin
Amanda Mieszala
Shirin Montazeri
Alexis Morvan
Ramis Movassagh
Wojtek Mruczkiewicz
Charles Neill
Ani Nersisyan
Michael Newman
JiunHow Ng
Murray Ich Nguyen
Tom O'Brien
Alex Opremcak
Andre Petukhov
Rebecca Potter
Leonid Pryadko
Charles Rocque
Negar Saei
Kannan Sankaragomathi
Henry Schurkus
Christopher Schuster
Mike Shearn
Aaron Shorter
Noah Shutty
Vladimir Shvarts
Jindra Skruzny
Clarke Smith
Rolando Somma
George Sterling
Doug Strain
Marco Szalay
Alfredo Torres
Guifre Vidal
Cheng Xing
Jamie Yao
Ping Yeh
Juhwan Yoo
Grayson Young
Yaxing Zhang
Ningfeng Zhu
Jeremy Hilton
Anthony Megrant
Yu Chen
Vadim Smelyanskiy
Dmitry Abanin
Science, 383 (2024), pp. 1332-1337
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Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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Do Large Code Models Understand Programming Concepts? A Black Box Approach
Ashish Hooda
Aaron Wilson
Kassem Fawaz
Somesh Jha
(2024) (to appear)
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Large Language Models have been able to replicate their success from text generation to coding tasks. While a lot of work has made it clear that they have remarkable performance on tasks such as code completion and editing, it is still unclear as to why. We help bridge this gap by exploring to what degree do auto-regressive models understand the logical constructs of the underlying programs. We propose CAPP, a counterfactual testing framework to evaluate whether large code models understand programming concepts. With only black-box access to the model, we use CAPP to evaluate 10 popular large code models for 5 different programming concepts. Our findings suggest that current models lack understanding of concepts such as data flow and control flow.
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RewriteLM: An Instruction-Tuned Large LanguageModel for Text Rewriting
Yun Zhu
Simon Tong
Lei Meng
Proceedings of the AAAI Conference on Artificial Intelligence, 38(17), 18970-18980 (2024)
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In recent years, Large Language Models (LLMs) have demonstrated impressive zero-shot capabilities in text generation tasks expressed through natural language instructions. However, text rewriting is a challenging task, and unintended modifications can negatively impact the system's performance. To address this challenge, we introduce a novel benchmark for text rewriting that covers a wide variety of rewriting types expressed through natural language instructions. Unlike previous benchmarks, which were primarily focused on limited rewrite styles and sentence-level rewriting, our benchmark is specifically designed to facilitate open-ended rewriting of long-form text. Additionally, we present a strong baseline model, RewriteLM, which is an instruction-tuned large language model for text rewriting. The model is trained using supervised fine-tuning, reward training, and reinforcement learning. To minimize human intervention in the data collection process, we develop new data generation strategies: (1) utilizing high-quality, long-form edits from Wikipedia as our primary natural training data source, (2) generating a synthetic dataset that includes diverse edit types and non-Wiki domains using chain-of-thoughts and the capabilities of LLMs, and (3) employing human-designed heuristic rankers to generate preference data. Our experiments demonstrate the effectiveness of our proposed benchmark and baseline model, as well as the benefits of our data collection strategies in minimizing human intervention.
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Structured Complex Task Decomposition (SCTD) is the problem of breaking down a complex real-world task (such as planning a wedding) into a directed acyclic graph over individual steps that contribute to achieving the task, with edges specifying temporal dependencies between them. SCTD is an important component of assistive planning tools, and a challenge for commonsense reasoning systems. We probe how accurately SCTD can be done with the knowledge extracted from Large Language Models (LLMs). We introduce a high-quality human-annotated dataset for this problem and novel metrics to fairly assess performance of LLMs against several baselines. Our experiments reveal that LLMs are able to decompose complex tasks into individual steps effectively, with a relative improvement of 15% to 280% over the best baseline. We also propose a number of approaches to further improve their performance, with a relative improvement of 7% to 37% over the base model. However, we find that LLMs still struggle to predict pairwise temporal dependencies, which reveals a gap in their understanding of complex tasks.
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What is it to explain the outputs of an opaque machine learning model? Popular strategies in the literature are to develop
explainable machine learning techniques. These techniques approximate how the model works by providing local or global
information about the inner workings of a machine learning model. In this paper, we argue that, in some cases, explaining
machine learning outputs requires appealing to the third kind of explanation that we call socio-structural explanations.
The importance of socio-structural explanations is motivated by the observation that machine learning models are not
autonomous mathematico-computational entities. Instead, their very existence is intrinsically tied to the social context in
which they operate. Sometimes, the social structures are mirrored in the design and training of machine learning models
and hence appealing to the socio-structural explanations offers the relevant explanation for why the output is obtained.
By thoroughly examining a well-known case of racially biased algorithmic resource allocation in healthcare, we highlight
the significance of socio-structural explanations. One ramification of our proposal is that to understand how machine
learning models perpetuate unjust social harms, more is needed to interpret them by model interpretability methods.
Instead, providing socio-structural explanations adds explanatory adequacy as to how and why machine learning outputs
are obtained
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One of the most basic problems for studying the "price of privacy over time" is the so called private counter problem, introduced by Dwork et al. (2010) and Chan et al. (2011). In this problem, we aim to track the number of events that occur over time, while hiding the existence of every single event. More specifically, in every time step $t\in[T]$ we learn (in an online fashion) that $\Delta_t\geq 0$ new events have occurred, and must respond with an estimate $n_t\approx\sum_{j=1}^t \Delta_j$. The privacy requirement is that all of the outputs together, across all time steps, satisfy event level differential privacy.
The main question here is how our error needs to depend on the total number of time steps $T$ and the total number of events $n$. Dwork et al. (2015) showed an upper bound of $O\left(\log(T)+\log^2(n)\right)$, and Henzinger et al. (2023) showed a lower bound of $\Omega\left(\min\{\log n, \log T\}\right)$. We show a new lower bound of $\Omega\left(\min\{n,\log T\}\right)$, which is tight w.r.t. the dependence on $T$, and is tight in the sparse case where $\log^2 n=O(\log T)$. Our lower bound has the following implications:
* We show that our lower bound extends to the online thresholds problem, where the goal is to privately answer many "quantile queries" when these queries are presented one-by-one. This resolves an open question of Bun et al. (2017).
* Our lower bound implies, for the first time, a separation between the number of mistakes obtainable by a private online learner and a non-private online learner. This partially resolves a COLT'22 open question published by Sanyal and Ramponi.
* Our lower bound also yields the first separation between the standard model of private online learning and a recently proposed relaxed variant of it, called private online prediction.
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Specifying BGP using TLA+
Aman Shaikh
(2024)
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This presentation is about the TLA+ specification we have written for BGP, the routing protocol underpinning the Internet. The specification also serves as a crucial first-step towards the use of TLA+ for verification of network designs.
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Analyzing Prospects for Quantum Advantage in Topological Data Analysis
Dominic W. Berry
Yuan Su
Casper Gyurik
Robbie King
Joao Basso
Abhishek Rajput
Nathan Wiebe
Vedran Djunko
PRX Quantum, 5 (2024), pp. 010319
Preview abstract
Lloyd et al. were first to demonstrate the promise of quantum algorithms for computing Betti numbers in persistent homology (a way of characterizing topological features of data sets). Here, we propose, analyze, and optimize an improved quantum algorithm for topological data analysis (TDA) with reduced scaling, including a method for preparing Dicke states based on inequality testing, a more efficient amplitude estimation algorithm using Kaiser windows, and an optimal implementation of eigenvalue projectors based on Chebyshev polynomials. We compile our approach to a fault-tolerant gate set and estimate constant factors in the Toffoli complexity. Our analysis reveals that super-quadratic quantum speedups are only possible for this problem when targeting a multiplicative error approximation and the Betti number grows asymptotically. Further, we propose a dequantization of the quantum TDA algorithm that shows that having exponentially large dimension and Betti number are necessary, but insufficient conditions, for super-polynomial advantage. We then introduce and analyze specific problem examples for which super-polynomial advantages may be achieved, and argue that quantum circuits with tens of billions of Toffoli gates can solve some seemingly classically intractable instances.
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Reinforcement Learning-Enhanced Cloud-Based Open Source Analog Circuit Generator for Standard and Cryogenic Temperatures in 130-nm and 180-nm OpenPDKs
Ali Hammoud
Anhang Li
Ayushman Tripathi
Wen Tian
Harsh Khandeparkar
Ryan Wans
Boris Murmann
Dennis Sylvester
Mehdi Saligane
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This work introduces an open-source, Process Technology-agnostic framework for hierarchical circuit netlist, layout, and Reinforcement Learning (RL) optimization. The layout, netlist, and optimization python API is fully modular and publicly installable via PyPI. It features a bottom-up hierarchical construction, which allows for complete design reuse across provided PDKs. The modular hierarchy also facilitates parallel circuit design iterations on cloud platforms. To illustrate its capabilities, a two-stage OpAmp with a 5T first-stage, commonsource second-stage, and miller compensation is implemented. We instantiate the OpAmp in two different open-source process design kits (OpenPDKs) using both room-temperature models and cryogenic (4K) models. With a human designed version as the baseline, we leveraged the parameterization capabilities of the framework and applied the RL optimizer to adapt to the power consumption limits suitable for cryogenic applications while maintaining gain and bandwidth performance. Using the modular RL optimization framework we achieve a 6x reduction in power consumption compared to manually designed circuits while maintaining gain to within 2%.
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ImageInWords: Unlocking Hyper-Detailed Image Descriptions
Andrew Bunner
Ranjay Krishna
(2024)
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Despite the longstanding adage "an image is worth a thousand words," creating accurate and hyper-detailed image descriptions for training Vision-Language models remains challenging.
Current datasets typically have web-scraped descriptions that are short, low-granularity, and often contain details unrelated to the visual content. As a result, models trained on such data generate descriptions replete with missing information, visual inconsistencies, and hallucinations. To address these issues, we introduce ImageInWords (IIW), a carefully designed human-in-the-loop annotation framework for curating hyper-detailed image descriptions and a new dataset resulting from this process.
We validate the framework through evaluations focused on the quality of the dataset and its utility for fine-tuning with considerations for readability, comprehensiveness, specificity, hallucinations, and human-likeness. Our dataset significantly improves across these dimensions compared to recently released datasets (+66%) and GPT-4V outputs (+48%). Furthermore, models fine-tuned with IIW data excel by +31% against prior work along the same human evaluation dimensions. Given our fine-tuned models, we also evaluate text-to-image generation and vision-language reasoning. Our model's descriptions can generate images closest to the original, as judged by both automated and human metrics. We also find our model produces more compositionally rich descriptions, outperforming the best baseline by up to 6% on ARO, SVO-Probes, and Winoground datasets.
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Detecting offensive content in text is an increasingly central challenge for both social-media platforms and AI-driven technologies. However offensiveness remains a subjective phenomenon as perspectives differ across sociodemographic characteristics, as well as cultural norms and moral values. This intricacy is largely ignored in the current AI-focused approaches for detecting offensiveness or related concepts such as hate speech and toxicity detection. We frame the task of determining offensiveness as essentially a matter of moral judgment --- deciding the boundaries of ethically wrong vs. right language to be used or generated within an implied set of sociocultural norms. In this paper, we investigate how judgment of offensiveness varies across diverse global cultural regions, and the crucial role of moral values in shaping these variations. Our findings highlight substantial cross-cultural differences in perceiving offensiveness, with moral concerns about Caring and Purity as the mediating factor driving these differences. These insights are of importance as AI safety protocols, shaped by human annotators' inputs and perspectives, embed their moral values which do not align with the notions of right and wrong in all contexts, and for all individuals.
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Creativity, Generative AI, and Software Development: A Research Agenda
Victoria Jackson
Bogdan Vasilescu
Daniel Russo
Paul Ralph
Maliheh Izadi
Rafael Prikladnicki
Anielle Lisboa
Andre van der Hoek
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Creativity has always been considered a major differentiator to separate the good from the great, and we believe the importance of creativity to software development will only increase as GenAI becomes embedded in developer tool-chains and working practices. This paper uses the McLuhan tetrad alongside scenarios of how GenAI may disrupt software development more broadly, to identify potential impacts GenAI may have on creativity within software development. The impacts are discussed along with a future research agenda comprising of six connected themes that consider how individual capabilities, team capabilities, the product, unintended consequences, society, and human aspects can be affected.
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HyperAttention: Large-scale Attention in Linear Time
Amin Karbasi
Amir Zandieh
Insu Han
David Woodruff
HyperAttention: Long-context Attention in Near-Linear Time (2024) (to appear)
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In this paper, we introduce a novel approximate attention mechanism dubbed ``HyperAttention``. Despite the rapidly increasing size and complexity of contexts used with Large Language Models (LLM), there is still a dire lack of computationally efficient attention mechanisms scaling better than the naive quadratic time. HyperAttention addresses this gap: it delivers provably linear time complexity with respect to the size of the context, while only incurring a negligible loss in downstream quality. Distinctively, it integrates the principles of Locality Sensitive Hashing (LSH), for efficient detection of heavy elements, along with uniform column sampling, allowing for a good approximation both of the heavy and light components of the attention matrix. HyperAttention provably approximates the attention layer in \textit{linear time}, making it the first practical linear time approximate attention mechanism. Crucially, HyperAttention has a highly-modular design, allowing seamless integration of other rapid low-level implementations, most notably FlashAttention. Empirical evaluations indicate that HyperAttention surpasses the existing methods, achieving orders of magnitude speed-up when compared to prevalent state-of-the-art solutions such as Flash Attention. This breakthrough presents significant implications for enabling the scalability of LLMs to significantly larger contexts.
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