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 10119 publications
Can Query Expansion Improve Generalization of Strong Cross-Encoder Rankers?
Minghan Li
Jimmy Lin
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’24) (2024)
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Query expansion has been widely used to improve the search results of first-stage retrievers, yet its influence on second-stage, crossencoder rankers remains under-explored. A recent study shows that current expansion techniques benefit weaker models but harm stronger rankers. In this paper, we re-examine this conclusion and raise the following question: Can query expansion improve generalization of strong cross-encoder rankers? To answer this question, we first apply popular query expansion methods to different crossencoder rankers and verify the deteriorated zero-shot effectiveness. We identify two vital steps in the experiment: high-quality keyword generation and minimally-disruptive query modification. We show that it is possible to improve the generalization of a strong neural ranker, by generating keywords through a reasoning chain and aggregating the ranking results of each expanded query via selfconsistency, reciprocal rank weighting, and fusion. Experiments on BEIR and TREC Deep Learning 2019/2020 show that the nDCG@10 scores of both MonoT5 and RankT5 following these steps are improved, which points out a direction for applying query expansion to strong cross-encoder rankers.
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A Setwise Approach for Effective and Highly Efficient Zero-shot Ranking with Large Language Models
Shengyao Zhuang
Bevan Koopman
Guido Zuccon
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’24) (2024)
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We propose a novel zero-shot document ranking approach based on Large Language Models (LLMs): the Setwise prompting approach. Our approach complements existing prompting approaches for LLM-based zero-shot ranking: Pointwise, Pairwise, and Listwise. Through the first-of-its-kind comparative evaluation within a consistent experimental framework and considering factors like model size, token consumption, latency, among others, we show that existing approaches are inherently characterised by trade-offs between effectiveness and efficiency. We find that while Pointwise approaches score high on efficiency, they suffer from poor effectiveness. Conversely, Pairwise approaches demonstrate superior effectiveness but incur high computational overhead. Our Setwise approach, instead, reduces the number of LLM inferences and the amount of prompt token consumption during the ranking procedure, compared to previous methods. This significantly improves the efficiency of LLM-based zero-shot ranking, while also retaining high zero-shot ranking effectiveness. We make our code and results publicly available at https://github.com/ielab/llm-rankers.
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Generalizing Tree-Level Sap Flow Across the European Continent
Ralf Loritz
Chen Huan Wu
Daniel Klotz
Martin Gauch
Frederik Kratzert
Maoya Bassiouni
Geophysical Research Letters (2024)
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Sap flow offers key insights about transpiration dynamics and forest-climate interactions. Accurately simulating sap flow remains challenging due to measurement uncertainties and interactions between global and local environmental controls. Addressing these complexities, this study leveraged Long Short-Term Memory networks (LSTMs) with SAPFLUXNET to predict hourly tree-level sap flow across Europe. We built models with diverse training sets to assess performance under previously unseen conditions. The average Kling-Gupta Efficiency was 0.77 for models trained on 50% of time series across all forest stands, and 0.52 for models trained on 50% of the forest stands. Continental models not only matched but surpassed the performance of specialized and baselines for all genera and forest types, showcasing the capacity of LSTMs to effectively generalize across tree genera, climates, and forest ecosystems given minimal inputs. This study underscores the potential of LSTMs in generalizing state-dependent ecohydrological processes and bridging tree level measurements to continental scales.
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Reinforcement can be a useful tool to solve combinatorial problems, even in the presence of constraints. This presentation details two use cases: one industrial application in the field of logistics, one of a more abstract problem in combinatorial optimization.
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AI-powered software development tooling is changing the way that developers interact with tools and write code. However, the ability for AI to truly transform software development depends on developers' level of trust in the tools. In this work, we take a mixed methods approach to measuring the factors that influence developers' trust in AI-powered code completion. We identified that familiarity with AI suggestions, quality of the suggestion, and level of expertise with the language all increased acceptance rate of AI-powered suggestions. While suggestion length and presence in a test file decreased acceptance rates. Based on these findings we propose recommendations for the design of AI-powered development tools to improve trust.
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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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Characterizing a Memory Allocator at Warehouse Scale
Zhuangzhuang Zhou
Nilay Vaish
Patrick Xia
Christina Delimitrou
Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 3, Association for Computing Machinery, La Jolla, CA, USA (2024), 192–206
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Memory allocation constitutes a substantial component of warehouse-scale computation. Optimizing the memory allocator not only reduces the datacenter tax, but also improves application performance, leading to significant cost savings.
We present the first comprehensive characterization study of TCMalloc, a warehouse-scale memory allocator used in our production fleet. Our characterization reveals a profound diversity in the memory allocation patterns, allocated object sizes and lifetimes, for large-scale datacenter workloads, as well as in their performance on heterogeneous hardware platforms. Based on these insights, we redesign TCMalloc for warehouse-scale environments. Specifically, we propose optimizations for each level of its cache hierarchy that include usage-based dynamic sizing of allocator caches, leveraging hardware topology to mitigate inter-core communication overhead, and improving allocation packing algorithms based on statistical data. We evaluate these design choices using benchmarks and fleet-wide A/B experiments in our production fleet, resulting in a 1.4% improvement in throughput and a 3.4% reduction in RAM usage for the entire fleet. At our scale, even a single percent CPU or memory improvement translates to significant savings in server costs.
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We focus on the problem of learning without forgetting from multiple tasks arriving sequentially, where each task is defined using a few-shot episode of novel or already seen classes. We approach this problem using the recently published HyperTransformer (HT), a Transformer-based hypernetwork that generates specialized task-specific CNN weights directly from the support set. In order to learn from a continual sequence of tasks, we propose to recursively re-use the generated weights as input to the HT for the next task. This way, the generated CNN weights themselves act as a representation of previously learned tasks, and the HT is trained to update these weights so that the new task can be learned without forgetting past tasks. This approach is different from most continual learning algorithms that typically rely on using replay buffers, weight regularization or task-dependent architectural changes. We demonstrate that our proposed Continual HyperTransformer method equipped with a prototypical loss is capable of learning and retaining knowledge about past tasks for a variety of scenarios, including learning from mini-batches, and task-incremental and class-incremental learning scenarios.
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Optimizing quantum gates towards the scale of logical qubits
Alexandre Bourassa
Andrew Dunsworth
Will Livingston
Vlad Sivak
Trond Andersen
Yaxing Zhang
Desmond Chik
Jimmy Chen
Charles Neill
Alejo Grajales Dau
Anthony Megrant
Alexander Korotkov
Vadim Smelyanskiy
Yu Chen
Nature Communications, 15 (2024), pp. 2442
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A foundational assumption of quantum error correction theory is that quantum gates can be scaled to large processors without exceeding the error-threshold for fault tolerance. Two major challenges that could become fundamental roadblocks are manufacturing high-performance quantum hardware and engineering a control system that can reach its performance limits. The control challenge of scaling quantum gates from small to large processors without degrading performance often maps to non-convex, high-constraint, and time-dynamic control optimization over an exponentially expanding configuration space. Here we report on a control optimization strategy that can scalably overcome the complexity of such problems. We demonstrate it by choreographing the frequency trajectories of 68 frequency-tunable superconducting qubits to execute single- and two-qubit gates while mitigating computational errors. When combined with a comprehensive model of physical errors across our processor, the strategy suppresses physical error rates by ~3.7× compared with the case of no optimization. Furthermore, it is projected to achieve a similar performance advantage on a distance-23 surface code logical qubit with 1057 physical qubits. Our control optimization strategy solves a generic scaling challenge in a way that can be adapted to a variety of quantum operations, algorithms, and computing architectures.
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In-DRAM Stochastic and Approximate Counting (DSAC) is a recently published algorithm that aims to mitigate Rowhammer at low cost. Existing in-DRAM counter-based schemes keep track of row activations and issue Targeted Row Refresh (TRR) upon detecting a concerning pattern. However, due to insufficiency of the tracking ability they are vulnerable to attacks utilizing decoy rows. DSAC claims to improve upon existing TRR mitigation by filtering out decoy-row accesses, so they cannot saturate the limited number of counters available for detecting Rowhammer, promising a reliable mitigation without the area cost of deterministic and provable schemes such as per-row activation counting (PRAC).
In this paper, we analyze DSAC and discover some gaps that make it vulnerable to Rowhammer and Rowpress attacks. The main focus of this work is a novel attack named SoothSayer that targets the counter replacement policy in DSAC by cloning the random number generator. We describe and simulate this attack, and establish its efficacy. Finally, we discuss other weaknesses in DSAC.
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Securing the AI Software Supply Chain
Isaac Hepworth
Kara Olive
Kingshuk Dasgupta
Michael Le
Mark Lodato
Mihai Maruseac
Sarah Meiklejohn
Shamik Chaudhuri
Tehila Minkus
Google, Google, 1600 Amphitheatre Parkway, Mountain View, CA, 94043 (2024)
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As AI-powered features gain traction in software applications, we see many of the same problems we’ve faced with traditional software—but at an accelerated pace. The threat landscape continues to expand as AI is further integrated into everyday products, so we can expect more attacks. Given the expense of building models, there is a clear need for supply chain solutions.
This paper explains our approach to securing our AI supply chain using provenance information and provides guidance for other organizations. Although there are differences between traditional and AI development processes and risks, we can build on our work over the past decade using Binary Authorization for Borg (BAB), Supply-chain Levels for Software Artifacts (SLSA), and next-generation cryptographic signing solutions via Sigstore, and adapt these to the AI supply chain without reinventing the wheel. Depending on internal processes and platforms, each organization’s approach to AI supply chain security will look different, but the focus should be on areas where it can be improved in a relatively short time.
Readers should note that the first part of this paper provides a broad overview of “Development lifecycles for traditional and AI software”. Then we delve specifically into AI supply chain risks, and explain our approach to securing our AI supply chain using provenance information. More advanced practitioners may prefer to go directly to the sections on “AI supply chain risks,” “Controls for AI supply chain security,” or even the “Guidance for practitioners” section at the end of the paper, which can be adapted to the needs of any organization.
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"We Need Structured Output": Towards User-centered Constraints on Large Language Model Output
Michael Xieyang Liu
Frederick Liu
Alex Fiannaca
Terry Koo
In Extended Abstract in ACM CHI Conference on Human Factors in Computing Systems (CHI EA '24), ACM (2024), pp. 9 (to appear)
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Large language models can produce creative and diverse responses. However, to integrate them into current developer workflows, it is essential to constrain their outputs to follow specific formats or standards. In this work, we surveyed 51 experienced industry professionals to understand the range of scenarios and motivations driving the need for output constraints from a user-centered perspective. We identified 134 concrete use cases for constraints at two levels: low-level, which ensures the output adhere to a structured format and an appropriate length, and high-level, which requires the output to follow semantic and stylistic guidelines without hallucination. Critically, applying output constraints could not only streamline the currently repetitive process of developing, testing, and integrating LLM prompts for developers, but also enhance the user experience of LLM-powered features and applications. We conclude with a discussion on user preferences and needs towards articulating intended constraints for LLMs, alongside an initial design for a constraint prototyping tool.
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Visual Grounding for User Interfaces
Yijun Qian
Yujie Lu
Alexander G. Hauptmann
2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2024) - Industry Track
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Enabling autonomous language agents to drive application user interfaces (UIs) as humans do can significantly expand the capability of today's API-based agents. Essential to this vision is the ability of agents to ground natural language commands to on-screen UI elements. Prior UI grounding approaches work by relaying on developer-provided UI metadata (UI trees, such as web DOM, and accessibility labels) to detect on-screen elements. However, such metadata is often unavailable or incomplete. Object detection techniques applied to UI screens remove this dependency, by inferring location and types of UI elements directly from the UI's visual appearance. The extracted semantics, however, are too limited to directly enable grounding. We overcome the limitations of both approaches by introducing the task of visual UI grounding, which unifies detection and grounding. A model takes as input a UI screenshot and a free-form language expression, and must identify the referenced UI element. We propose a solution to this problem, LVG, which learns UI element detection and grounding using a new technique called layout-guided contrastive learning, where the semantics of individual UI objects are learned also from their visual organization. Due to the scarcity of UI datasets, LVG integrates synthetic data in its training using multi-context learning. LVG outperforms baselines pre-trained on much larger datasets by over 4.9 points in top-1 accuracy, thus demonstrating its effectiveness.
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Minimizing Live Experiments in Recommender Systems: User Simulation to Evaluate Preference Elicitation Policies
Martin Mladenov
James Pine
Hubert Pham
Shane Li
Xujian Liang
Anton Polishko
Li Yang
Ben Scheetz
Proceedings of he 47th International ACM/SIGIR Conference on Research and Development in Information Retrieval (SIGIR-24), Washington, DC (2024), pp. 2925-2929
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Evaluation of policies in recommender systems (RSs) typically involves A/B testing using live experiments on real users to assess a new policy's impact on relevant metrics. This ``gold standard'' comes at a high cost, however, in terms of cycle time, user cost, and potential user retention. In developing policies for onboarding new users, these costs can be especially problematic, since on-boarding occurs only once. In this work, we describe a simulation methodology used to augment (and reduce) the use of live experiments. We illustrate its deployment for the evaluation of preference elicitation algorithms used to onboard new users of the YouTube Music platform. By developing counterfactually robust user behavior models, and a simulation service that couples such models with production infrastructure, we are able to test new algorithms in a way that reliably predicts their performance on key metrics when deployed live, sometimes more reliably than live experiments due to the scale at which simulation can be realized. We describe our domain, our simulation models and platform, results of experiments and deployment, and suggest future steps needed to further realistic simulation as a powerful complement to live experiments.
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