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 10081 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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DOCCI: Descriptions of Connected and Contrasting Images
Garrett Tanzer
Jaemin Cho
Su Wang
Sunayana Rane
Zack Berger
Zarana Parekh
(2024)
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Despite recent advancements, text-to-image (T2I) models still exhibit critical limitations, such as errors in understanding spatial relationships, object counting, text rendering, and more. One challenge in overcoming these failure modes is the lack of resources; the majority of existing image-text datasets provide only brief captions that do not offer sufficient detail to discrepancies between images and their descriptions. To advance the development of T2I models further, we introduce \textbf{Descriptions of Connected and Contrasting Images (DOCCI)}, a dataset of 15k images taken by a single person with detailed human-annotated descriptions in English. We meticulously annotated detailed and coherent descriptions, averaging 136 words, which sufficiently differentiate images from related or similar ones. We intentionally curated images that showcase a diverse range of visual properties, including entities with their attributes, various orientations, and lighting effects, many of which are related to each other. We thoroughly analyze the quality and characteristics of the image-description pairs, and assess the performance of the latest T2I and I2T models. The experimental results indicate that the current state-of-the-art T2I models still struggle with the aforementioned challenges, and even the SOTA models have not fully addressed them. DOCCI is publicly available, and we believe that this dataset will be a valuable benchmark for vision-language research.
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We present an approach to modeling an image-space prior on scene motion. Our prior is learned from a collection of motion trajectories extracted from real video sequences depicting natural, oscillatory dynamics such as trees, flowers, candles, and clothes swaying in the wind. We model this dense, long-term motion prior in the Fourier domain:given a single image, our trained model uses a frequency-coordinated diffusion sampling process to predict a spectral volume, which can be converted into a motion texture that spans an entire video. Along with an image-based rendering module, these trajectories can be used for a number of downstream applications, such as turning still images into seamlessly looping videos, or allowing users to realistically interact with objects in real pictures by interpreting the spectral volumes as image-space modal bases, which approximate object dynamics.
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ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation
Akshita Jha
Sarah Laszlo
Rida Qadri
Chandan Reddy
ACL (2024)
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Recent studies have highlighted the issue of varying degrees of stereotypical depictions for different identity group. However, these existing approaches have several key limitations, including a noticeable lack of coverage of identity groups in their evaluation, and the range of their associated stereotypes. Additionally, these studies often lack a critical distinction between inherently visual stereotypes, such as `brown' or `sombrero', and culturally influenced stereotypes like `kind' or `intelligent'. In this work, we address these limitations by grounding our evaluation of regional, geo-cultural stereotypes in the generated images from Text-to-Image models by leveraging existing textual resources. We employ existing stereotype benchmarks to evaluate stereotypes and focus exclusively on the identification of visual stereotypes within the generated images spanning 135 identity groups. We also compute the offensiveness across identity groups, and check the feasibility of identifying stereotypes automatically. Further, through a detailed case study and quantitative analysis, we reveal how the default representations of all identity groups have a more stereotypical appearance, and for historically marginalized groups, how the images across different attributes are visually more similar than other groups, even when explicitly prompted otherwise.
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Quantifying urban park use in the USA at scale: empirical estimates of realised park usage using smartphone location data
Michael T Young
Swapnil Vispute
Stylianos Serghiou
Akim Kumok
Yash Shah
Kevin J. Lane
Flannery Black-Ingersoll
Paige Brochu
Monica Bharel
Sarah Skenazy
Shailesh Bavadekar
Mansi Kansal
Evgeniy Gabrilovich
Gregory A. Wellenius
Lancet Planetary Health (2024)
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Summary
Background A large body of evidence connects access to greenspace with substantial benefits to physical and mental
health. In urban settings where access to greenspace can be limited, park access and use have been associated with
higher levels of physical activity, improved physical health, and lower levels of markers of mental distress. Despite the
potential health benefits of urban parks, little is known about how park usage varies across locations (between or
within cities) or over time.
Methods We estimated park usage among urban residents (identified as residents of urban census tracts) in
498 US cities from 2019 to 2021 from aggregated and anonymised opted-in smartphone location history data. We
used descriptive statistics to quantify differences in park usage over time, between cities, and across census tracts
within cities, and used generalised linear models to estimate the associations between park usage and census tract
level descriptors.
Findings In spring (March 1 to May 31) 2019, 18·9% of urban residents visited a park at least once per week, with
average use higher in northwest and southwest USA, and lowest in the southeast. Park usage varied substantially
both within and between cities; was unequally distributed across census tract-level markers of race, ethnicity, income,
and social vulnerability; and was only moderately correlated with established markers of census tract greenspace. In
spring 2019, a doubling of walking time to parks was associated with a 10·1% (95% CI 5·6–14·3) lower average
weekly park usage, adjusting for city and social vulnerability index. The median decline in park usage from spring
2019 to spring 2020 was 38·0% (IQR 28·4–46·5), coincident with the onset of physical distancing policies across
much of the country. We estimated that the COVID-19-related decline in park usage was more pronounced for those
living further from a park and those living in areas of higher social vulnerability.
Interpretation These estimates provide novel insights into the patterns and correlates of park use and could enable
new studies of the health benefits of urban greenspace. In addition, the availability of an empirical park usage metric
that varies over time could be a useful tool for assessing the effectiveness of policies intended to increase such
activities.
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Rust is a general-purpose programming language designed for performance and safety. Unrecoverable errors (e.g., Divide by Zero) in Rust programs are critical, as they signal bad program states and terminate programs abruptly. Previous work has contributed to utilizing KLEE, a dynamic symbolic test engine, to verify the program would not panic. However, it is difficult for engineers who lack domain expertise to write test code correctly. Besides, the effectiveness of KLEE in finding panics in production Rust code has not been evaluated. We created an approach, called PanicCheck, to hide the complexity of verifying Rust programs with KLEE. Using PanicCheck, engineers only need to annotate the function-to-verify with #[panic_check]. The annotation guides PanicCheck to generate test code, compile the function together with tests, and execute KLEE for verification. After applying PanicCheck to 21 open-source and 2 closed-source projects, we found 61 test inputs that triggered panics; 60 of the 61 panics have been addressed by developers so far. Our research shows promising verification results by KLEE, while revealing technical challenges in using KLEE. Our experience will shed light on future practice and research in program verification.
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Stereotypes are oversimplified beliefs and ideas about particular groups of people. These cognitive biases are omnipresent in our language, reflected in human-generated dataset and potentially learned and perpetuated by language technologies. Although mitigating stereotypes in language technologies is necessary for preventing harms, stereotypes can impose varying levels of risks for targeted individuals and social groups by appearing in various contexts. Technical challenges in detecting stereotypes are rooted in the societal nuances of stereotyping, making it impossible to capture all intertwined interactions of social groups in diverse cultural context in one generic benchmark. This paper delves into the nuances of detecting stereotypes in an annotation task with humans from various regions of the world. We iteratively disambiguate our definition of the task, refining it as detecting ``generalizing language'' and contribute a multilingual, annotated dataset consisting of sentences mentioning a wide range of social identities in 9 languages and labeled on whether they make broad statements and assumptions about those groups. We experiment with training generalizing language detection models, which provide insight about the linguistic context in which stereotypes can appear, facilitating future research in addressing the dynamic, social aspects of stereotypes.
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Hierarchical Text Spotter for Joint Text Spotting and Layout Analysis
Winter Conference on Applications of Computer Vision 2024 (2024) (to appear)
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We propose Hierarchical Text Spotter (HTS), the first method for the joint task of word-level text spotting and geometric layout analysis.
HTS can annotate text in images with a hierarchical representation of 4 levels: character, word, line, and paragraph.
The proposed HTS is characterized by two novel components:
(1) a Unified-Detector-Polygon (UDP) that produces Bezier Curve polygons of text lines and an affinity matrix for paragraph grouping between detected lines;
(2) a Line-to-Character-to-Word (L2C2W) recognizer that splits lines into characters and further merges them back into words.
HTS achieves state-of-the-art results on multiple word-level text spotting benchmark datasets as well as geometric layout analysis tasks.
Code will be released upon acceptance.
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Productive Coverage: Improving the Actionability of Code Coverage
Gordon
Luka Kalinovcic
Mateusz Lewko
Rene Just
Yana Kulizhskaya
ICSE-SEIP '24: Proceedings of the 46th International Conference on Software Engineering: Software Engineering in Practice (2024) (to appear)
Preview abstract
Code coverage is an intuitive and established test adequacy measure.
However, not all parts of the code base are equally important, and
hence additional testing may be critical for some uncovered code,
whereas it may not be worthwhile for other uncovered code. As a
result, simply visualizing uncovered code is not reliably actionable.
To make code coverage actionable and further improve code
coverage in our codebase, we developed Productive Coverage —
a novel approach to code coverage that guides developers to uncovered code that that should be tested by (unit) tests. Specifically,
Productive Coverage identifies uncovered code that is similar to
existing code, which in turn is tested and/or frequently executed in
production. We implemented and evaluated Productive Coverage
for four programming languages (C++, Java, Go, and Python). The
evaluation shows: (1) The developer sentiment, measured at the
point of use, is strongly positive; (2) Productive Coverage meaningfully increases code coverage above a strong baseline; (3) Productive
Coverage has no negative effect on code authoring efficiency; (4)
Productive Coverage modestly improves code-review effiency; (5)
Productive Coverage directly improves code quality and prevents
bugs from being introduced, in addition to improving test quality
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Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting
Conference of the North American Chapter of the Association for Computational Linguistics (NAACL) (2024)
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Ranking documents using Large Language Models (LLMs) by directly feeding the query and candidate documents into the prompt is an interesting and practical problem. However, researchers have found it difficult to outperform fine-tuned baseline rankers on benchmark datasets. We analyze pointwise and listwise ranking prompts used by existing methods and argue that off-the-shelf LLMs do not fully understand these challenging ranking formulations. In this paper, we propose to significantly reduce the burden on LLMs by using a new technique called Pairwise Ranking Prompting (PRP). Our results are the first in the literature to achieve state-of-the-art ranking performance on standard benchmarks using moderate-sized open-sourced LLMs. On TREC-DL 2019&2020, PRP based on the Flan-UL2 model with 20B parameters performs favorably with the previous best approach in the literature, which is based on the blackbox commercial GPT-4 that has 50x (estimated) model size, while outperforming other LLM-based solutions, such as InstructGPT which has 175B parameters, by over 10% for all ranking metrics. By using the same prompt template on seven BEIR tasks, PRP outperforms supervised baselines and outperforms the blackbox commercial ChatGPT solution by 4.2% and pointwise LLM-based solutions by more than 10% on average NDCG@10. Furthermore, we propose several variants of PRP to improve efficiency and show that it is possible to achieve competitive results even with linear complexity.
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We present PhoMoH, a neural network methodology to construct generative models of photo-realistic 3D geometry and appearance of human heads including hair, beards, an oral cavity, and clothing. In contrast to prior work, PhoMoH models the human head using neural fields, thus supporting complex topology. Instead of learning a head model from scratch, we propose to augment an existing expressive head model with new features. Concretely, we learn a highly detailed geometry network layered on top of a mid-resolution head model together with a detailed, local geometry-aware, and disentangled color field. Our proposed architecture allows us to learn photo-realistic human head models from relatively little data. The learned generative geometry and appearance networks can be sampled individually and enable the creation of diverse and realistic human heads. Extensive experiments validate our method qualitatively and across different metrics.
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Perspective Chapter: Assessment of Subjective and Objective Sleep Quality from Wrist-Worn Wearable Data
Ben Yetton
Daniel McDuff
Andrew Barakat
Allen Jiang
Nicholas Allen
Logan Schneider
Ari Winbush
Conor Heneghan
Preview abstract
Researchers are interested in measuring both objective and subjective assessments of sleep, and associated phenomena such as sleepiness, quality and restoration. Predicting perceived sleep quality accurately from objective measurements remains an unsolved and interesting problem. Previous studies using polysomnograms and actigraphy have shown poor concordance between objective metrics and subjective sleep quality, but were often limited by study duration (e.g., one or two nights of PSG, study population in low 100 s). In this chapter, we consider whether consumer sleep trackers could significantly improve the assessment of subjective sleep quality through longer periods of assessment and larger data scale. We describe a recent study that modeled two subjective sleep quality metrics (PROMIS Sleep-Related Impairment (SI) and Sleep Disturbance (SD) Index) from objective sleep metrics acquired from a consumer wearable device (Fitbit). However, the goodness-of-fit parameter remains relatively low, even with the increased data availability and scale of data provided by consumer wearables. Specifically, for a well-characterized normative population of 2106 adults, we see that a linear multivariate model produces an R2 of 0.107 for predicting SI and R2 of 0.147 for SR, consistent with prior results using PSG and actigraphy. We conclude that subjective sleep quality remains broadly a psychological construct that cannot be fully modeled solely by objective sleep metrics.
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The Case for Globalizing Fairness: A Mixed Methods Study on the Perceptions of Colonialism, AI and Health in Africa
Iskandar Haykel
Aisha Walcott-Bryant
Sanmi Koyejo
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With growing machine learning (ML) and large language model applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose. Fairness has implications for health in Africa, which already has inequitable power imbalances between the Global North and South. This paper seeks to explore fairness for global health, with Africa as a case study.
We conduct a scoping review to propose fairness attributes for consideration in the African context and delineate where they may come into play in different ML-enabled medical modalities. We then conduct qualitative research studies with 625 general population study participants in 5 countries in Africa and 28 experts in ML, Health, and/or policy focussed on Africa to obtain feedback on the proposed attributes. We delve specifically into understanding the interplay between AI, health and colonialism.
Our findings demonstrate that among experts there is a general mistrust that technologies that are solely developed by former colonizers can benefit Africans, and that associated resource constraints due to pre-existing economic and infrastructure inequities can be linked to colonialism. General population survey responses found about an average of 40% of people associate an undercurrent of colonialism to AI and this was most dominant amongst participants from South Africa. However the majority of the general population participants surveyed did not think there was a direct link between AI and colonialism.Colonial history, country of origin, National income level were specific axes of disparities that participants felt would cause an AI tool to be biased
This work serves as a basis for policy development around Artificial Intelligence for health in Africa and can be expanded to other regions.
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Instant 3D Human Avatar Generation using Image Diffusion Models
Enric Corona
European Conference on Computer Vision (ECCV) (2024)
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We present AvatarPopUp, a method for fast, high quality 3D human avatar generation from different input modalities, such as images and text prompts and with control over the generated pose and shape. The common theme is the use of diffusion-based image generation networks that are specialized for each particular task, followed by a 3D lifting network. We purposefully decouple the generation from the 3D modeling which allow us to leverage powerful image synthesis priors, trained on billions of text-image pairs. We fine-tune latent diffusion networks with additional image conditioning for image generation and back-view prediction, and to support qualitatively different multiple 3D hypotheses. Our partial fine-tuning approach allows to adapt the networks for each task without inducing catastrophic forgetting. In our experiments, we demonstrate that our method produces accurate, high-quality 3D avatars with diverse appearance that respect the multimodal text, image, and body control signals. Our approach can produce a 3D model in as few as 2 seconds, a four orders of magnitude speedup w.r.t. the vast majority of existing methods, most of which solve only a subset of our tasks, and with fewer controls. AvatarPopUp enables applications that require the controlled 3D generation of human avatars at scale.
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CodecLM: Aligning Language Models with Tailored Synthetic Data
Chun-Liang Li
Jin Miao
NAACL 2024
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Instruction tuning has emerged as the key in aligning large language models (LLMs) with specific task instructions, thereby mitigating the discrepancy between the next-token prediction objective and users' actual goals. To reduce the labor and time cost to collect or annotate data by humans, researchers start to explore the use of LLMs to generate instruction-aligned synthetic data. Recent works focus on generating diverse instructions and applying LLM to increase instruction complexity, often neglecting downstream use cases. It remains unclear how to tailor high-quality data to elicit better instruction-following abilities in different target instruction distributions and LLMs. To this end, we introduce CodecLM, a general framework for adaptively generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs. Drawing on the Encode-Decode principles, we use LLMs as codecs to guide the data generation process. We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution, and then decode metadata to create tailored instructions. We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples. Extensive experiments on four open-domain instruction following benchmarks validate the effectiveness of CodecLM over the current state-of-the-arts.
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