Title: Cultural Biases, World Languages, and User Privacy in Large Language Models
Abstract: In this talk, I will highlight three key aspects of large language models: (1) cultural bias in LLMs and pre-training data, (2) decoding algorithm for low-resource languages, and (3) human-centered design for real-world applications.
The first part focuses on systematically assessing LLMs' favoritism towards Western culture. We take an entity-centric approach to measure the cultural biases among LLMs (e.g., GPT-4, Aya, and mT5) through natural prompts, story generation, sentiment analysis, and named entity tasks. One interesting finding is that a potential cause of cultural biases in LLMs is the extensive use and upsampling of Wikipedia data during the pre-training of almost all LLMs. The second part will introduce a constrained decoding algorithm that can facilitate the generation of high-quality synthetic training data for fine-grained prediction tasks (e.g., named entity recognition, event extraction). This approach outperforms GPT-4 on many non-English languages, particularly low-resource African languages. Lastly, I will showcase an LLM-powered privacy preservation tool designed to safeguard users against the disclosure of personal information. I will share findings from an HCI user study that involves real Reddit users utilizing our tool, which in turn informs our ongoing efforts to improve the design of AI models.
Bio:
Wei Xu is an Associate Professor in the College of Computing and Machine Learning Center at the Georgia Institute of Technology, where she is the director of the NLP X Lab. Her research interests are in natural language processing and machine learning, with a focus on Generative AI, robustness and fairness of large language models, multilingual LLMs, as well as AI for science, education, accessibility, and privacy research. She is a recipient of the NSF CAREER Award, Google Academic Research Award, CrowdFlower AI for Everyone Award, Best Paper Awards and Honorable Mentions at COLING'18, ACL'23, ACL'24. She also received research funds from DARPA and IARPA. She is currently an executive board member of NAACL. Join Zoom Meeting https://stonybrook.zoom.us/j/98855994362?pwd=F2qnpwL85fhCBHAEW9ZBpXihfwGHsj.1 (ID: 98855994362, passcode: 172797) Join by phone (US) +1 646-876-9923 (passcode: 172797) Joining instructions: https://www.google.com/url?q=https://applications.zoom.us/addon/invitation/detail?meetingUuid%3DuDJcUTvyQueZkCaUSAwFlg%253D%253D%26signature%3Da3d49e0f7f2e74e7130f7308c74bd85ba7b99587b98ba2e34238bb657ca51a09%26v%3D1&sa=D&source=calendar&usg=AOvVaw2jTn5cjfRG8vXU8KHHlU2Y Meeting host: H.Andrew.Schwartz@stonybrook.edu
Join Zoom Meeting:
https://stonybrook.zoom.us/j/98855994362?pwd=F2qnpwL85fhCBHAEW9ZBpXihfwGHsj.1
Abstract: In this talk, I will highlight three key aspects of large language models: (1) cultural bias in LLMs and pre-training data, (2) decoding algorithm for low-resource languages, and (3) human-centered design for real-world applications.
The first part focuses on systematically assessing LLMs' favoritism towards Western culture. We take an entity-centric approach to measure the cultural biases among LLMs (e.g., GPT-4, Aya, and mT5) through natural prompts, story generation, sentiment analysis, and named entity tasks. One interesting finding is that a potential cause of cultural biases in LLMs is the extensive use and upsampling of Wikipedia data during the pre-training of almost all LLMs. The second part will introduce a constrained decoding algorithm that can facilitate the generation of high-quality synthetic training data for fine-grained prediction tasks (e.g., named entity recognition, event extraction). This approach outperforms GPT-4 on many non-English languages, particularly low-resource African languages. Lastly, I will showcase an LLM-powered privacy preservation tool designed to safeguard users against the disclosure of personal information. I will share findings from an HCI user study that involves real Reddit users utilizing our tool, which in turn informs our ongoing efforts to improve the design of AI models.
Bio:
Wei Xu is an Associate Professor in the College of Computing and Machine Learning Center at the Georgia Institute of Technology, where she is the director of the NLP X Lab. Her research interests are in natural language processing and machine learning, with a focus on Generative AI, robustness and fairness of large language models, multilingual LLMs, as well as AI for science, education, accessibility, and privacy research. She is a recipient of the NSF CAREER Award, Google Academic Research Award, CrowdFlower AI for Everyone Award, Best Paper Awards and Honorable Mentions at COLING'18, ACL'23, ACL'24. She also received research funds from DARPA and IARPA. She is currently an executive board member of NAACL. Join Zoom Meeting https://stonybrook.zoom.us/j/98855994362?pwd=F2qnpwL85fhCBHAEW9ZBpXihfwGHsj.1 (ID: 98855994362, passcode: 172797) Join by phone (US) +1 646-876-9923 (passcode: 172797) Joining instructions: https://www.google.com/url?q=https://applications.zoom.us/addon/invitation/detail?meetingUuid%3DuDJcUTvyQueZkCaUSAwFlg%253D%253D%26signature%3Da3d49e0f7f2e74e7130f7308c74bd85ba7b99587b98ba2e34238bb657ca51a09%26v%3D1&sa=D&source=calendar&usg=AOvVaw2jTn5cjfRG8vXU8KHHlU2Y Meeting host: H.Andrew.Schwartz@stonybrook.edu
Join Zoom Meeting:
https://stonybrook.zoom.us/j/98855994362?pwd=F2qnpwL85fhCBHAEW9ZBpXihfwGHsj.1
Abstract: Capturing the spatio-temporal (4D) dynamics of humans has been a long standing research problem in computer vision and graphics. Synthesizing photorealistic human avatars has broad applications, ranging from immersive telepresence in AR/VR and the movie industry, to enriching the education and healthcare systems. Earlier approaches relied on hand-engineered models that use a small amount of data from one or more subjects. With the advent of neural networks, training on large datasets enhanced the output visual quality. Currently, the combination of neural networks with graphics techniques has achieved natural-looking human animation. However, most approaches are identity-specific, trained only on a single identity, and use only one modality.
In this dissertation, we address the problem of learning neural representations of humans in a holistic way. Given that the video data in the real world include multiple modalities (e.g., audio and video) and multiple identities, we develop multi-modal and multi-identity representations. First, we propose to reconstruct the 4D face geometry of humans by leveraging both audio and video information. In this way, the network produces accurate lip shapes and is robust to cases when either modality is insufficient. Next, we introduce a NeRF-based representation for audio-driven human face animation that achieves high-quality lip synchronization for cinematic content. Since humans communicate with their full body, combining body pose, hand gestures, and facial expressions, we extend the network to capture full-body human motion for multiple identities simultaneously. In order to better disentangle identity and non-identity specific information, we subsequently study non-linear interactions between latent factors of variation, and propose a specific multiplicative module. In this way, we learn a multi-identity NeRF that robustly animates human faces under novel expressions and achieves a significant decrease in the total training time. Similarly, we propose a multi-identity Gaussian splatting representation for human bodies, by constructing a high-order tensor. Assuming a low-rank structure, we learn a tensor decomposition that leads to a significant decrease in the total number of learnable parameters, as well as to a robust animation under novel poses. Last but not least, we propose to jointly synthesize audio and visual outputs from just text input. Given the recent rise of large language models, coupling text with natural-looking avatars can enhance the overall interaction between a human and an AI system.
Location: NCS 220 or Zoom
In this dissertation, we address the problem of learning neural representations of humans in a holistic way. Given that the video data in the real world include multiple modalities (e.g., audio and video) and multiple identities, we develop multi-modal and multi-identity representations. First, we propose to reconstruct the 4D face geometry of humans by leveraging both audio and video information. In this way, the network produces accurate lip shapes and is robust to cases when either modality is insufficient. Next, we introduce a NeRF-based representation for audio-driven human face animation that achieves high-quality lip synchronization for cinematic content. Since humans communicate with their full body, combining body pose, hand gestures, and facial expressions, we extend the network to capture full-body human motion for multiple identities simultaneously. In order to better disentangle identity and non-identity specific information, we subsequently study non-linear interactions between latent factors of variation, and propose a specific multiplicative module. In this way, we learn a multi-identity NeRF that robustly animates human faces under novel expressions and achieves a significant decrease in the total training time. Similarly, we propose a multi-identity Gaussian splatting representation for human bodies, by constructing a high-order tensor. Assuming a low-rank structure, we learn a tensor decomposition that leads to a significant decrease in the total number of learnable parameters, as well as to a robust animation under novel poses. Last but not least, we propose to jointly synthesize audio and visual outputs from just text input. Given the recent rise of large language models, coupling text with natural-looking avatars can enhance the overall interaction between a human and an AI system.
Location: NCS 220 or Zoom
Zoom: https://stonybrook.zoom.us/j/
Meeting ID: 97512368946
passcode: 045476
passcode: 045476
Nam Nguyen
4-5pm, Dec 17 2020
https://stonybrook.zoom.us/j/94214254415?pwd=K1VoQml4cFdlVW51VW41dWtid2tJdz09
The molecular mechanisms and functions in complex biological systems
currently remain elusive. Recent high-throughput techniques, such as
next-generation sequencing, have generated a wide variety of
multiomics datasets that enable the identification of biological
functions and mechanisms via multiple facets. However, integrating
these large-scale multiomics data and discovering functional insights
are, nevertheless, challenging tasks. To address these challenges,
machine learning has been broadly applied to analyze multiomics. In
particular, multiview learning is more effective than previous
integrative methods for learning data's heterogeneity and revealing
cross-talk patterns. Although it has been applied to various contexts,
such as computer vision and speech recognition, multiview learning has
not yet been widely applied to biological data--specifically,
multiomics data. Therefore, we have developed a framework called
multiview empirical risk minimization (MV-ERM) for unifying multiview
learning methods (Nguyen, et al., PLoS Computational Biology, 2020).
MV-ERM enables potential applications to understand multiomics
including genomics, transcriptomics, and epigenomics, in an aim to
discover the functional and mechanistic interpretations across omics.
Based on MV-ERM, we have developed the following methods:
ManiNetCluster, Varmole and ECMarker.
(1) ManiNetCluster (Nguyen, et al., BMC Genomics, 2019) is a manifold
learning method which simultaneously aligns and clusters gene networks
(e.g., co-expression) to systematically reveal the links of genomic
function between different phenotypes. Specifically, ManiNetCluster
employs manifold alignment to uncover and match local and non-linear
structures among networks, and identifies cross-network functional
links. We demonstrated that ManiNetCluster better aligns the
orthologous genes from their developmental expression profiles across
model organisms than state-of-the-art methods. This indicates the
potential non-linear interactions of evolutionarily conserved genes
across species in development. Furthermore, we applied ManiNetCluster
to time series transcriptome data measured in the green alga
Chlamydomonas reinhardtii to discover the genomic functions linking
various metabolic processes between the light and dark periods of a
diurnally cycling culture;
(2) Varmole (Nguyen, et al., Bioinformatics, 2020) is an interpretable
deep learning method that simultaneously reveals genomic functions and
mechanisms while predicting phenotype from genotype. In particular,
Varmole embeds multi-omic networks into a deep neural network
architecture and prioritizes variants, genes and regulatory linkages
via biological drop-connect without needing prior feature selections.
With an application to schizophonia, we demonstrate that Varmole
provides an effective alternative for recent statistical methods that
associate functional omic data (e.g. gene expression) with genotype
and phenotype and that link variants to individual genes in population
studies such as genome-wide association study;
(3) ECMarker (Jin*, Nguyen*, et al., Bioinformatics, 2020) is an
interpretable and scalable machine learning model that predicts gene
expression biomarkers for disease phenotypes and simultaneously
reveals underlying regulatory mechanisms. Particularly, ECMarker is
built on the integration of semi- and discriminative- restricted
Boltzmann machines, a neural network model for classification allowing
lateral connections at the input gene layer. With application to the
gene expression data of non-small cell lung cancer (NSCLC) patients,
we found that ECMarker not only achieved a relatively high accuracy
for predicting cancer stages but also identified the biomarker genes
and gene networks implying the regulatory mechanisms in lung cancer
development.
Finally, we propose a novel multiview learning method, Malignomics, to
predict phenotypes from heterogeneous multi-omic features. Malignomics
will first align multi-omic features by deep manifold alignment onto a
common latent space, better predicting nonlinear relationships across
omics. This deep alignment aims to preserve both global consistency
and local smoothness across omics and reveal higher-order nonlinear
interactions (i.e., manifolds) among cross-omic features. Second, it
uses these manifold structures to regularize the classifiers for
predicting phenotypes. This manifold-regularization allows
highlighting cross-omic feature manifolds and prioritizing the
features and interactions for the phenotypes. The prioritized
multi-omic features will further reveal underlying phenotypic
functions and mechanisms and thus enhance the biological
interpretation of Malignomics. We will apply Malignomics to
multi-omics data in neuropsychiatric disorders, and prioritize gene
regulatory networks linking risk variants, regulatory elements, and
genes for the disorders. We will also compare Malignomics with the
state-of-the-arts, and investigate how the manifold regulation will
potentially improve understanding of multi-omics functions and
predicting diseases.
4-5pm, Dec 17 2020
https://stonybrook.zoom.us/j/9
The molecular mechanisms and functions in complex biological systems
currently remain elusive. Recent high-throughput techniques, such as
next-generation sequencing, have generated a wide variety of
multiomics datasets that enable the identification of biological
functions and mechanisms via multiple facets. However, integrating
these large-scale multiomics data and discovering functional insights
are, nevertheless, challenging tasks. To address these challenges,
machine learning has been broadly applied to analyze multiomics. In
particular, multiview learning is more effective than previous
integrative methods for learning data's heterogeneity and revealing
cross-talk patterns. Although it has been applied to various contexts,
such as computer vision and speech recognition, multiview learning has
not yet been widely applied to biological data--specifically,
multiomics data. Therefore, we have developed a framework called
multiview empirical risk minimization (MV-ERM) for unifying multiview
learning methods (Nguyen, et al., PLoS Computational Biology, 2020).
MV-ERM enables potential applications to understand multiomics
including genomics, transcriptomics, and epigenomics, in an aim to
discover the functional and mechanistic interpretations across omics.
Based on MV-ERM, we have developed the following methods:
ManiNetCluster, Varmole and ECMarker.
(1) ManiNetCluster (Nguyen, et al., BMC Genomics, 2019) is a manifold
learning method which simultaneously aligns and clusters gene networks
(e.g., co-expression) to systematically reveal the links of genomic
function between different phenotypes. Specifically, ManiNetCluster
employs manifold alignment to uncover and match local and non-linear
structures among networks, and identifies cross-network functional
links. We demonstrated that ManiNetCluster better aligns the
orthologous genes from their developmental expression profiles across
model organisms than state-of-the-art methods. This indicates the
potential non-linear interactions of evolutionarily conserved genes
across species in development. Furthermore, we applied ManiNetCluster
to time series transcriptome data measured in the green alga
Chlamydomonas reinhardtii to discover the genomic functions linking
various metabolic processes between the light and dark periods of a
diurnally cycling culture;
(2) Varmole (Nguyen, et al., Bioinformatics, 2020) is an interpretable
deep learning method that simultaneously reveals genomic functions and
mechanisms while predicting phenotype from genotype. In particular,
Varmole embeds multi-omic networks into a deep neural network
architecture and prioritizes variants, genes and regulatory linkages
via biological drop-connect without needing prior feature selections.
With an application to schizophonia, we demonstrate that Varmole
provides an effective alternative for recent statistical methods that
associate functional omic data (e.g. gene expression) with genotype
and phenotype and that link variants to individual genes in population
studies such as genome-wide association study;
(3) ECMarker (Jin*, Nguyen*, et al., Bioinformatics, 2020) is an
interpretable and scalable machine learning model that predicts gene
expression biomarkers for disease phenotypes and simultaneously
reveals underlying regulatory mechanisms. Particularly, ECMarker is
built on the integration of semi- and discriminative- restricted
Boltzmann machines, a neural network model for classification allowing
lateral connections at the input gene layer. With application to the
gene expression data of non-small cell lung cancer (NSCLC) patients,
we found that ECMarker not only achieved a relatively high accuracy
for predicting cancer stages but also identified the biomarker genes
and gene networks implying the regulatory mechanisms in lung cancer
development.
Finally, we propose a novel multiview learning method, Malignomics, to
predict phenotypes from heterogeneous multi-omic features. Malignomics
will first align multi-omic features by deep manifold alignment onto a
common latent space, better predicting nonlinear relationships across
omics. This deep alignment aims to preserve both global consistency
and local smoothness across omics and reveal higher-order nonlinear
interactions (i.e., manifolds) among cross-omic features. Second, it
uses these manifold structures to regularize the classifiers for
predicting phenotypes. This manifold-regularization allows
highlighting cross-omic feature manifolds and prioritizing the
features and interactions for the phenotypes. The prioritized
multi-omic features will further reveal underlying phenotypic
functions and mechanisms and thus enhance the biological
interpretation of Malignomics. We will apply Malignomics to
multi-omics data in neuropsychiatric disorders, and prioritize gene
regulatory networks linking risk variants, regulatory elements, and
genes for the disorders. We will also compare Malignomics with the
state-of-the-arts, and investigate how the manifold regulation will
potentially improve understanding of multi-omics functions and
predicting diseases.
Abstract: Implicit functions have long been a fundamental representation for both 2D and 3D objects in computer graphics, playing a significant role in the field's early development. With the rise of 3D deep learning and the rapid advancement of neural rendering techniques, implicit representations of 3D shapes have regained significant attention in recent years. In this talk, I will present several recent research projects focusing on implicit function-based 3D reconstruction and neural rendering. Furthermore, I will discuss potential future developments in this dynamic and rapidly evolving field.
Biography: Ying He is an Associate Professor at the College of Computing and Data Science, Nanyang Technological University, where he also serves as the Director of the Centre for Augmented and Virtual Reality. His research interests lie in geometric computation and analysis, with applications spanning computer graphics, 3D vision, computer-aided design, multimedia, and wireless sensor networks. Dr. He is an active member of the technical program committees for major conferences on geometric modeling and has served on the editorial boards of IEEE Transactions on Visualization and Computer Graphics, Computer Graphics Forum, and Computational Visual Media. He has also taken on key leadership roles as General/Program Co-Chair for several conferences, including Shape Modeling International (SMI) 2022, Solid and Physical Modeling (SPM) 2022 & 2023, Geometric Modeling and Processing (GMP) 2014 & 2021, and Computational Visual Media (CVM) 2020. For more information, please visit https://personal.ntu.
Location: NCS 115
Public Interest Technology Research and Education by Ellen Zegura, Georgia Tech
2019 * 2020 Shutterstock Distinguished Lecture Series
Learn how to unlock the power of Image and visuals that will enhance your work by asking the experts questions in-person
No registration required - just stop by!
Location: Frank Melville Jr. Memorial Library Galleria (across from the Central Reading room)
CSE 600 Talk: Squeezing Software Performance via Eliminating Wasteful Operations presented by Xu Liu
ABSTRACT: Inefficiencies abound in complex, layered software. A variety of inefficiencies show up as wasteful memory operations, such as redundant or useless memory loads and stores. Aliasing, limited optimization scopes, and insensitivity to input and execution contexts act as severe deterrents to static program analysis. Microscopic observation of whole executions at instruction- and operand-level granularity breaks down abstractions and helps recognize redundancies that masquerade in complex programs. In this talk, I will describe various wasteful memory operations, which pervasively exist in modern
software packages and expose great potential for optimization. I will discuss the design of a fine-grained instrumentation-based profiling framework that identifies wasteful operations in their contexts, which guides nontrivial performance improvement. Furthermore, I will show our recent improvement to the profiling framework by abandoning
instrumentation, which reduces the runtime overhead from 10x to 3% on average. I will show how our approach works for native binaries and various managed languages such as Java, yielding new performance insights for optimization.
BIO: Xu Liu is an assistant professor in the Department of Computer Science at College of William & Mary. He obtained his PhD from Rice University in 2014 and joined the College of William & Mary in the same year. Prof. Liu works on building performance tools to pinpoint and optimize inefficiencies in HPC code bases. He has developed several open-source profiling tools, which are used worldwide at universities, DOE national laboratories and industrial companies. Prof. Liu has published a number of papers in high-quality venues. His papers received Best Paper Award at SC'15, PPoPP'18, PPoPP'19 and ASPLOS'17 Highlights, as well as Distinguished Paper Award at ICSE'19. His recent ASPLOS'18 paper has been selected as ACM SIGPLAN Research Highlights in 2019 and nominated for CACM Research Highlights. Prof. Liu is the receipt of 2019 IEEE TCHPC Early Career Researchers Award for Excellence in High Performance Computing. Prof. Liu served on the program committee of conferences such as SC, PPoPP, IPDPS, CGO, HPCA and ASPLOS.
ABSTRACT: Inefficiencies abound in complex, layered software. A variety of inefficiencies show up as wasteful memory operations, such as redundant or useless memory loads and stores. Aliasing, limited optimization scopes, and insensitivity to input and execution contexts act as severe deterrents to static program analysis. Microscopic observation of whole executions at instruction- and operand-level granularity breaks down abstractions and helps recognize redundancies that masquerade in complex programs. In this talk, I will describe various wasteful memory operations, which pervasively exist in modern
software packages and expose great potential for optimization. I will discuss the design of a fine-grained instrumentation-based profiling framework that identifies wasteful operations in their contexts, which guides nontrivial performance improvement. Furthermore, I will show our recent improvement to the profiling framework by abandoning
instrumentation, which reduces the runtime overhead from 10x to 3% on average. I will show how our approach works for native binaries and various managed languages such as Java, yielding new performance insights for optimization.
BIO: Xu Liu is an assistant professor in the Department of Computer Science at College of William & Mary. He obtained his PhD from Rice University in 2014 and joined the College of William & Mary in the same year. Prof. Liu works on building performance tools to pinpoint and optimize inefficiencies in HPC code bases. He has developed several open-source profiling tools, which are used worldwide at universities, DOE national laboratories and industrial companies. Prof. Liu has published a number of papers in high-quality venues. His papers received Best Paper Award at SC'15, PPoPP'18, PPoPP'19 and ASPLOS'17 Highlights, as well as Distinguished Paper Award at ICSE'19. His recent ASPLOS'18 paper has been selected as ACM SIGPLAN Research Highlights in 2019 and nominated for CACM Research Highlights. Prof. Liu is the receipt of 2019 IEEE TCHPC Early Career Researchers Award for Excellence in High Performance Computing. Prof. Liu served on the program committee of conferences such as SC, PPoPP, IPDPS, CGO, HPCA and ASPLOS.
Artificial Intelligence is rapidly reshaping research, education, and industry--but its growth carries important environmental implications. From the energy demands of large-scale computing to AI's potential to advance climate modeling, conservation, and sustainable design, the relationship between AI and the environment is both challenging and promising. This interdisciplinary panel explores AI's ecological footprint, its role in environmental solutions, and how universities can pursue innovation while upholding sustainability commitments.
Panelists:
Dana Golden -- PhD student in Economics, Stony Brook University.
Dr. Sharon Pochron -- Associate Professor in Sustainability Studies Program, School of Marine and Atmospheric Sciences, Stony Brook University.
Dr. Jordanna Sprayberry -- Associate Professor, Ecology & Evolution, Director of Undergraduate Biology, Stony Brook University.
Dr. Lav Varshney -- Director of the Artificial Intelligence Innovation Institute (AI3) and inaugural Della Pietra Infinity Chair, Stony Brook University.
Register here.
Panelists:
Dana Golden -- PhD student in Economics, Stony Brook University.
Dr. Sharon Pochron -- Associate Professor in Sustainability Studies Program, School of Marine and Atmospheric Sciences, Stony Brook University.
Dr. Jordanna Sprayberry -- Associate Professor, Ecology & Evolution, Director of Undergraduate Biology, Stony Brook University.
Dr. Lav Varshney -- Director of the Artificial Intelligence Innovation Institute (AI3) and inaugural Della Pietra Infinity Chair, Stony Brook University.
Register here.