64 citations · 155 across the 16 of their papers we have counts for
16 papers
GeMQuAD : Generating Multilingual Question Answering Datasets from Large Language Models using Few Shot Learning
Amani Namboori, Shivam Mangale, Andy Rosenbaum +1
The emergence of Large Language Models (LLMs) with capabilities like In-Context Learning (ICL) has ushered in new possibilities for data generation across various domains while min…
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards
Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay +9
Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are take…
Recipes for Sequential Pre-training of Multilingual Encoder and Seq2Seq Models
Saleh Soltan, Andy Rosenbaum, Tobias Falke +3
Pre-trained encoder-only and sequence-to-sequence (seq2seq) models each have advantages, however training both model types from scratch is computationally expensive. We explore rec…
CLASP: Few-Shot Cross-Lingual Data Augmentation for Semantic Parsing
Andy Rosenbaum, Saleh Soltan, Wael Hamza +3
A bottleneck to developing Semantic Parsing (SP) models is the need for a large volume of human-labeled training data. Given the complexity and cost of human annotation for SP, lab…
LINGUIST: Language Model Instruction Tuning to Generate Annotated Utterances for Intent Classification and Slot Tagging
Andy Rosenbaum, Saleh Soltan, Wael Hamza +2
We present LINGUIST, a method for generating annotated data for Intent Classification and Slot Tagging (IC+ST), via fine-tuning AlexaTM 5B, a 5-billion-parameter multilingual seque…
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model
Saleh Soltan, Shankar Ananthakrishnan, Jack FitzGerald +13
In this work, we demonstrate that multilingual large-scale sequence-to-sequence (seq2seq) models, pre-trained on a mixture of denoising and Causal Language Modeling (CLM) tasks, ar…
Alexa Teacher Model: Pretraining and Distilling Multi-Billion-Parameter Encoders for Natural Language Understanding Systems
Jack FitzGerald, Shankar Ananthakrishnan, Konstantine Arkoudas +38
We present results from a large-scale experiment on pretraining encoders with non-embedding parameter counts ranging from 700M to 9.3B, their subsequent distillation into smaller m…
Don't Parse, Insert: Multilingual Semantic Parsing with Insertion Based Decoding
Qile Zhu, Haidar Khan, Saleh Soltan +2
Semantic parsing is one of the key components of natural language understanding systems. A successful parse transforms an input utterance to an action that is easily understood by…
Distribution of blackouts in the power grid and the Motter and Lai model
Yosef Kornbluth, Gabriel Cwilich, Sergey V. Buldyrev +2
Carreras, Dobson and colleagues have studied empirical data on the sizes of the blackouts in real grids and modeled them by computer simulations using the direct current approximat…
Protecting the Grid against IoT Botnets of High-Wattage Devices
Saleh Soltan, Prateek Mittal, H. Vincent Poor
We provide methods to prevent line failures in the power grid caused by a newly revealed MAnipulation of Demand (MAD) attacks via an IoT botnet of high-wattage devices. In particul…
EXPOSE the Line Failures following a Cyber-Physical Attack on the Power Grid
Saleh Soltan, Gil Zussman
Recent attacks on power grids demonstrated the vulnerability of the grids to cyber and physical attacks. To analyze this vulnerability, we study cyber-physical attacks that affect…
REACT to Cyber Attacks on Power Grids
Saleh Soltan, Mihalis Yannakakis, Gil Zussman
Motivated by the recent cyber attack on the Ukrainian power grid, we study cyber attacks on power grids that affect both the physical infrastructure and the data at the control cen…
A study of cascading failures in real and synthetic power grid topologies using DC power flows
Russell Spiewak, Sergey V. Buldyrev, Yakir Forman +2
Using the linearized DC power flow model, we study cascading failures and their spatial and temporal properties in the US Western Interconnect (USWI) power grid. We also introduce…
Doubly Balanced Connected Graph Partitioning
Saleh Soltan, Mihalis Yannakakis, Gil Zussman
We introduce and study the Doubly Balanced Connected graph Partitioning (DBCP) problem: Let be a connected graph with a weight (supply/demand) function $p:V\rightarrow \{…
Generation of Synthetic Spatially Embedded Power Grid Networks
Saleh Soltan, Gil Zussman
The development of algorithms for enhancing the resilience and efficiency of the power grid requires performance evaluation with real topologies of power transmission networks. How…
Cascading Failures in Power Grids - Analysis and Algorithms
Saleh Soltan, Dorian Mazauric, Gil Zussman
This paper focuses on cascading line failures in the transmission system of the power grid. Recent large-scale power outages demonstrated the limitations of percolation- and epid-…