Search Results for author:
Found 17 papers, 7 papers with code
"Confidently Nonsensical?'': A Critical Survey on the Perspectives and Challenges of 'Hallucinations' in NLP
We investigate how hallucination in large language models (LLM) is characterized in peer-reviewed literature using a critical examination of 103 publications across NLP research.
Interpretable Multi-Source Data Fusion Through Latent Variable Gaussian Process
Towards resolving this issue, a multi-source data fusion framework based on Latent Variable Gaussian Process (LVGP) is proposed.
The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis
We conduct an inquiry into the sociotechnical aspects of sentiment analysis (SA) by critically examining 189 peer-reviewed papers on their applications, models, and datasets.
CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias
We apply CALM to 20 large language models, and find that for 2 language model series, larger parameter models tend to be more biased than smaller ones.
Semantic Consistency for Assuring Reliability of Large Language Models
Large Language Models (LLMs) exhibit remarkable fluency and competence across various natural language tasks.
Sociodemographic Bias in Language Models: A Survey and Forward Path
This paper presents a comprehensive survey of work on sociodemographic bias in language models (LMs).
Do we need entire training data for adversarial training?
We show that we can decrease the training time for any adversarial training algorithm by using only a subset of training data for adversarial training.
SwapMix: Diagnosing and Regularizing the Over-Reliance on Visual Context in Visual Question Answering
By swapping the context object features, the model reliance on context can be suppressed effectively.
LocalNewton: Reducing Communication Bottleneck for Distributed Learning
To enhance practicability, we devise an adaptive scheme to choose L, and we show that this reduces the number of local iterations in worker machines between two model synchronizations as the training proceeds, successively refining the model quality at the master.
BEAR: Sketching BFGS Algorithm for Ultra-High Dimensional Feature Selection in Sublinear Memory
We consider feature selection for applications in machine learning where the dimensionality of the data is so large that it exceeds the working memory of the (local) computing machine.
Training Recommender Systems at Scale: Communication-Efficient Model and Data Parallelism
This is done by identifying and updating only the most relevant neurons of the neural network for each training sample in the data.
Utility-based Resource Allocation and Pricing for Serverless Computing
As one of the fastest-growing cloud services, serverless computing provides an opportunity to better serve both users and providers through the incorporation of market-based strategies for pricing and resource allocation.
Distributed, Parallel, and Cluster Computing Computer Science and Game Theory
Serverless Straggler Mitigation using Local Error-Correcting Codes
Inexpensive cloud services, such as serverless computing, are often vulnerable to straggling nodes that increase end-to-end latency for distributed computation.
Distributed, Parallel, and Cluster Computing Information Theory Information Theory
Stochastic Weight Averaging in Parallel: Large-Batch Training that Generalizes Well
We propose Stochastic Weight Averaging in Parallel (SWAP), an algorithm to accelerate DNN training.
OverSketched Newton: Fast Convex Optimization for Serverless Systems
Motivated by recent developments in serverless systems for large-scale computation as well as improvements in scalable randomized matrix algorithms, we develop OverSketched Newton, a randomized Hessian-based optimization algorithm to solve large-scale convex optimization problems in serverless systems.
OverSketch: Approximate Matrix Multiplication for the Cloud
We propose OverSketch, an approximate algorithm for distributed matrix multiplication in serverless computing.
Distributed, Parallel, and Cluster Computing Information Theory Information Theory
A Sequential Approximation Framework for Coded Distributed Optimization
As an application of the results, we demonstrate solving optimization problems using a sequential approximation approach, which accelerates the algorithm in a distributed system with stragglers.
