Search Results for author:
Found 17 papers, 5 papers with code
Benchmarking Intersectional Biases in NLP
While much work has highlighted biases embedded in state-of-the-art language models, and more recent efforts have focused on how to debias, research assessing the fairness and performance of biased/debiased models on downstream prediction tasks has been limited.
Constructing a Psychometric Testbed for Fair Natural Language Processing
We also discuss the important implications of our work and resulting testbed for future NLP research on psychometrics and fairness.
Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts
Specifically, we propose a variant of the beam search method to automatically search for biased prompts such that the cloze-style completions are the most different with respect to different demographic groups.
FL-NAS: Towards Fairness of NAS for Resource Constrained Devices via Large Language Models
Neural Architecture Search (NAS) has become the de fecto tools in the industry in automating the design of deep neural networks for various applications, especially those driven by mobile and edge devices with limited computing resources.
Enabling On-Device Large Language Model Personalization with Self-Supervised Data Selection and Synthesis
While it is possible to obtain annotation locally by directly asking users to provide preferred responses, such annotations have to be sparse to not affect user experience.
Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Heads
Although a burgeoning literature has emerged on stereotypical bias mitigation in PLMs, such as work on debiasing gender and racial stereotyping, how such biases manifest and behave internally within PLMs remains largely unknown.
Exploring the Relationship between In-Context Learning and Instruction Tuning
In this work, we explore the relationship between ICL and IT by examining how the hidden states of LLMs change in these two paradigms.
Data Science for Social Good
However, this growth has been accompanied by a diminishing emphasis on social good challenges - our analysis reveals that the proportion of data science research focusing on social good is less than it has ever been.
When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs
Interestingly, we find that the transformer PLMs tend to score GPT-generated text 10-15\% higher on average, relative to human-authored documents.
Alternating minimization algorithm with initialization analysis for r-local and k-sparse unlabeled sensing
The unlabeled sensing problem is to recover an unknown signal from permuted linear measurements.
Constructing a Testbed for Psychometric Natural Language Processing
We discuss our multi-step process to align user text with their survey-based response items and provide an overview of the resulting testbed which encompasses survey-based psychometric measures and accompanying user-generated text from over 8, 500 respondents.
R-local unlabeled sensing: A novel graph matching approach for multiview unlabeled sensing under local permutations
Unlabeled sensing is a linear inverse problem where the measurements are scrambled under an unknown permutation leading to loss of correspondence between the measurements and the rows of the sensing matrix.
Benchmarking Twitter Sentiment Analysis Tools
The results have important implications for various stakeholder groups, including social media analytics researchers, NLP developers, and industry managers and practitioners using social media sentiments as input for decision-making.
A Statistical Learning Based System for Fake Website Detection
Existing fake website detection systems are unable to effectively detect fake websites.
Evaluating Link-Based Techniques for Detecting Fake Pharmacy Websites
There is a need for fake website detection techniques capable of identifying fake online pharmacy websites with a high degree of accuracy.
Evaluating the Usefulness of Sentiment Information for Focused Crawlers
Despite the prevalence of sentiment-related content on the Web, there has been limited work on focused crawlers capable of effectively collecting such content.
Detecting Fake Escrow Websites using Rich Fraud Cues and Kernel Based Methods
The ability to automatically detect fraudulent escrow websites is important in order to alleviate online auction fraud.
