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Found 13 papers, 3 papers with code
Decomposed Prompting: Unveiling Multilingual Linguistic Structure Knowledge in English-Centric Large Language Models
Despite the predominance of English in their training data, English-centric Large Language Models (LLMs) like GPT-3 and LLaMA display a remarkable ability to perform multilingual tasks, raising questions about the depth and nature of their cross-lingual capabilities.
"My Answer is C": First-Token Probabilities Do Not Match Text Answers in Instruction-Tuned Language Models
The open-ended nature of language generation makes the evaluation of autoregressive large language models (LLMs) challenging.
ToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks
However, most previous studies primarily focused on sentence-level classification tasks, and only a few considered token-level labeling tasks such as Named Entity Recognition (NER) and Part-of-Speech (POS) tagging.
Annotation Sensitivity: Training Data Collection Methods Affect Model Performance
We introduce the term annotation sensitivity to refer to the impact of annotation data collection methods on the annotations themselves and on downstream model performance and predictions.
Bridging the Gap: Towards an Expanded Toolkit for ML-Supported Decision-Making in the Public Sector
Machine Learning (ML) systems are becoming instrumental in the public sector, with applications spanning areas like criminal justice, social welfare, financial fraud detection, and public health.
To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?
Although the NLP community has adopted central differential privacy as a go-to framework for privacy-preserving model training or data sharing, the choice and interpretation of the key parameter, privacy budget $\varepsilon$ that governs the strength of privacy protection, remains largely arbitrary.
Sources of Uncertainty in Machine Learning -- A Statisticians' View
Machine Learning and Deep Learning have achieved an impressive standard today, enabling us to answer questions that were inconceivable a few years ago.
Seeing ChatGPT Through Students' Eyes: An Analysis of TikTok Data
To address this gap, we analyzed the content on ChatGPT available on TikTok in February 2023.
Classification ensembles for multivariate functional data with application to mouse movements in web surveys
We propose new ensemble models for multivariate functional data classification as combinations of semi-metric-based weak learners.
Fairness in Algorithmic Profiling: A German Case Study
One example is the prediction-based statistical profiling of job seekers to guide the allocation of support measures by public employment services.
Distributive Justice and Fairness Metrics in Automated Decision-making: How Much Overlap Is There?
The advent of powerful prediction algorithms led to increased automation of high-stake decisions regarding the allocation of scarce resources such as government spending and welfare support.
Global Trends and Predictors of Face Mask Usage During the COVID-19 Pandemic
Methods: We examined a total of 13, 723, 810 responses to a daily cross-sectional representative online survey in 38 countries who completed from April 23, 2020 to October 31, 2020 and reported having been in public at least once during the last seven days.
Applications
Predicting respondent difficulty in web surveys: A machine-learning approach based on mouse movement features
We use data from a survey on respondents' employment history and demographic information, in which we experimentally manipulate the difficulty of several questions.
