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Found 7 papers, 2 papers with code
The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education
Assessing instruction quality is a fundamental component of any improvement efforts in the education system.
Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey
Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.
Emojis Decoded: Leveraging ChatGPT for Enhanced Understanding in Social Media Communications
Our objective is to validate the hypothesis that ChatGPT can serve as a viable alternative to human annotators in emoji research and that its ability to explain emoji meanings can enhance clarity and transparency in online communications.
Explore Spurious Correlations at the Concept Level in Language Models for Text Classification
We find that LMs, when encountering spurious correlations between a concept and a label in training or prompts, resort to shortcuts for predictions.
Towards Understanding In-Context Learning with Contrastive Demonstrations and Saliency Maps
We investigate the role of various demonstration components in the in-context learning (ICL) performance of large language models (LLMs).
GFairHint: Improving Individual Fairness for Graph Neural Networks via Fairness Hint
Given the growing concerns about fairness in machine learning and the impressive performance of Graph Neural Networks (GNNs) on graph data learning, algorithmic fairness in GNNs has attracted significant attention.
Using Noisy Self-Reports to Predict Twitter User Demographics
We present a method to identify self-reports of race and ethnicity from Twitter profile descriptions.
