4 citations · 5 across the 7 of their papers we have counts for
7 papers
Revealing the Numeracy Gap: An Empirical Investigation of Text Embedding Models
Ningyuan Deng, Hanyu Duan, Yixuan Tang +1
Text embedding models are widely used in natural language processing applications. However, their capability is often benchmarked on tasks that do not require understanding nuanced…
Ready2Unlearn: A Learning-Time Approach for Preparing Models with Future Unlearning Readiness
Hanyu Duan, Yi Yang, Ahmed Abbasi +1
Machine unlearning is the process of removing the imprint left by specific data samples during the training of a machine learning model. AI developers, including those building per…
Robust Predictive Modeling Under Unseen Data Distribution Shifts: A Methodological Commentary
Hanyu Duan, Yi Yang, Ahmed Abbasi +1
Most research designing novel predictive models, or employing existing ones, assumes that training and testing data are independent and identically distributed. In practice, the da…
LLM-Measure: Generating Valid, Consistent, and Reproducible Text-Based Measures for Social Science Research
Yi Yang, Hanyu Duan, Jiaxin Liu +1
The increasing use of text as data in social science research necessitates the development of valid, consistent, reproducible, and efficient methods for generating text-based conce…
Do LLMs Know about Hallucination? An Empirical Investigation of LLM's Hidden States
Hanyu Duan, Yi Yang, Kar Yan Tam
Large Language Models (LLMs) can make up answers that are not real, and this is known as hallucination. This research aims to see if, how, and to what extent LLMs are aware of hall…
Exploring the Relationship between In-Context Learning and Instruction Tuning
Hanyu Duan, Yixuan Tang, Yi Yang +2
In-Context Learning (ICL) and Instruction Tuning (IT) are two primary paradigms of adopting Large Language Models (LLMs) to downstream applications. However, they are significantly…
Bias A-head? Analyzing Bias in Transformer-Based Language Model Attention Heads
Yi Yang, Hanyu Duan, Ahmed Abbasi +2
Transformer-based pretrained large language models (PLM) such as BERT and GPT have achieved remarkable success in NLP tasks. However, PLMs are prone to encoding stereotypical biase…