1 citations · 2 across the 8 of their papers we have counts for
9 papers
PersonaFuse: A Personality Activation-Driven Framework for Enhancing Human-LLM Interactions
Yixuan Tang, Yi Yang, Ahmed Abbasi
Recent advancements in Large Language Models (LLMs) demonstrate remarkable capabilities across various fields. These developments have led to more direct communication between huma…
Benchmarking Sociolinguistic Diversity in Swahili NLP: A Taxonomy-Guided Approach
Kezia Oketch, John P. Lalor, Ahmed Abbasi
We introduce the first taxonomy-guided evaluation of Swahili NLP, addressing gaps in sociolinguistic diversity. Drawing on health-related psychometric tasks, we collect a dataset o…
PersonaTwin: A Multi-Tier Prompt Conditioning Framework for Generating and Evaluating Personalized Digital Twins
Sihan Chen, John P. Lalor, Yi Yang +1
While large language models (LLMs) afford new possibilities for user modeling and approximation of human behaviors, they often fail to capture the multidimensional nuances of indiv…
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…
Bridging the LLM Accessibility Divide? Performance, Fairness, and Cost of Closed versus Open LLMs for Automated Essay Scoring
Kezia Oketch, John P. Lalor, Yi Yang +1
Closed large language models (LLMs) such as GPT-4 have set state-of-the-art results across a number of NLP tasks and have become central to NLP and machine learning (ML)-driven sol…
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…
Aligning Human and Machine Attention for Enhanced Supervised Learning
Avihay Chriqui, Inbal Yahav, Dov Teeni +1
Attention, or prioritization of certain information items over others, is a critical element of any learning process, for both humans and machines. Given that humans continue to ou…
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…