27 citations · 33 across the 9 of their papers we have counts for
10 papers
The Illusion of AI Expertise Under Uncertainty: A Probabilistic Paradigm to Interpret Results Under Ground Truth Variation
Aparna Elangovan, Lei Xu, Mahsa Elyasi +8
Benchmarking the capabilities of AI systems, including Large Language Models (LLMs) and Vision Models, typically ignores the impact of uncertainty in the underlying ground truth an…
Adaptive Multi-Agent Response Refinement in Conversational Systems
Soyeong Jeong, Aparna Elangovan, Emine Yilmaz +1
Large Language Models (LLMs) have demonstrated remarkable success in conversational systems by generating human-like responses. However, they can fall short, especially when requir…
Salient Information Prompting to Steer Content in Prompt-based Abstractive Summarization
Lei Xu, Mohammed Asad Karim, Saket Dingliwal +1
Large language models (LLMs) can generate fluent summaries across domains using prompting techniques, reducing the need to train models for summarization applications. However, cra…
Beyond correlation: The Impact of Human Uncertainty in Measuring the Effectiveness of Automatic Evaluation and LLM-as-a-Judge
Aparna Elangovan, Lei Xu, Jongwoo Ko +4
The effectiveness of automatic evaluation of generative models is typically measured by comparing the labels generated via automation with labels by humans using correlation metric…
ConSiDERS-The-Human Evaluation Framework: Rethinking Human Evaluation for Generative Large Language Models
Aparna Elangovan, Ling Liu, Lei Xu +2
In this position paper, we argue that human evaluation of generative large language models (LLMs) should be a multidisciplinary undertaking that draws upon insights from discipline…
Principles from Clinical Research for NLP Model Generalization
Aparna Elangovan, Jiayuan He, Yuan Li +1
The NLP community typically relies on performance of a model on a held-out test set to assess generalization. Performance drops observed in datasets outside of official test sets a…
Effects of Human Adversarial and Affable Samples on BERT Generalization
Aparna Elangovan, Jiayuan He, Yuan Li +1
BERT-based models have had strong performance on leaderboards, yet have been demonstrably worse in real-world settings requiring generalization. Limited quantities of training data…
Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT
Aparna Elangovan, Yuan Li, Douglas E. V. Pires +2
Protein-protein interactions (PPIs) are critical to normal cellular function and are related to many disease pathways. However, only 4% of PPIs are annotated with PTMs in biologica…
Memorization vs. Generalization: Quantifying Data Leakage in NLP Performance Evaluation
Aparna Elangovan, Jiayuan He, Karin Verspoor
Public datasets are often used to evaluate the efficacy and generalizability of state-of-the-art methods for many tasks in natural language processing (NLP). However, the presence…
Assigning function to protein-protein interactions: a weakly supervised BioBERT based approach using PubMed abstracts
Aparna Elangovan, Melissa Davis, Karin Verspoor
Motivation: Protein-protein interactions (PPI) are critical to the function of proteins in both normal and diseased cells, and many critical protein functions are mediated by inter…