3 citations · 4 across the 8 of their papers we have counts for
8 papers
Risk Under Pressure: Compute-Aware Evaluation of Adversarial Robustness in Language Models
Malikeh Ehghaghi, Boglárka Ecsedi, Marsha Chechik +1
Adversarial robustness evaluations of large language models (LLMs) typically report attack success rate (ASR) under fixed query budgets, implicitly treating all attacks as equally…
TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior
Gül Sena Altıntaş, Malikeh Ehghaghi, Brian Lester +4
Tokenizers provide the fundamental basis through which text is represented and processed by language models (LMs). Despite the importance of tokenization, its role in LM performanc…
Merging in a Bottle: Differentiable Adaptive Merging (DAM) and the Path from Averaging to Automation
Thomas Gauthier-Caron, Shamane Siriwardhana, Elliot Stein +5
By merging models, AI systems can combine the distinct strengths of separate language models, achieving a balance between multiple capabilities without requiring substantial retrai…
Zero-Shot Multi-Lingual Speaker Verification in Clinical Trials
Ali Akram, Marija Stanojevic, Malikeh Ehghaghi +1
Due to the substantial number of clinicians, patients, and data collection environments involved in clinical trials, gathering data of superior quality poses a significant challeng…
Arcee's MergeKit: A Toolkit for Merging Large Language Models
Charles Goddard, Shamane Siriwardhana, Malikeh Ehghaghi +5
The rapid expansion of the open-source language model landscape presents an opportunity to merge the competencies of these model checkpoints by combining their parameters. Advances…
Factors Affecting the Performance of Automated Speaker Verification in Alzheimer's Disease Clinical Trials
Malikeh Ehghaghi, Marija Stanojevic, Ali Akram +1
Detecting duplicate patient participation in clinical trials is a major challenge because repeated patients can undermine the credibility and accuracy of the trial's findings and r…
DEPAC: a Corpus for Depression and Anxiety Detection from Speech
Mashrura Tasnim, Malikeh Ehghaghi, Brian Diep +1
Mental distress like depression and anxiety contribute to the largest proportion of the global burden of diseases. Automated diagnosis systems of such disorders, empowered by recen…
Data-driven Approach to Differentiating between Depression and Dementia from Noisy Speech and Language Data
Malikeh Ehghaghi, Frank Rudzicz, Jekaterina Novikova
A significant number of studies apply acoustic and linguistic characteristics of human speech as prominent markers of dementia and depression. However, studies on discriminating de…