3 citations · 4 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…