4 papers
TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior
Gül Sena AltıntaÅ, 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…
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…
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…
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…