1 citations · 1 across the 6 of their papers we have counts for
6 papers
Intrinsic Selection and Particle Resampling for Inference-Time Scaling Beyond Domain Verifiability
Giorgio Giannone, Mustafa Eyceoz, Shabana Baig +5
Inference-Time Scaling (ITS) has largely succeeded in verifiable domains like math and coding, where cheap verification enables scalable output selection. However, extending ITS to…
Hopscotch: Discovering and Skipping Redundancies in Language Models
Mustafa Eyceoz, Nikhil Shivakumar Nayak, Hao Wang +2
Modern causal language models stack many attention blocks to improve performance, but not all blocks are necessary for every task. We propose Hopscotch, a simple yet effective meth…
Sculpting Subspaces: Constrained Full Fine-Tuning in LLMs for Continual Learning
Nikhil Shivakumar Nayak, Krishnateja Killamsetty, Ligong Han +8
Continual learning in large language models (LLMs) is prone to catastrophic forgetting, where adapting to new tasks significantly degrades performance on previously learned ones. E…
Extractive Schema Linking for Text-to-SQL
Michael Glass, Mustafa Eyceoz, Dharmashankar Subramanian +3
Text-to-SQL is emerging as a practical interface for real world databases. The dominant paradigm for Text-to-SQL is cross-database or schema-independent, supporting application sch…
Robust Open-Set Spoken Language Identification and the CU MultiLang Dataset
Mustafa Eyceoz, Justin Lee, Siddharth Pittie +1
Most state-of-the-art spoken language identification models are closed-set; in other words, they can only output a language label from the set of classes they were trained on. Open…
Modernizing Open-Set Speech Language Identification
Mustafa Eyceoz, Justin Lee, Homayoon Beigi
While most modern speech Language Identification methods are closed-set, we want to see if they can be modified and adapted for the open-set problem. When switching to the open-set…