3 papers
cs.CL2026
Unlearning in LLMs: Methods, Evaluation, and Open Challenges
Tyler Lizzo, Larry Heck
Large language models (LLMs) have achieved remarkable success across natural language processing tasks, yet their widespread deployment raises pressing concerns around privacy, cop…
cs.CL2026
Evaluating Cross-Lingual Unlearning in Multilingual Language Models
Tyler Lizzo, Larry Heck
We present the first comprehensive evaluation of cross-lingual unlearning in multilingual LLMs. Using translated TOFU benchmarks in seven language/script variants, we test major un…
cs.LG2025
Draft, Verify, and Improve: Toward Training-Aware Speculative Decoding
Shrenik Bhansali, Larry Heck
Autoregressive (AR) decoding is a major latency bottleneck for large language models. Speculative decoding (SD) accelerates AR by letting a drafter propose multi-token blocks that…