4 papers
Making VLMs More Robot-Friendly: Self-Critical Distillation of Low-Level Procedural Reasoning
Chan Young Park, Jillian Fisher, Marius Memmel +4
Large language models (LLMs) have shown promise in robotic procedural planning, yet their human-centric reasoning often omits the low-level, grounded details needed for robotic exe…
StyleRemix: Interpretable Authorship Obfuscation via Distillation and Perturbation of Style Elements
Jillian Fisher, Skyler Hallinan, Ximing Lu +3
Authorship obfuscation, rewriting a text to intentionally obscure the identity of the author, is an important but challenging task. Current methods using large language models (LLM…
JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models
Jillian Fisher, Ximing Lu, Jaehun Jung +3
The permanence of online content combined with the enhanced authorship identification techniques calls for stronger computational methods to protect the identity and privacy of onl…
FiLM: Fill-in Language Models for Any-Order Generation
Tianxiao Shen, Hao Peng, Ruoqi Shen +3
Language models have become the backbone of today's AI systems. However, their predominant left-to-right generation limits the use of bidirectional context, which is essential for…