9 citations · 22 across the 32 of their papers we have counts for
4 papers · 1 filter
Feature-Guided SAE Steering for Refusal-Rate Control using Contrasting Prompts
Samaksh Bhargav, Zining Zhu
Large Language Model (LLM) deployment requires guiding the LLM to recognize and not answer unsafe prompts while complying with safe prompts. Previous methods for achieving this req…
KerZOO: Kernel Function Informed Zeroth-Order Optimization for Accurate and Accelerated LLM Fine-Tuning
Zhendong Mi, Qitao Tan, Xiaodong Yu +3
Large language models (LLMs) have demonstrated impressive capabilities across numerous NLP tasks. Nevertheless, conventional first-order fine-tuning techniques impose heavy memory…
Distribution Prompting: Understanding the Expressivity of Language Models Through the Next-Token Distributions They Can Produce
Haojin Wang, Zining Zhu, Freda Shi
Autoregressive neural language models (LMs) generate a probability distribution over tokens at each time step given a prompt. In this work, we attempt to systematically understand…
Contrastive Similarity Learning for Market Forecasting: The ContraSim Framework
Nicholas Vinden, Raeid Saqur, Zining Zhu +1
We introduce the Contrastive Similarity Space Embedding Algorithm (ContraSim), a novel framework for uncovering the global semantic relationships between daily financial headlines…