Search Results for author:
Found 4 papers, 2 papers with code
Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in LLMs
In an era where language models are increasingly integrated into decision-making and communication, understanding the biases within Large Language Models (LLMs) becomes imperative, especially when these models are applied in the economic and political domains.
MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning
Adapting models pre-trained on large-scale datasets to a variety of downstream tasks is a common strategy in deep learning.
GraPhSyM: Graph Physical Synthesis Model
Given a gate-level netlist of a circuit represented as a graph, GraPhSyM utilizes graph structure, connectivity, and electrical property features to predict the impact of physical synthesis transformations such as buffer insertion and gate sizing.
AdaMTL: Adaptive Input-dependent Inference for Efficient Multi-Task Learning
Specifically, we attach a task-aware lightweight policy network to the shared encoder and co-train it alongside the MTL model to recognize unnecessary computations.
