Search Results for author:
Found 4 papers, 0 papers with code
Asynchronous Convergence in Multi-Task Learning via Knowledge Distillation from Converged Tasks
However, one issue that often arises in MTL is the convergence speed between tasks varies due to differences in task difficulty, so it can be a challenge to simultaneously achieve the best performance on all tasks with a single model checkpoint.
AMPO: Automatic Multi-Branched Prompt Optimization
Prompt engineering is very important to enhance the performance of large language models (LLMs).
StraGo: Harnessing Strategic Guidance for Prompt Optimization
Prompt engineering is pivotal for harnessing the capabilities of large language models (LLMs) across diverse applications.
DynaMaR: Dynamic Prompt with Mask Token Representation
In this paper, we propose an improvement to prompt-based fine-tuning that addresses these two issues.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
