Fine-Grained Control and Manipulation of Large Language Models: Conditioning and Prompting Strategies

EasyChair Preprint 12282

Abstract

This paper presents an overview of recent advancements in fine-grained control techniques for LLMs, focusing on conditioning and prompting strategies. The ability to effectively control and manipulate large language models (LLMs) has become a pivotal area of research, offering promising avenues for tailored text generation and task-oriented language understanding. The implications of these techniques in enhancing LLM performance across diverse applications, including text generation, sentiment analysis, and language translation, are investigated. Lastly, challenges and future directions in the field are highlighted, emphasizing the importance of robustness, interpretability, and ethical considerations in the design and deployment of controlled LLMs.

Keyphrases: language, large, models

@booklet{EasyChair:12282,
  author    = {Kurez Oroy and Emily Anderson},
  title     = {Fine-Grained Control and Manipulation of Large Language Models: Conditioning and Prompting Strategies},
  howpublished = {EasyChair Preprint 12282},
  year      = {EasyChair, 2024}}