Fine-Tuning Optimization of Small Language Models: A Novel Graph-Theoretical Approach for Efficient Prompt Engineering-Reference-Cited by-同舟云学术

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Fine-Tuning Optimization of Small Language Models: A Novel Graph-Theoretical Approach for Efficient Prompt Engineering

Published:2024-06-19 Issue: Volume:20 Page:1-7
ISSN:
Container-title:2024 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)
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Author:

Gadiraju Venkata¹,Tsai Hao-Yu²,Wu Hsiao-Chun³,Singha Manali⁴,Scott Huang C.-H.²,Liu Guannan⁴,Chang Shih Yu⁵,Wu Yiyan⁶

Affiliation:

1. Louisiana State University,School of Electrical Engineering and Computer Science,Baton Rouge,USA

2. National Tsing Hua University,Department of Electrical Engineering,Hsinchu,Taiwan,30013

3. Yuan Ze University,Innovation Center for AI Applications,Chungli,Taiwan,32003

4. Institute for Genomic Medicine (IGM), Nationwide Children’s Hospital,Columbus,USA

5. San Jose State University,Department of Applied Data Science,San Jose,USA

6. Communications Research Centre,Ottawa,Canada

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10608145/10608191/10608341.pdf?arnumber=10608341

Reference32 articles.

1. Prompting Large Language Models with Answer Heuristics for Knowledge-Based Visual Question Answering

2. Methods to Optimize Wav2Vec with Language Model for Automatic Speech Recognition in ResourceConstrained Environment;Haswani

3. It’s not just size that matters: Small language models are also few-shot learners;Schick;arXiv:2009.07118,2020

4. How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering

5. Enhancing Question Prediction with flan t5-a context-aware language model approach;Oza;Authorea Preprints,2023

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