LLMs-as-instructors : Learning from errors toward automating model improvement
This paper introduces the innovative "LLMs-as-Instructors'' framework, which leverages the advanced Large Language Models (LLMs) to autonomously enhance the training of smaller target models. Inspired by the theory of "Learning from Errors'', this framework employs an i...
Saved in:
Main Authors: | YING, Jiahao, LIN, Mingbao, CAO, Yixin, TANG, Wei, WANG, Bo, SUN, Qianru, HUANG, Xuanjing, YAN, Shuicheng |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2024
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/9440 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
Automating dataset updates towards reliable and timely evaluation of Large Language Models
by: YING, Jiahao, et al.
Published: (2024) -
CoSec : On-the-Fly security hardening of code LLMs via supervised co-decoding
by: LI, Dong, et al.
Published: (2024) -
Evaluation of Orca 2 against other LLMs for Retrieval Augmented Generation
by: HUANG, Donghao, et al.
Published: (2024) -
Performance analysis of Llama 2 among other LLMs
by: HUANG, Donghao, et al.
Published: (2024) -
Experience Report: Identifying common misconceptions and errors of novice programmers with ChatGPT
by: FWA, Hua Leong
Published: (2024)