Automating dataset updates towards reliable and timely evaluation of Large Language Models
Large language models (LLMs) have achieved impressive performance across various natural language benchmarks, prompting a continual need to curate more difficult datasets for larger LLMs, which is costly and time-consuming. In this paper, we propose to automate dataset updating and provide systemati...
Saved in:
Main Authors: | YING, Jiahao, CAO, Yixin, BAI, Yushi, SUN, Qianru, WANG, Bo, TANG, Wei, DING, Zhaojun, YANG, Yizhe, 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/9439 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
LLMs-as-instructors : Learning from errors toward automating model improvement
by: YING, Jiahao, et al.
Published: (2024) -
Eigenvalues and switching algorithms for Quasi-Newton updates
by: Phua, P.K.H.
Published: (2014) -
We challenge you to certify your updates
by: Chen, S., et al.
Published: (2013) -
View update in entity-relationship approach
by: Ling, T.W., et al.
Published: (2014) -
Combating obsolescence: Predictors of technical updating among engineers
by: Aryee, S.
Published: (2013)