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A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick

A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick

Nishant Balepur
Matthew Shu
Alexander Hoyle
Alison Robey
Shi Feng
Seraphina Goldfarb-Tarrant
Jordan Boyd-Graber
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Papers citing "A SMART Mnemonic Sounds like "Glue Tonic": Mixing LLMs with Student Feedback to Make Mnemonic Learning Stick"

5 / 5 papers shown
Title
LESS: Selecting Influential Data for Targeted Instruction Tuning
LESS: Selecting Influential Data for Targeted Instruction Tuning
Mengzhou Xia
Sadhika Malladi
Suchin Gururangan
Sanjeev Arora
Danqi Chen
73
180
0
06 Feb 2024
Can Large Language Models Be an Alternative to Human Evaluations?
Can Large Language Models Be an Alternative to Human Evaluations?
Cheng-Han Chiang
Hung-yi Lee
ALM
LM&MA
206
559
0
03 May 2023
Poisoning Language Models During Instruction Tuning
Poisoning Language Models During Instruction Tuning
Alexander Wan
Eric Wallace
Sheng Shen
Dan Klein
SILM
90
124
0
01 May 2023
The Authenticity Gap in Human Evaluation
The Authenticity Gap in Human Evaluation
Kawin Ethayarajh
Dan Jurafsky
79
24
0
24 May 2022
Fine-Tuning Language Models from Human Preferences
Fine-Tuning Language Models from Human Preferences
Daniel M. Ziegler
Nisan Stiennon
Jeff Wu
Tom B. Brown
Alec Radford
Dario Amodei
Paul Christiano
G. Irving
ALM
273
1,561
0
18 Sep 2019