NICE Data Selection for Instruction Tuning in LLMs with Non-differentiable Evaluation Metric [ICML 2025]
This is the official implementation of the ICML 2025 paper "NICE Data Selection for Instruction Tuning in LLMs with Non-differentiable Evaluation Metric".
Our code are based on the code from LESS.
To get started with this repository, you'll need to install environment in environment.yml
In our project, for task-agnostic setting, we use four datasets: Flan v2, COT, Dolly, and Open Assistant.
For task-aware setting, we use two datasets: RLHF and Code-alpaca-20k.
For the purposes of evaluation, we evaluate on four datasets: AlpacaEval, TLDR, RLHF, HumanEval.
Dataset can be downloaded from link.
The selection commands are in running_commands.sh.
Follow the sequence to conduct data selection.
@inproceedings{
wang2025nice,
title={{NICE} Data Selection for Instruction Tuning in {LLM}s with Non-differentiable Evaluation Metric},
author={Jingtan Wang and Xiaoqiang Lin and Rui Qiao and Pang Wei Koh and Chuan-Sheng Foo and Bryan Kian Hsiang Low},
booktitle={Forty-second International Conference on Machine Learning},
year={2025}
}