Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality

17 June 2025

Papers citing "Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality"

7 / 7 papers shown

Title
Long Is More for Alignment: A Simple but Tough-to-Beat Baseline for Instruction Fine-Tuning Hao Zhao Maksym Andriushchenko Francesco Croce Nicolas Flammarion ALM 136 55 0 07 Feb 2024
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena Lianmin Zheng Wei-Lin Chiang Ying Sheng Siyuan Zhuang Zhanghao Wu ... Dacheng Li Eric Xing Haotong Zhang Joseph E. Gonzalez Ion Stoica ALM OSLM ELM 391 4,388 0 09 Jun 2023
Program Synthesis with Large Language Models Jacob Austin Augustus Odena Maxwell Nye Maarten Bosma Henryk Michalewski ... Ellen Jiang Carrie J. Cai Michael Terry Quoc V. Le Charles Sutton ELM AIMat ReCod ALM 200 1,986 0 16 Aug 2021
Evaluating Large Language Models Trained on Code Mark Chen Jerry Tworek Heewoo Jun Qiming Yuan Henrique Pondé ... Bob McGrew Dario Amodei Sam McCandlish Ilya Sutskever Wojciech Zaremba ELM ALM 233 5,635 0 07 Jul 2021
Aligning AI With Shared Human Values Dan Hendrycks Collin Burns Steven Basart Andrew Critch Jingkai Li Basel Alomair Jacob Steinhardt 145 569 0 05 Aug 2020
BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions Christopher Clark Kenton Lee Ming-Wei Chang Tom Kwiatkowski Michael Collins Kristina Toutanova 227 1,549 0 24 May 2019
TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension Mandar Joshi Eunsol Choi Daniel S. Weld Luke Zettlemoyer RALM 210 2,676 0 09 May 2017