Zephyr: Direct Distillation of LM Alignment: Appendix

by Writings, Papers and Blogs on Text ModelsJuly 3rd, 2024

Too Long; Didn't Read

In this study, researchers aim to produce a smaller language model that is aligned to user intent.

featured image - Zephyr: Direct Distillation of LM Alignment: Appendix

Authors:

(1) Lewis Tunstall, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team (email: [email protected]);

(2) Edward Beeching, Equal contribution and The H4 (Helpful, Honest, Harmless, Huggy) Team;

(3) Nathan Lambert, The H4 (Helpful, Honest, Harmless, Huggy) Team;

(4) Nazneen Rajani, The H4 (Helpful, Honest, Harmless, Huggy) Team;

(5) Kashif Rasul, The H4 (Helpful, Honest, Harmless, Huggy) Team;

(6) Younes Belkada, The H4 (Helpful, Honest, Harmless, Huggy) Team;

(7) Shengyi Huang, The H4 (Helpful, Honest, Harmless, Huggy) Team;

(8) Leandro von Werra, The H4 (Helpful, Honest, Harmless, Huggy) Team;

(9) Clementine Fourrier, The H4 (Helpful, Honest, Harmless, Huggy) Team;

(10) Nathan Habib, The H4 (Helpful, Honest, Harmless, Huggy) Team;

(11) Nathan Sarrazin, The H4 (Helpful, Honest, Harmless, Huggy) Team;

(12) Omar Sanseviero, The H4 (Helpful, Honest, Harmless, Huggy) Team;

(13) Alexander M. Rush, The H4 (Helpful, Honest, Harmless, Huggy) Team;

(14) Thomas Wolf, The H4 (Helpful, Honest, Harmless, Huggy) Team.

Table of Links

A APPENDIX

A.1 QUALITATIVE EXAMPLES

To qualitatively compare the responses from our dSFT and dDPO models, we choose prompts from a few domains of MT-Bench, as well as some adversarial prompts to test each model’s capability to follow instructions with false premises or harmful intent. Completions for the adversarial prompts were generated with nucleus sampling(top-p = 0.95) and T = 0.7.

A.2 SFT IS A REQUIRED STEP BEFORE DPO

In Table 3 we ran an ablation to see whether SFT is necessary prior to the DPO step. We observed a significant reduction in performance in both the MT-Bench and AlpacaEval scores when the SFT step is skipped. After a qualitative evaluation of the MT-Bench generations, we observe that the pure DPO model struggles to learn the chat template: