@inproceedings{xu-etal-2024-automatic,
title = "Automatic Pair Construction for Contrastive Post-training",
author = "Xu, Canwen and
Rosset, Corby and
Chau, Ethan and
Corro, Luciano and
Mahajan, Shweti and
McAuley, Julian and
Neville, Jennifer and
Awadallah, Ahmed and
Rao, Nikhil",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2024",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-naacl.11",
pages = "149--162",
abstract = "Alignment serves as an important step to steer large language models (LLMs) towards human preferences. In this paper, we propose an automatic way to construct contrastive data for LLM, using preference pairs from multiple models of varying strengths (e.g., InstructGPT, ChatGPT and GPT-4). We compare the contrastive techniques of SLiC and DPO to SFT baselines and find that DPO provides a step-function improvement even after continuing SFT saturates. We also explore a data curriculum learning scheme for contrastive post-training, which starts by learning from {``}easier{''} pairs and transitioning to {``}harder{''} ones, which further improves alignment. Finally, we scale up our experiments to train with more data and larger models like Orca. Remarkably, our automatic contrastive post-training further improves the performance of Orca, already a state-of-the-art instruction learning model tuned with GPT-4 outputs, to outperform ChatGPT.",
}
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<abstract>Alignment serves as an important step to steer large language models (LLMs) towards human preferences. In this paper, we propose an automatic way to construct contrastive data for LLM, using preference pairs from multiple models of varying strengths (e.g., InstructGPT, ChatGPT and GPT-4). We compare the contrastive techniques of SLiC and DPO to SFT baselines and find that DPO provides a step-function improvement even after continuing SFT saturates. We also explore a data curriculum learning scheme for contrastive post-training, which starts by learning from “easier” pairs and transitioning to “harder” ones, which further improves alignment. Finally, we scale up our experiments to train with more data and larger models like Orca. Remarkably, our automatic contrastive post-training further improves the performance of Orca, already a state-of-the-art instruction learning model tuned with GPT-4 outputs, to outperform ChatGPT.</abstract>
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%0 Conference Proceedings
%T Automatic Pair Construction for Contrastive Post-training
%A Xu, Canwen
%A Rosset, Corby
%A Chau, Ethan
%A Corro, Luciano
%A Mahajan, Shweti
%A McAuley, Julian
%A Neville, Jennifer
%A Awadallah, Ahmed
%A Rao, Nikhil
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Findings of the Association for Computational Linguistics: NAACL 2024
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F xu-etal-2024-automatic
%X Alignment serves as an important step to steer large language models (LLMs) towards human preferences. In this paper, we propose an automatic way to construct contrastive data for LLM, using preference pairs from multiple models of varying strengths (e.g., InstructGPT, ChatGPT and GPT-4). We compare the contrastive techniques of SLiC and DPO to SFT baselines and find that DPO provides a step-function improvement even after continuing SFT saturates. We also explore a data curriculum learning scheme for contrastive post-training, which starts by learning from “easier” pairs and transitioning to “harder” ones, which further improves alignment. Finally, we scale up our experiments to train with more data and larger models like Orca. Remarkably, our automatic contrastive post-training further improves the performance of Orca, already a state-of-the-art instruction learning model tuned with GPT-4 outputs, to outperform ChatGPT.
%U https://aclanthology.org/2024.findings-naacl.11
%P 149-162
Markdown (Informal)
[Automatic Pair Construction for Contrastive Post-training](https://aclanthology.org/2024.findings-naacl.11) (Xu et al., Findings 2024)
ACL
- Canwen Xu, Corby Rosset, Ethan Chau, Luciano Corro, Shweti Mahajan, Julian McAuley, Jennifer Neville, Ahmed Awadallah, and Nikhil Rao. 2024. Automatic Pair Construction for Contrastive Post-training. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 149–162, Mexico City, Mexico. Association for Computational Linguistics.