@inproceedings{feng-etal-2025-sequence,
title = "Sequence-level Large Language Model Training with Contrastive Preference Optimization",
author = "Feng, Zhili and
Ram, Dhananjay and
Hawkins, Cole and
Rawal, Aditya and
Zhao, Jinman and
Zha, Sheng",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.233/",
doi = "10.18653/v1/2025.findings-naacl.233",
pages = "4158--4164",
ISBN = "979-8-89176-195-7",
abstract = "The next token prediction loss is the dominant self-supervised training objective for large language models and has achieved promising results in a variety of downstream tasks. However, upon closer investigation of this objective, we find that it lacks an understanding of sequence-level signals, leading to a mismatch between training and inference processes. To bridge this gap, we introduce a contrastive preference optimization (CPO) procedure that can inject sequence-level information into the language model at any training stage without expensive human labeled data. Our experiments show that the proposed objective surpasses the next token prediction in terms of win rate in the instruction-following and text generation tasks."
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<abstract>The next token prediction loss is the dominant self-supervised training objective for large language models and has achieved promising results in a variety of downstream tasks. However, upon closer investigation of this objective, we find that it lacks an understanding of sequence-level signals, leading to a mismatch between training and inference processes. To bridge this gap, we introduce a contrastive preference optimization (CPO) procedure that can inject sequence-level information into the language model at any training stage without expensive human labeled data. Our experiments show that the proposed objective surpasses the next token prediction in terms of win rate in the instruction-following and text generation tasks.</abstract>
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%0 Conference Proceedings
%T Sequence-level Large Language Model Training with Contrastive Preference Optimization
%A Feng, Zhili
%A Ram, Dhananjay
%A Hawkins, Cole
%A Rawal, Aditya
%A Zhao, Jinman
%A Zha, Sheng
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F feng-etal-2025-sequence
%X The next token prediction loss is the dominant self-supervised training objective for large language models and has achieved promising results in a variety of downstream tasks. However, upon closer investigation of this objective, we find that it lacks an understanding of sequence-level signals, leading to a mismatch between training and inference processes. To bridge this gap, we introduce a contrastive preference optimization (CPO) procedure that can inject sequence-level information into the language model at any training stage without expensive human labeled data. Our experiments show that the proposed objective surpasses the next token prediction in terms of win rate in the instruction-following and text generation tasks.
%R 10.18653/v1/2025.findings-naacl.233
%U https://aclanthology.org/2025.findings-naacl.233/
%U https://doi.org/10.18653/v1/2025.findings-naacl.233
%P 4158-4164
Markdown (Informal)
[Sequence-level Large Language Model Training with Contrastive Preference Optimization](https://aclanthology.org/2025.findings-naacl.233/) (Feng et al., Findings 2025)
ACL