Computer Science > Computation and Language
arXiv:2310.02263 (cs)
[Submitted on 3 Oct 2023 (v1), last revised 3 Apr 2024 (this version, v2)]
Title:Automatic Pair Construction for Contrastive Post-training
Authors:Canwen Xu,Corby Rosset,Ethan C. Chau,Luciano Del Corro,Shweti Mahajan,Julian McAuley,Jennifer Neville,Ahmed Hassan Awadallah,Nikhil Rao
View a PDF of the paper titled Automatic Pair Construction for Contrastive Post-training, by Canwen Xu and 8 other authors
View PDFHTML (experimental)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.
| Comments: | NAACL 2024 (Findings) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) |
| Cite as: | arXiv:2310.02263 [cs.CL] |
| (orarXiv:2310.02263v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2310.02263 arXiv-issued DOI via DataCite |
Submission history
From: Canwen Xu [view email][v1] Tue, 3 Oct 2023 17:59:46 UTC (104 KB)
[v2] Wed, 3 Apr 2024 00:16:19 UTC (100 KB)
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View a PDF of the paper titled Automatic Pair Construction for Contrastive Post-training, by Canwen Xu and 8 other authors
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