Direct Preference Optimization for Chatbot Fine-Tuning: An Empirical Study
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Computer Science > Computation and Language
Title:Direct Preference Optimization for Chatbot Fine-Tuning: An Empirical Study
Abstract:We present an approach to fine-tuning large language models using Direct Preference Optimization (DPO), a reinforcement learning technique. Our experimental results demonstrate that DPO simplifies the training pipeline, improves computational efficiency, and achieves competitive performance. The evaluation using BLEU, ROUGE, and cosine similarity metrics indicates effective learning and convergence, though further investigation is needed to address observed training instability.
| Comments: | 7 pages, 3 figures, 1 table |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.12881 [cs.CL] |
| (or arXiv:2606.12881v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2606.12881
arXiv-issued DOI via DataCite (pending registration)
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