![]() ![]() Our code is open-sourced at this https URL. Notably, fine-tuning with TDPO strikes a better balance than DPO in the controlled sentiment generation and single-turn dialogue datasets, and significantly improves the quality of generated responses compared to both DPO and PPO-based RLHF methods. ![]() Experimental results across various text tasks demonstrate TDPO's superior performance in balancing alignment with generation diversity. Utilizing the Bradley-Terry model for a token-based reward system, TDPO enhances the regulation of KL divergence, while preserving simplicity without the need for explicit reward modeling. Unlike previous methods, which face challenges in divergence efficiency, TDPO incorporates forward KL divergence constraints for each token, improving alignment and diversity. In this paper, we introduce Token-level Direct Preference Optimization (TDPO), a novel approach to align LLMs with human preferences by optimizing policy at the token level. Show more Why users love our Calculus Calculator. Here is another classic calculus problem: A woman has a 100 feet of fencing, a small dog, and a large yard that contains a. This can be used to solve problems in a wide range of fields, including physics, engineering, and economics. However, the generation of these responses occurs in a token level, following a sequential, auto-regressive fashion. Integral calculus is a branch of calculus that includes the determination, properties, and application of integrals. This process often utilizes methods like pairwise comparisons and KL divergence against a reference LLM, focusing on the evaluation of full answers generated by the models. View a PDF of the paper titled Token-level Direct Preference Optimization, by Yongcheng Zeng and 5 other authors View PDF HTML (experimental) Abstract:Fine-tuning pre-trained Large Language Models (LLMs) is essential to align them with human values and intentions.
0 Comments
Leave a Reply. |