Direct Preference Optimization Graph
%20directly%20optimizes%20the%20generative%20model%20with%20preference%20data%2C%20skipping%20reinforcement%20learning.%20However%2C%20both%20RLHF%20and%20DPO%20assume%20uniform%20preferences%2C%20overlooking%20the%20reality%20of%20diverse%20human%20annotators.%20This%20paper%20presents%20a%20new%20method%20to%20align%20generative%20models%20with%20varied%20human%20preferences.%20We%20propose%20an%20Expectation-Maximization%20adaptation%20to%20DPO%2C%20generating%20a%20mixture%20of%20models%20based%20on%20latent%20preference%20types%20of%20the%20annotators.%20We%20then%20introduce%20a%20min-max%20regret%20ensemble%20learning%20model%20to%20produce%20a%20single%20generative%20method%20to%20minimize%20worst-case%20regret%20among%20annotator%20subgroups%20with%20similar%20latent%20factors.%20Our%20algorithms%20leverage%20the%20simplicity%20of%20DPO%20while%20accommodating%20diverse%20preferences.%20Experimental%20results%20validate%20the%20effectiveness%20of%20our%20approach%20in%20producing%20equitable%20generative%20policies.)
![[D] what's the proper way of doing direct preference optimization (DPO ...](https://preview.redd.it/d-whats-the-proper-way-of-doing-direct-preference-v0-6c9z61o4bbfc1.png?width=2164&format=png&auto=webp&s=7868907b815f27af565f032b0c1acf83b23a8fa4)
-4.png)
%2C%20has%20shown%20effective%20in%20enhancing%20the%20generalization%20abilities%20of%20large%20language%20model%20(LLM).%20However%2C%20in%20tasks%20involving%20video%20instruction-following%2C%20providing%20informative%20feedback%2C%20especially%20for%20detecting%20hallucinations%20in%20generated%20responses%2C%20remains%20a%20significant%20challenge.%20Previous%20studies%20have%20explored%20using%20large%20large%20multimodal%20models%20(LMMs)%20as%20reward%20models%20to%20guide%20preference%20modeling%2C%20but%20their%20ability%20to%20accurately%20assess%20the%20factuality%20of%20generated%20responses%20compared%20to%20corresponding%20videos%20has%20not%20been%20conclusively%20established.%20This%20paper%20introduces%20a%20novel%20framework%20that%20utilizes%20detailed%20video%20captions%20as%20a%20proxy%20of%20video%20content%2C%20enabling%20language%20models%20to%20incorporate%20this%20information%20as%20supporting%20evidence%20for%20scoring%20video%20Question%20Answering%20(QA)%20predictions.%20Our%20approach%20demonstrates%20robust%20alignment%20with%20OpenAI%20GPT-4V%20model's%20reward%20mechanism%2C%20which%20directly%20takes%20video%20frames%20as%20input.%20Furthermore%2C%20we%20show%20that%20applying%20this%20tailored%20reward%20through%20DPO%20significantly%20improves%20the%20performance%20of%20video%20LMMs%20on%20video%20QA%20tasks.)
%20is%20essential%20to%20align%20them%20with%20human%20values%20and%20intentions.%20This%20process%20often%20utilizes%20methods%20like%20pairwise%20comparisons%20and%20KL%20divergence%20against%20a%20reference%20LLM%2C%20focusing%20on%20the%20evaluation%20of%20full%20answers%20generated%20by%20the%20models.%20However%2C%20the%20generation%20of%20these%20responses%20occurs%20in%20a%20token%20level%2C%20following%20a%20sequential%2C%20auto-regressive%20fashion.%20In%20this%20paper%2C%20we%20introduce%20Token-level%20Direct%20Preference%20Optimization%20(TDPO)%2C%20a%20novel%20approach%20to%20align%20LLMs%20with%20human%20preferences%20by%20optimizing%20policy%20at%20the%20token%20level.%20Unlike%20previous%20methods%2C%20which%20face%20challenges%20in%20divergence%20efficiency%2C%20TDPO%20incorporates%20forward%20KL%20divergence%20constraints%20for%20each%20token%2C%20improving%20alignment%20and%20diversity.%20Utilizing%20the%20Bradley-Terry%20model%20for%20a%20token-based%20reward%20system%2C%20TDPO%20enhances%20the%20regulation%20of%20KL%20divergence%2C%20while%20preserving%20simplicity%20without%20the%20need%20for%20explicit%20reward%20modeling.%20Experimental%20results%20across%20various%20text%20tasks%20demonstrate%20TDPO's%20superior%20performance%20in%20balancing%20alignment%20with%20generation%20diversity.%20Notably%2C%20fine-tuning%20with%20TDPO%20strikes%20a%20better%20balance%20than%20DPO%20in%20the%20controlled%20sentiment%20generation%20and%20single-turn%20dialogue%20datasets%2C%20and%20significantly%20improves%20the%20quality%20of%20generated%20responses%20compared%20to%20both%20DPO%20and%20PPO-based%20RLHF%20methods.%20Our%20code%20is%20open-sourced%20at%20https:%2F%2Fgithub.com%2FVance0124%2FToken-level-Direct-Preference-Optimization.)
Discover the vibrancy of city life through vast arrays of urban Direct Preference Optimization Graph photographs. highlighting the diversity of photography, images, and pictures in urban settings. designed to showcase urban culture and lifestyle. Each Direct Preference Optimization Graph image is carefully selected for superior visual impact and professional quality. Suitable for various applications including web design, social media, personal projects, and digital content creation All Direct Preference Optimization Graph images are available in high resolution with professional-grade quality, optimized for both digital and print applications, and include comprehensive metadata for easy organization and usage. Discover the perfect Direct Preference Optimization Graph images to enhance your visual communication needs. Reliable customer support ensures smooth experience throughout the Direct Preference Optimization Graph selection process. Multiple resolution options ensure optimal performance across different platforms and applications. Each image in our Direct Preference Optimization Graph gallery undergoes rigorous quality assessment before inclusion. Regular updates keep the Direct Preference Optimization Graph collection current with contemporary trends and styles. Whether for commercial projects or personal use, our Direct Preference Optimization Graph collection delivers consistent excellence. Diverse style options within the Direct Preference Optimization Graph collection suit various aesthetic preferences. The Direct Preference Optimization Graph collection represents years of careful curation and professional standards.