![]() ![]() HOW do you get a sound to come out of the speakers attached to your computer? :>īTW, I have an M-Audio Oxygen 49. The proposed LLaVA-RLHF model achieves significant improvements in human alignment benchmarks like LLaVA-Bench (+10%) and MMH_AL-BENCH (+60%) over baselines, establishing new state-of-the-art results.So, like me, you just got your MIDI keyboard controller, installed LMMS and the latest drivers for your keyboard. Evaluated on LLaVA-Bench, MMH_AL-BENCH (new benchmark to detect hallucinations), MMBench, and POPE.Added symbolic rewards for correctness and length to prevent reward hacking.Introduced Factually Augmented RLHF which utilizes additional factual information like image captions to calibrate the reward model.Performed RLHF on 50k LLaVA conversations to optimize against simulated human preferences.Collected human preferences for 10k responses by re-sampling LLaVA responses, emphasizing multimodal alignment and minimizing hallucinations.*Enriched the synthetic vision instruction tuning data from LLaVA with existing high-quality human-annotated image-text pairs (VQA-v2, A-OKVQA, Flickr30k). LLaVA-RLHF achieves state-of-the-art results across multiple benchmarks as the first LMM trained with RLHF.New benchmark MMH_AL-BENCH focuses on detecting hallucinations in LMM responses.Symbolic rewards help mitigate reward hacking issues in RLHF.Factually Augmented RLHF effectively utilizes existing human annotations to improve reward modeling.RLHF further enhances human alignment, reduces hallucination, and encourages truthfulness based on evaluations.High-quality instruction tuning data (VQA-v2, A-OKVQA, Flickr30k) significantly improves LMM capabilities on benchmarks.The paper aims to align large multimodal models (LMMs) with human values and reduce hallucinations by adapting reinforcement learning from human feedback (RLHF) to the multimodal domain. Level of the text-only GPT-4 (while previous best methods can only achieve theĨ7% level), and an improvement by 60% on MMHAL-BENCH over other baselines. Remarkable improvement on the LLaVA-Bench dataset with the 94% performance As the first LMM trained with RLHF, our approach achieves New evaluation benchmark MMHAL-BENCH with a special focus on penalizing To evaluate the proposed approach in real-world scenarios, we develop a Human-written image-text pairs to improve the general capabilities of our Training data (for vision instruction tuning) with previously available Multi-choice options, which alleviates the reward hacking phenomenon in RLHFĪnd further improves the performance. We propose a new alignmentĪlgorithm called Factually Augmented RLHF that augments the reward model withĪdditional factual information such as image captions and ground-truth Is trained to maximize the simulated human rewards. Responses and pinpoint the more hallucinated one, and the vision-language model Vision-language alignment, where human annotators are asked to compare two Learning from Human Feedback (RLHF) from the text domain to the task of To address the multimodal misalignment issue, we adapt the Reinforcement Textual outputs that are not grounded by the multimodal information in context. Misalignment between two modalities can result in "hallucination", generating Large Multimodal Models (LMM) are built across modalities and the ![]()
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