DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback
Authors: Yangyi Chen, Karan Sikka, Michael Cogswell, Heng Ji, Ajay Divakaran

We present DRESS a large vision language model (LVLM) that innovatively exploits Natural Language feedback (NLF) from Large Language Models to enhance its alignment and interactions by addressing two key limitations in the state-of-the-art LVLMs. First prior LVLMs generally rely only on the instruction finetuning stage to enhance alignment with human preferences. Without incorporating extra feedback they are still prone to generate unhelpful hallucinated or harmful responses. Second while the visual instruction tuning data is generally structured in a multi-turn dialogue format the connections and dependencies among consecutive conversational turns are weak. This reduces the capacity for effective multi-turn interactions. To tackle these we propose a novel categorization of the NLF into two key types: critique and refinement. The critique NLF identifies the strengths and weaknesses of the responses and is used to align the LVLMs with human preferences. The refinement NLF offers concrete suggestions for improvement and is adopted to improve the interaction ability of the LVLMs-- which focuses on LVLMs' ability to refine responses by incorporating feedback in multi-turn interactions. To address the non-differentiable nature of NLF we generalize conditional reinforcement learning for training. Our experimental results demonstrate that DRESS can generate more helpful (9.76%) honest (11.52%) and harmless (21.03%) responses and more effectively learn from feedback during multi-turn interactions compared to SOTA LVLMs.