Big vision-language models, or LVLMs, can interpret visual cues and supply easy replies for users to interact with. That is achieved by skillfully fusing large language models (LLMs) with large-scale visual instruction finetuning. Nevertheless, LVLMs only need hand-crafted or LLM-generated datasets for alignment by supervised fine-tuning (SFT). Even though it works well to vary LVLMs from caption generators to models that obey instructions, LVLMs can still produce replies which can be hurtful, ill-intentioned, or useless. This means that they still should be more aligned with human preferences. Moreover, while previous research encourages the organization of visual instruction tuning samples in multi-turn forms, the LVLMs’ capability to interact is restricted by the weak connections and interdependence between different turns. Here, the interaction ability assesses how well LVLMs can adjust their replies using the prior context in multi-turn interactions. These two drawbacks limit the sensible use of LVLMs as visual helpers. 

The research team from  SRI International and the University of Illinois Urbana-Champaign presents DRESS, an LVLM that’s uniquely taught using Natural Language Feedback (NLF) produced by LLMs on this work (confer with Figure 1). The research team instructs LLMs to supply fine-grained feedback on the LVLM’s replies by providing them with specific rules and extensive photo annotation. In step with the strategy of creating human-aligned LLMs, this feedback annotation considers the three H criteria: helpfulness, honesty, and harmlessness. The feedback measures the replies’ overall quality along the 3H criteria and provides a numerical rating and NLF. The research team’s method divides NLF into critique and refining. It is a novel classification. While the refinement NLF offers precise recommendations to LVLMs on improving their replies to align with the bottom truth reference, the critique NLF evaluates the responses’ strengths and faults. This classification provides a natural application of two sorts of NLF to make LVLMs more palatable to humans and enhance their interaction capabilities. 

The research team generalizes the conditional reinforcement learning technique to fulfill the non-differentiable character of NLF and trains the LVLMs with such feedback. Specifically, the research team uses linguistic modeling (LM) loss on the replies to coach DRESS to generate equivalent responses conditioned on the 2 NLFs. The research team refines DRESS by analyzing and interpreting the numerical results to match user preferences higher. Through multi-turn interactions during inference, the research team trains DRESS to learn the meta-skill of refining its original replies by employing refinement NLF. 

The research team assesses DRESS on multi-turn interactions, adversarial prompting for harmlessness assessment, picture captioning for honesty assessment, and open-ended visual query responding for helpfulness evaluation. The experiments’ findings show that, in comparison with earlier LVLMs, DRESS can provide replies that align with human values and have superior interaction capabilities that allow it to learn from feedback and modify responses as needed efficiently. To their knowledge, the research team’s effort is the primary to handle the interaction ability and all three 3H criteria for LVLMs. 

The research team’s contributions are summed up as follows: 

• The research team suggests using natural language feedback (NLF), which could also be divided into critique and refining NLF, to boost LVLMs’ ability to interact and align with human preferences. 

• By training the model to supply matching responses conditioned on the NLF, the research team generalizes the conditional reinforcement learning method to accommodate the non-differentiable NLF successfully. In comparison with the previous SOTA, the research team’s suggested model, DRESS, demonstrates relative improvements of 9.76%, 11.52%, and 21.03% based on a scientific evaluation of helpfulness, honesty, and harmlessness alignment. 

• The research group generates and makes 63K annotated language NLF examples available for public use, including 3H characteristics. Moreover, the research team created a publicly available dataset of 4.7K samples for harmlessness alignment and LVLM assessment. 

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