Knowledge-based visual question answering (VQA) requires external knowledge
beyond the image to answer the question. Early studies retrieve required
knowledge from explicit knowledge bases (KBs), which often introduces
irrelevant information to the question, hence restricting the performance of
their models. Recent works have sought to use a large language model (i.e.,
GPT-3) as an implicit knowledge engine to acquire the necessary knowledge for
answering. Despite the encouraging results achieved by these methods, we argue
that they have not fully activated the capacity of GPT-3 as the provided input
information is insufficient. In this paper, we present Prophet -- a
conceptually simple framework designed to prompt GPT-3 with answer heuristics
for knowledge-based VQA. Specifically, we first train a vanilla VQA model on a
specific knowledge-based VQA dataset without external knowledge. After that, we
extract two types of complementary answer heuristics from the model: answer
candidates and answer-aware examples. Finally, the two types of answer
heuristics are encoded into the prompts to enable GPT-3 to better comprehend
the task thus enhancing its capacity. Prophet significantly outperforms all
existing state-of-the-art methods on two challenging knowledge-based VQA
datasets, OK-VQA and A-OKVQA, delivering 61.1% and 55.7% accuracies on their
testing sets, respectively.

本论文提出了一种名为 “Prophet” 的框架，采用两个补充答案启发式条目对 GPT-3 进行提示，以提高其理解任务的能力，从而在基于知识的视觉问答方面显著优于所有现有的最先进方法，分别在 OK-VQA 和 A-OKVQA 的测试集上达到了 61.1％和 55.7％的准确率。

使用答案启发式来启发大型语言模型进行基于知识的视觉问答

Prompting Large Language Models with Answer Heuristics for Knowledge-based Visual Question Answering

Offline reinforcement learning (RL) methods can generally be categorized into
two types: RL-based and Imitation-based. RL-based methods could in principle
enjoy out-of-distribution generalization but suffer from erroneous off-policy
evaluation. Imitation-based methods avoid off-policy evaluation but are too
conservative to surpass the dataset. In this study, we propose an alternative
approach, inheriting the training stability of imitation-style methods while
still allowing logical out-of-distribution generalization. We decompose the
conventional reward-maximizing policy in offline RL into a guide-policy and an
execute-policy. During training, the guide-poicy and execute-policy are learned
using only data from the dataset, in a supervised and decoupled manner. During
evaluation, the guide-policy guides the execute-policy by telling where it
should go so that the reward can be maximized, serving as the \textit{Prophet}.
By doing so, our algorithm allows \textit{state-compositionality} from the
dataset, rather than \textit{action-compositionality} conducted in prior
imitation-style methods. We dumb this new approach Policy-guided Offline RL
(\texttt{POR}). \texttt{POR} demonstrates the state-of-the-art performance on
D4RL, a standard benchmark for offline RL. We also highlight the benefits of
\texttt{POR} in terms of improving with supplementary suboptimal data and
easily adapting to new tasks by only changing the guide-poicy.

该研究提出了一种 Policy-guided Offline RL 算法，该算法在训练时将想法分解为指导策略和执行策略，并通过指导策略来指导执行策略以实现状态组合性。该算法在离线 RL 的标准基准 D4RL 上展示了最高效的性能，并可以通过改变指导策略来轻松适应新的任务。