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

3 Mar 2023CVPR 2023 1arXiv:2303.01903archive 2025-07-28

Zhou Yu, Xuecheng Ouyang, Zhenwei Shao, Meng Wang, Jun Yu

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 resorted to using a powerful large language model (LLM) 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 the \emph{blind} LLM as the provided textual input is insufficient to depict the required visual information to answer the question. In this paper, we present Prophet -- a conceptually simple, flexible, and general framework designed to prompt LLM 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 VQA model: answer candidates and answer-aware examples. The two types of answer heuristics are jointly encoded into a formatted prompt to facilitate the LLM's understanding of both the image and question, thus generating a more accurate answer. By incorporating the state-of-the-art LLM GPT-3, Prophet significantly outperforms existing state-of-the-art methods on four challenging knowledge-based VQA datasets. Prophet is general that can be instantiated with the combinations of different VQA models (i.e., both discriminative and generative ones) and different LLMs (i.e., both commercial and open-source ones). Moreover, Prophet can also be integrated with modern large multimodal models in different stages, which is named Prophet++, to further improve the capabilities on knowledge-based VQA tasks.

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aok_score milvlg/prophet/prophet/stage2/utils/data_utils.py official repository unverified Apache-2.0 (permissive) · 47ce2e0ae5037265 · report
apply_rotary_pos_emb milvlg/prophet/prophet/stage1/model/rope2d.py official repository unverified Apache-2.0 (permissive) · f8f937b414d7e0a9 · report
flatten milvlg/prophet/prophet/stage1/model/net_utils.py official repository unverified Apache-2.0 (permissive) · 0ae3d3fa436c0fdc · report
get_optim milvlg/prophet/prophet/stage1/utils/optim.py official repository unverified Apache-2.0 (permissive) · 4120d55fb48dd6b0 · report
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ok_score milvlg/prophet/prophet/stage2/utils/data_utils.py official repository unverified Apache-2.0 (permissive) · 6aa5b8bdcc4dfde8 · report
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Tasks

Language ModellingLarge Language ModelQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Visual Question Answering (VQA) A-OKVQA Prophet DA VQA Score 58.5 #3 of 15 Archive leaderboard report
Visual Question Answering (VQA) A-OKVQA Prophet MC Accuracy 75.1 #3 of 15 Archive leaderboard report
Visual Question Answering (VQA) OK-VQA Prophet Accuracy 62.5 #5 of 37 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

AdamAttentionAttention DropoutBPECosine AnnealingDense ConnectionsDropoutGPT-3Layer NormalizationLinear LayerLinear Warmup With Cosine AnnealingMulti-Head AttentionResidual ConnectionSoftmaxWeight Decay

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