Multimodal memes are dynamic and often require up-to-date background knowledge for interpretation. Existing methods often overlook such knowledge or rely on fixed parametric knowledge of pre-trained models that may be incomplete, outdated, or unavailable for emerging memes. We introduce Query-Retrieve-Conclude, a zero-shot framework that identifies missing knowledge, retrieves open-web evidence, and synthesizes evidence-grounded background knowledge for meme understanding and detection. We also introduce a curated meme understanding benchmark of recent memes from 2024–2026 with external background knowledge annotations. Experiments on three meme understanding datasets and five meme detection tasks show that our framework improves knowledge recovery, meme understanding and downstream detection over zero-shot baselines.

The DimABSA task requires fine-grained sentiment intensity prediction for restaurant reviews, including scores for Valence and Arousal dimensions for each Aspect Term. In this study, we propose a Coarse-to-Fine In-context Learning (CFICL) method based on the Baichuan2-7B model for the DimABSA task in the SIGHAN 2024 workshop. Our method improves prediction accuracy through a two-stage optimization process. In the first stage, we use fixed in-context examples and prompt templates to enhance the model’s sentiment recognition capability and provide initial predictions for the test data. In the second stage, we encode the Opinion field using BERT and select the most similar training data as new in-context examples based on similarity. These examples include the Opinion field and its scores, as well as related opinion words and their average scores. By filtering for sentiment polarity, we ensure that the examples are consistent with the test data. Our method significantly improves prediction accuracy and consistency by effectively utilizing training data and optimizing in-context examples, as validated by experimental results.