Despite the rapid advancement of Large Language Models (LLMs), uncertainty quantification in LLM generation is a persistent challenge.Although recent approaches have achieved strong performance by restricting LLMs to produce short or constrained answer sets, many real-world applications require long-form and free-form text generation.A key difficulty in this setting is that LLMs often produce responses that are semantically coherent yet factually inaccurate, while the underlying semantics are multifaceted and the linguistic structure is complex.To tackle this challenge, this paper introduces Interrogative Uncertainty Quantification (IUQ), a novel framework that leverages inter-sample consistency and intrasample faithfulness to quantify the uncertainty in long-form LLM outputs.By utilizing an interrogate-then-respond paradigm, our method provides reliable measures of claim-level uncertainty and the model's faithfulness.Experimental results across diverse model families and model sizes demonstrate the superior performance of IUQ over two widely used long-form generation datasets.The code is available at https://github.com/louisfanhz/IUQ.

Cross-modal retrieval, as an emerging field within multimedia research, has gained significant attention in recent years. Unsupervised cross-modal hashing methods are attractive due to their ability to capture latent relationships within the data without label supervision and to produce compact hash codes for high search efficiency. However, the text modality exhibits worse representation ability compared with the image modality, leading to weak guidance to construct the joint similarity matrix. Moreover, most unsupervised cross-modal hashing methods are based on pairwise similarities for training, resulting in non-aggregating data distribution in the hash space. In this paper, we propose a novel Vision-guided Text Mining for Unsupervised Cross-modal Hashing via Community Similarity Quantization, termed VTM-UCH. Specifically, we first find the one-to-one correspondence between each word and each vision (image or object) based on the Contrastive Language-Image Pre-training (CLIP) model and compute the text similarities according to the clustering of their corresponding visions. Then, we define the fine-grained object-level image similarities and design the joint similarity matrix based on the text and image similarities. Accordingly, we construct an undirected graph to compute the communities as the pseudo-centers and adjust the pairwise similarities to improve the hash codes distribution. The experimental results on two common datasets verify the accuracy improvements in comparison with state-of-the-art baselines.