Multimodal LLMs are increasingly used with multi-image, interleaved image-text inputs, yet hallucination evaluation remains largely single-image. We study target-binding hallucination: a model answers as if an object appears in the queried target image when it appears only elsewhere. We introduce a position-controlled COCO haystack with POPE-style object-existence queries and split negatives into clean cases (object absent everywhere) and distractor-present cases (absent from the target but present elsewhere). On InternVL2-8B at N = 10, clean false positives remain near floor (1.15%), whereas distractor-present cases reach 10.55% and show a position-sensitive profile; the same regime-level separation appears across three additional architectures and in an object-balanced PASCAL VOC2012 check. Channel and paired-removal controls are consistent with a visual-channel-dominant source-misattribution interpretation, and a related MIHBench Existence re-audit finds that over 98% of false positives concentrate in partial-present negatives. For object-existence diagnostics and other source-sensitive multi-image evaluations with auditable negative evidence, we recommend reporting clean and evidence-supported negatives separately whenever non-target images may contain supporting evidence.

Summarizing data samples by quantitative measures has a long history, with descriptive statistics being a case in point. However, as natural language processing methods flourish, there are still insufficient characteristic metrics to describe a collection of texts in terms of the words, sentences, or paragraphs they comprise. In this work, we propose metrics of diversity, density, and homogeneity that quantitatively measure the dispersion, sparsity, and uniformity of a text collection. We conduct a series of simulations to verify that each metric holds desired properties and resonates with human intuitions. Experiments on real-world datasets demonstrate that the proposed characteristic metrics are highly correlated with text classification performance of a renowned model, BERT, which could inspire future applications.

Most models for learning word embeddings are trained based on the context information of words, more precisely first order co-occurrence relations. In this paper, a metric is designed to estimate second order co-occurrence relations based on context overlap. The estimated values are further used as the augmented data to enhance the learning of word embeddings by joint training with existing neural word embedding models. Experimental results show that better word vectors can be obtained for word similarity tasks and some downstream NLP tasks by the enhanced approach.