#AI

This paper proposes a new benchmark, 'Document Haystack,' which measures the ability to find specific information from long documents up to 200 pages long. This benchmark evaluates how accurately a Vision Language Model (VLM) can find intentionally embedded text or image information ('needles') within a document. The experimental results reveal that while current VLMs perform well on text-only documents, their performance significantly degrades on imaged documents or when handling information that combines text and images. This highlights future research challenges in the long-context and multimodal document understanding capabilities of VLMs.

This paper proposes a hybrid Top-k recommendation system that combines traditional recommendation methods with large language models (LLMs). Users are categorized as "active users" and "weak users," with LLMs employed to improve recommendation accuracy and fairness for the latter group. At the same time, the model controls LLM computational costs to ensure practical feasibility.