A well-known dilemma in large vision-language models ( e.g., GPT -4, LLaV A) is that while increasing the number of vision tokens generally enhances visual

To evaluate Large Language Models (LLMs) for question answering (QA), traditional methods typically focus on assessing single-turn responses to given questions. However, this approach doesn't capture the dynamic nature of human-AI interactions, where humans actively seek information through conversation.

Moreover, we analyze the relevance of existing metrics to dynamics metrics, improving them from the perspective of dynamics.

Elliptical pays attention to more relevant information.

The advent of large language models (LLMs) [Brown et al., 2020] has revolutionized the field of

We start by exploring strategies for eliciting explicit, coherent numerical predictive distributions from LLMs.

Subgraph-based methods have proven to be effective and interpretable in predicting drug-drug interactions (DDIs), which are essential for medical practice and drug development. Subgraph selection and encoding are critical stages in these methods, yet customizing these components remains underexplored due to the high cost of manual adjustments.

This paper introduces xRAG, a novel context compression method designed specifically for retrieval-augmented generation.

For example, in personalized medical diagnostics, a model is built based on historical data to facilitate an optimal treatment for a new patient (Bica et al., 2021).

This paper introduces DiffTORI, which utilizes Diff erentiable T rajectory O ptimization as the policy representation to generate actions for deep R einforcement and I mitation learning. Trajectory optimization is a powerful and widely used algorithm in control, parameterized by a cost and a dynamics function.