Abstract Multi-modal Large Language Models (MLLMs) tuned on machine-generated
instruction-following data have demonstrated remarkable performance in various multimodal …

This work focuses on the potential of Vision LLMs (VLLMs) in visual reasoning. Different
from prior studies, we shift our focus from evaluating standard performance to introducing a …

With the rising tide of large language models (LLMs), there has been a growing interest in
developing general-purpose instruction-following models, eg, ChatGPT. To this end, we …

Multi-modal large language models (MLLMs) are trained based on large language models
(LLM), with an enhanced capability to comprehend multi-modal inputs and generate textual …

Causality lays the foundation for the trajectory of our world. Causal inference (CI), which
aims to infer intrinsic causal relations among variables of interest, has emerged as a crucial …

The rapid development of large language models (LLMs) has been witnessed in recent
years. Based on the powerful LLMs, multi-modal LLMs (MLLMs) extend the modality from …

Counterfactual reasoning, as a crucial manifestation of human intelligence, refers to making
presuppositions based on established facts and extrapolating potential outcomes. Existing …

The advancement of large language models (LLMs) has significantly broadened the scope
of applications in natural language processing, with multi-modal LLMs extending these …

VQA (visual question and answer) is the task of enabling a computer to generate accurate
textual answers based on given images and related questions. It integrates computer vision …

This work focuses on benchmarking the capabilities of vision large language models
(VLLMs) in visual reasoning. Different from prior studies, we shift our focus from evaluating …