2 citations · 4 across the 10 of their papers we have counts for
10 papers
MR-Bench: A Unified Benchmark for Evidence-Grounded Multimodal Metaphor Understanding
Hong Jiang, Junnan Zhu, Jingwang Huang +9
Multimodal metaphor understanding requires identifying cross-domain Target-Source mappings and inferring sentiment from visual and textual evidence. However, existing benchmarks la…
ReTool-Video: Recursive Tool-Using Video Agents with Meta-Augmented Tool Grounding
Xiao Liu, Nayu Liu, Junnan Zhu +6
Video understanding requires active evidence seeking, motivating tool-augmented video agents for temporal reasoning, cross-modal understanding, and complex question answering. Exis…
CoMMET: A Psychologically Grounded Benchmark for Evaluating Theory of Mind in Multimodal LLMs
Ruirui Chen, Weifeng Jiang, Chengwei Qin +3
Theory of Mind (ToM)-the ability to reason about the mental states of oneself and others-is a cornerstone of human social intelligence. As Multimodal Large Language Models (MLLMs)…
Are Large Language Models Effective Knowledge Graph Constructors?
Ruirui Chen, Weifeng Jiang, Chengwei Qin +10
Knowledge graphs (KGs) are widely used in knowledge-intensive applications, yet it remains unclear how effectively current large language models (LLMs) can construct document-groun…
MELLA: Bridging Linguistic Capability and Cultural Groundedness for Low-Resource Language MLLMs
Yufei Gao, Jiaying Fei, Nuo Chen +4
Multimodal Large Language Models (MLLMs) perform strongly in high-resource languages, yet often produce fluent but culturally "thin" descriptions in low-resource settings. We argue…
From Grounding to Manipulation: Case Studies of Foundation Model Integration in Embodied Robotic Systems
Xiuchao Sui, Daiying Tian, Qi Sun +4
Foundation models (FMs) are increasingly used to bridge language and action in embodied agents, yet the operational characteristics of different FM integration strategies remain un…
Theory of Mind in Large Language Models: Assessment and Enhancement
Ruirui Chen, Weifeng Jiang, Chengwei Qin +1
Theory of Mind (ToM)-the ability to reason about the mental states of oneself and others-is a cornerstone of human social intelligence. As Large Language Models (LLMs) become incre…
LLM-Based Multi-Hop Question Answering with Knowledge Graph Integration in Evolving Environments
Ruirui Chen, Weifeng Jiang, Chengwei Qin +5
The important challenge of keeping knowledge in Large Language Models (LLMs) up-to-date has led to the development of various methods for incorporating new facts. However, existing…
Super Tiny Language Models
Dylan Hillier, Leon Guertler, Cheston Tan +3
The rapid advancement of large language models (LLMs) has led to significant improvements in natural language processing but also poses challenges due to their high computational a…
CausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes
Paritosh Parmar, Eric Peh, Ruirui Chen +4
Causal video question answering (QA) has garnered increasing interest, yet existing datasets often lack depth in causal reasoning. To address this gap, we capitalize on the unique…