1 citations · 1 across the 4 of their papers we have counts for
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MR-Bench: A Unified Benchmark for Evidence-Grounded Multimodal Metaphor Understanding
Hong Jiang, Junnan Zhu, Jingwang Huang +9
Metaphor enables the understanding of abstract concepts through cross-domain mappings while conveying affective attitudes. In multimodal scenarios, visual and textual information j…
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…
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…