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20242026
most citedCausalChaos! Dataset for Comprehensive Causal Action Question Answering Over Longer Causal Chains Grounded in Dynamic Visual Scenes

2 citations · 2 across the 4 of their papers we have counts for

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6 papers · 1 filter

cs.CL2026

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…

cs.CL2026

CoMMET: To What Extent Can LLMs Perform Theory of Mind Tasks?

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 ubiqu…

cs.CL2025

Are Large Language Models Effective Knowledge Graph Constructors?

Ruirui Chen, Weifeng Jiang, Chengwei Qin +4

Knowledge graphs (KGs) are vital for knowledge-intensive tasks and have shown promise in reducing hallucinations in large language models (LLMs). However, constructing high-quality…

cs.CL2025

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…

cs.CL2024

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…

cs.CL2024

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…