Papers by Tae-Eui Kam

2 papers
“Going to a trap house” conveys more fear than “Going to a mall”: Benchmarking Emotion Context Sensitivity for LLMs (2025.findings-emnlp)

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Challenge: a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans.
Approach: a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans.
Outcome: a new benchmark evaluates whether large language models can understand emotion context sensitivity of humans.
Connecting the Knowledge Dots: Retrieval-augmented Knowledge Connection for Commonsense Reasoning (2025.emnlp-main)

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Challenge: Recent studies show that large language models exhibit a limited understanding of commonsense reasoning due to the necessity of implicit knowledge that is rarely expressed in text.
Approach: They propose a retrieval-augmented knowledge connection framework that transforms indirectly relevant documents into a direct explanation to answer a given question.
Outcome: The proposed framework outperforms state-of-the-art (SOTA) benchmarks and achieves +2.0% and +4.6% average accuracy on in-domain (ID) and out-of domain (OOD) benchmark.

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