activity
20162026
most citedIs ChatGPT better than Human Annotators? Potential and Limitations of ChatGPT in Explaining Implicit Hate Speech

213 citations · 383 across the 32 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.CL2024★ 3 cited

A semantic embedding space based on large language models for modelling human beliefs

Byunghwee Lee, Rachith Aiyappa, Yong-Yeol Ahn +2

Beliefs form the foundation of human cognition and decision-making, guiding our actions and social connections. A model encapsulating beliefs and their interrelationships is crucia…

cs.CL2024★ 1 cited

Rematch: Robust and Efficient Matching of Local Knowledge Graphs to Improve Structural and Semantic Similarity

Zoher Kachwala, Jisun An, Haewoon Kwak +1

Knowledge graphs play a pivotal role in various applications, such as question-answering and fact-checking. Abstract Meaning Representation (AMR) represents text as knowledge graph…

cs.CL2024

ChatGPT Rates Natural Language Explanation Quality Like Humans: But on Which Scales?

Fan Huang, Haewoon Kwak, Kunwoo Park +1

As AI becomes more integral in our lives, the need for transparency and responsibility grows. While natural language explanations (NLEs) are vital for clarifying the reasoning behi…

cs.CL2024★ 2 cited

Benchmarking zero-shot stance detection with FlanT5-XXL: Insights from training data, prompting, and decoding strategies into its near-SoTA performance

Rachith Aiyappa, Shruthi Senthilmani, Jisun An +2

We investigate the performance of LLM-based zero-shot stance detection on tweets. Using FlanT5-XXL, an instruction-tuned open-source LLM, with the SemEval 2016 Tasks 6A, 6B, and P-…

cs.CL2024

ToBlend: Token-Level Blending With an Ensemble of LLMs to Attack AI-Generated Text Detection

Fan Huang, Haewoon Kwak, Jisun An

The robustness of AI-content detection models against sophisticated adversarial strategies, such as paraphrasing or word switching, is a rising concern in natural language generati…