2 citations · 5 across the 20 of their papers we have counts for
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Visualizing and Benchmarking LLM Factual Hallucination Tendencies via Internal State Analysis and Clustering
Nathan Mao, Varun Kaushik, Shreya Shivkumar +3
Large Language Models (LLMs) often hallucinate, generating nonsensical or false information that can be especially harmful in sensitive fields such as medicine or law. To study thi…
AutoAdv: Automated Adversarial Prompting for Multi-Turn Jailbreaking of Large Language Models
Aashray Reddy, Andrew Zagula, Nicholas Saban
Large Language Models (LLMs) remain vulnerable to jailbreaking attacks where adversarial prompts elicit harmful outputs. Yet most evaluations focus on single-turn interactions whil…
Emergent World Beliefs: Exploring Transformers in Stochastic Games
Adam Kamel, Tanish Rastogi, Michael Ma +2
Transformer-based large language models (LLMs) have demonstrated strong reasoning abilities across diverse fields, from solving programming challenges to competing in strategy-inte…
Don't Think of the White Bear: Ironic Negation in Transformer Models Under Cognitive Load
Logan Mann, Nayan Saxena, Sarah Tandon +3
Negation instructions such as 'do not mention ' can paradoxically increase the accessibility of in human thought, a phenomenon known as ironic rebound. Large language models…
UniToMBench: Integrating Perspective-Taking to Improve Theory of Mind in LLMs
Prameshwar Thiyagarajan, Vaishnavi Parimi, Shamant Sai +5
Theory of Mind (ToM), the ability to understand the mental states of oneself and others, remains a challenging area for large language models (LLMs), which often fail to predict hu…
NusaMT-7B: Machine Translation for Low-Resource Indonesian Languages with Large Language Models
William Tan, Kevin Zhu
Large Language Models (LLMs) have demonstrated exceptional promise in translation tasks for high-resource languages. However, their performance in low-resource languages is limited…