3 citations · 3 across the 6 of their papers we have counts for
7 papers
Refusals That Bend: Measuring and Predicting Task Malleability in Embodied VLM Planners
Leo Y. Lin, Mikhail Kuznetsov, Muslum Ozgur Ozmen +1
Embodied vision-language models (VLMs) are increasingly deployed as high-level planners for robots because they generalize across diverse environments. However, this requires their…
RoboJailBench: Benchmarking Adversarial Attacks and Defenses in Embodied Robotic Agents
Doguhuan Yeke, Yanming Zhou, Leo Y. Lin +3
Recent advances in Vision-Language Models (VLMs) facilitate a new class of embodied AI systems, where these models are integrated into physical platforms, e.g. robots and autonomou…
JP-TL-Bench: Anchored Pairwise LLM Evaluation for Bidirectional Japanese-English Translation
Leonard Lin, Adam Lensenmayer
We introduce JP-TL-Bench, a lightweight, open benchmark designed to guide the iterative development of Japanese-English translation systems. In this context, the challenge is often…
WebNovelBench: Placing LLM Novelists on the Web Novel Distribution
Leon Lin, Jun Zheng, Haidong Wang
Robustly evaluating the long-form storytelling capabilities of Large Language Models (LLMs) remains a significant challenge, as existing benchmarks often lack the necessary scale,…
Self-Evaluation as a Defense Against Adversarial Attacks on LLMs
Hannah Brown, Leon Lin, Kenji Kawaguchi +1
We introduce a defense against adversarial attacks on LLMs utilizing self-evaluation. Our method requires no model fine-tuning, instead using pre-trained models to evaluate the inp…
Single Character Perturbations Break LLM Alignment
Leon Lin, Hannah Brown, Kenji Kawaguchi +1
When LLMs are deployed in sensitive, human-facing settings, it is crucial that they do not output unsafe, biased, or privacy-violating outputs. For this reason, models are both tra…
Rethinking How to Evaluate Language Model Jailbreak
Hongyu Cai, Arjun Arunasalam, Leo Y. Lin +2
Large language models (LLMs) have become increasingly integrated with various applications. To ensure that LLMs do not generate unsafe responses, they are aligned with safeguards t…