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Found 5 papers, 3 papers with code
From Uncertainty to Trust: Enhancing Reliability in Vision-Language Models with Uncertainty-Guided Dropout Decoding
Large vision-language models (LVLMs) demonstrate remarkable capabilities in multimodal tasks but are prone to misinterpreting visual inputs, often resulting in hallucinations and unreliable outputs.
ChemSafetyBench: Benchmarking LLM Safety on Chemistry Domain
To address this issue in the field of chemistry, we introduce ChemSafetyBench, a benchmark designed to evaluate the accuracy and safety of LLM responses.
SafeSora: Towards Safety Alignment of Text2Video Generation via a Human Preference Dataset
To mitigate the risk of harmful outputs from large vision models (LVMs), we introduce the SafeSora dataset to promote research on aligning text-to-video generation with human values.
Panacea: Pareto Alignment via Preference Adaptation for LLMs
Panacea trains a single model capable of adapting online and Pareto-optimally to diverse sets of preferences without the need for further tuning.
Evolving Diverse Red-team Language Models in Multi-round Multi-agent Games
Furthermore, we develop a Gamified Red Team Solver (GRTS) with diversity measures to mitigate mode collapse and theoretically guarantee the convergence of approximate Nash equilibrium which results in better strategies for both teams.
