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Found 7 papers, 7 papers with code
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
AmpleGCG: Learning a Universal and Transferable Generative Model of Adversarial Suffixes for Jailbreaking Both Open and Closed LLMs
Moreover, we utilize those successful suffixes as training data to learn a generative model, named AmpleGCG, which captures the distribution of adversarial suffixes given a harmful query and enables the rapid generation of hundreds of suffixes for any harmful queries in seconds.
AttributionBench: How Hard is Automatic Attribution Evaluation?
Modern generative search engines enhance the reliability of large language model (LLM) responses by providing cited evidence.
A Trembling House of Cards? Mapping Adversarial Attacks against Language Agents
There is a surprisingly large gap between the speed and scale of their development and deployment and our understanding of their safety risks.
In Search of the Long-Tail: Systematic Generation of Long-Tail Inferential Knowledge via Logical Rule Guided Search
We further use the data generated by LINK to construct a dataset Logic-Induced-Long-Tail (LINT) that can be used to evaluate downstream models on the long-tail distribution; LINT contains 108K knowledge statements spanning four domains.
ChatCounselor: A Large Language Models for Mental Health Support
This paper presents ChatCounselor, a large language model (LLM) solution designed to provide mental health support.
RobustLR: Evaluating Robustness to Logical Perturbation in Deductive Reasoning
Transformers have been shown to be able to perform deductive reasoning on a logical rulebase containing rules and statements written in English natural language.
