SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use | Paper Citation Record

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use write newline

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Unresolved cited work

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use MVTamperBench: Evaluating Robustness of Vision-Language Models

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Unresolved cited work

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Unresolved cited work

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Unresolved cited work

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Enhancing Document AI Data Generation Through Graph-Based Synthetic Layouts

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Do, Yan Xu, and Pascale Fung

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Language Models are Few-Shot Learners

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Unresolved cited work

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use The Llama 3 Herd of Models

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Multilingual Large Language Models and Curse of Multilinguality

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Mistral 7B

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use A Survey on Large Language Models for Code Generation

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language Models

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Controllable Text Generation for Large Language Models: A Survey

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Corporate Communication Companion (CCC): An LLM-empowered Writing Assistant for Workplace Social Media

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use LLM for Barcodes: Generating Diverse Synthetic Data for Identity Documents

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Review of reference generation methods in large language models

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Tokenization Matters: Improving Zero-Shot NER for Indic Languages

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Clinical QA 2.0: Multi-Task Learning for Answer Extraction and Categorization

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Survey of Large Multimodal Model Datasets, Application Categories and Taxonomy

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Text Classification via Large Language Models

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use All Languages Matter: On the Multilingual Safety of Large Language Models

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal

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SweEval: Do LLMs Really Swear? A Safety Benchmark for Testing Limits for Enterprise Use Universal and Transferable Adversarial Attacks on Aligned Language Models

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Coverage vector

Reference resolution

Outbound references

Pith citing papers