Dialogue Safety Prediction
2개 벤치마크 · 논문 3편 · 이 태스크의 논문 보기 →
Benchmarks
rt-inod-jailbreaking
결과 10개
ProsocialDialog
결과 2개
Most implemented
ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming
2024-04-06 · 구현 2개
Benchmarking Llama2, Mistral, Gemma and GPT for Factuality, Toxicity, Bias and Propensity for Hallucinations
2024-04-15 · 구현 1개
ProsocialDialog: A Prosocial Backbone for Conversational Agents
2022-05-25 · 구현 1개
Papers
Benchmarking Llama2, Mistral, Gemma and GPT for Factuality, Toxicity, Bias and Propensity for Hallucinations
2024-04-15
· David Nadeau, Mike Kroutikov, Karen McNeil, Simon Baribeau
This paper introduces fourteen novel datasets for the evaluation of Large Language Models' safety in the context of enterprise tasks. A method was devised to evaluate a model's safety, as determined by its ability to fol…
BenchmarkingBias DetectionDialogue Safety PredictionHallucinationALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming
2024-04-06
· Simone Tedeschi, Felix Friedrich, Patrick Schramowski, Kristian Kersting 외
When building Large Language Models (LLMs), it is paramount to bear safety in mind and protect them with guardrails. Indeed, LLMs should never generate content promoting or normalizing harmful, illegal, or unethical beha…
Adversarial RobustnessDialogue Safety PredictionRed TeamingProsocialDialog: A Prosocial Backbone for Conversational Agents
2022-05-25
· Hyunwoo Kim, Youngjae Yu, Liwei Jiang, Ximing Lu 외
Most existing dialogue systems fail to respond properly to potentially unsafe user utterances by either ignoring or passively agreeing with them. To address this issue, we introduce ProsocialDialog, the first large-scale…
Dialogue GenerationDialogue Safety PredictionResponse GenerationRules-of-thumb Generation