A Causal Explainable Guardrails for Large Language Models

arXiv:2405.04160v1 Announce Type: new
Abstract: Large Language Models (LLMs) have shown impressive performance in natural language tasks, but their outputs can exhibit undesirable attributes or biases. Existing methods for steering LLMs towards desired attributes often assume unbiased representations and rely solely on steering prompts. However, the representations learned from pre-training can introduce semantic biases that influence the steering process, leading to suboptimal results. We propose LLMGuardaril, a novel framework that incorporates causal analysis and adversarial learning to obtain unbiased steering …

abstract arxiv biases causal cs.cl guardrails however language language models large language large language models llms natural natural language performance pre-training prompts tasks training type unbiased