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Input validation, output filtering, and content moderation classifiers.
Also in Non-Model
Bias mitigation efforts can extend into the inference stage, where specific techniques are used to adjust LLM outputs dynamically. Modifying decoding strategies, such as adjusting the probabilities for next-token or sequence generation, can help reduce bias in real-time without further training (Lauscher et al., 2021). Additionally, adjusting attention weights during inference allows for the modulation of the model’s focus, promoting fairness in generated outputs (Park et al., 2023b). Debiasing components, like AdapterFusion (Gira et al., 2022), can dynamically address biases as they arise during the generation process of LLMs. Techniques such as gradient-based decoding enable the neutral rewriting of biased or harmful text, ensuring that final outputs align with fairness goals (Joniak & Aizawa, 2022b). Moreover, online monitoring of model outputs is necessary to detect emerging biases, allowing for timely interventions and adjustments to maintain model integrity.
Reasoning
Post-training technique removes or modifies learned social bias capabilities in deployed models.
Mitigating social bias
Value Misalignment
99.9 OtherValue Misalignment > Mitigating social bias
1 AI SystemValue Misalignment > Privacy protection
1 AI SystemValue Misalignment > Methods for mitigating toxicity
1 AI SystemValue Misalignment > Methods for mitigating LLM amorality
1 AI SystemRobustness to attack
1 AI SystemLarge Language Model Safety: A Holistic Survey
Shi, Dan; Shen, Tianhao; Huang, Yufei; Li, Zhigen; Leng, Yongqi; Jin, Renren; Liu, Chuang; Wu, Xinwei; Guo, Zishan; Yu, Linhao; Shi, Ling; Jiang, Bojian; Xiong, Deyi (2024)
The rapid development and deployment of large language models (LLMs) have introduced a new frontier in artificial intelligence, marked by unprecedented capabilities in natural language understanding and generation. However, the increasing integration of these models into critical applications raises substantial safety concerns, necessitating a thorough examination of their potential risks and associated mitigation strategies. This survey provides a comprehensive overview of the current landscape of LLM safety, covering four major categories: value misalignment, robustness to adversarial attacks, misuse, and autonomous AI risks. In addition to the comprehensive review of the mitigation methodologies and evaluation resources on these four aspects, we further explore four topics related to LLM safety: the safety implications of LLM agents, the role of interpretability in enhancing LLM safety, the technology roadmaps proposed and abided by a list of AI companies and institutes for LLM safety, and AI governance aimed at LLM safety with discussions on international cooperation, policy proposals, and prospective regulatory directions. Our findings underscore the necessity for a proactive, multifaceted approach to LLM safety, emphasizing the integration of technical solutions, ethical considerations, and robust governance frameworks. This survey is intended to serve as a foundational resource for academy researchers, industry practitioners, and policymakers, offering insights into the challenges and opportunities associated with the safe integration of LLMs into society. Ultimately, it seeks to contribute to the safe and beneficial development of LLMs, aligning with the overarching goal of harnessing AI for societal advancement and well-being. A curated list of related papers has been publicly available at https://github.com/tjunlp-lab/Awesome-LLM-Safety-Papers.
Operate and Monitor
Running, maintaining, and monitoring the AI system post-deployment
Deployer
Entity that integrates and deploys the AI system for end users
Manage
Prioritising, responding to, and mitigating AI risks
