Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation

• URL: http://arxiv.org/abs/2410.14881v2
• Date: Tue, 17 Dec 2024 22:07:18 GMT
• Title: Class-RAG: Real-Time Content Moderation with Retrieval Augmented Generation
• Authors: Jianfa Chen, Emily Shen, Trupti Bavalatti, Xiaowen Lin, Yongkai Wang, Shuming Hu, Harihar Subramanyam, Ksheeraj Sai Vepuri, Ming Jiang, Ji Qi, Li Chen, Nan Jiang, Ankit Jain,
• Abstract summary: We propose a Classification approach employing Retrieval-Augmented Generation (Class-RAG) Compared to model fine-tuning, Class-RAG demonstrates flexibility and transparency in decision-making, outperforms on classification and is more robust against adversarial attack. Our findings also suggest that Class-RAG performance scales with retrieval library size, indicating that increasing the library size is a viable and low-cost approach to improve content moderation.
• Score: 15.298017013140385
• License:
• Abstract: Robust content moderation classifiers are essential for the safety of Generative AI systems. In this task, differences between safe and unsafe inputs are often extremely subtle, making it difficult for classifiers (and indeed, even humans) to properly distinguish violating vs. benign samples without context or explanation. Scaling risk discovery and mitigation through continuous model fine-tuning is also slow, challenging and costly, preventing developers from being able to respond quickly and effectively to emergent harms. We propose a Classification approach employing Retrieval-Augmented Generation (Class-RAG). Class-RAG extends the capability of its base LLM through access to a retrieval library which can be dynamically updated to enable semantic hotfixing for immediate, flexible risk mitigation. Compared to model fine-tuning, Class-RAG demonstrates flexibility and transparency in decision-making, outperforms on classification and is more robust against adversarial attack, as evidenced by empirical studies. Our findings also suggest that Class-RAG performance scales with retrieval library size, indicating that increasing the library size is a viable and low-cost approach to improve content moderation.

Related papers

• TrustRAG: Enhancing Robustness and Trustworthiness in RAG [31.231916859341865]
  TrustRAG is a framework that systematically filters compromised and irrelevant contents before they are retrieved for generation. TrustRAG delivers substantial improvements in retrieval accuracy, efficiency, and attack resistance compared to existing approaches.
  arXiv  Detail & Related papers  (2025-01-01T15:57:34Z)
• Enhancing AI Safety Through the Fusion of Low Rank Adapters [7.384556630042846]
  Low-Rank Adapter Fusion mitigates harmful responses when faced with malicious prompts. We show a 42% reduction in the harmfulness rate by leveraging LoRA fusion between a task adapter and a safety adapter. We also observe exaggerated safety behaviour, where the model rejects safe prompts that closely resemble unsafe ones.
  arXiv  Detail & Related papers  (2024-12-30T13:12:27Z)
• Unsupervised dense retrieval with conterfactual contrastive learning [16.679649921935482]
  We propose to improve the robustness of dense retrieval models by enhancing their sensitivity of fine-graned relevance signals. A model achieving sensitivity in this context should exhibit high variances when documents' key passages determining their relevance to queries have been modified. Motivated by causality and counterfactual analysis, we propose a series of counterfactual regularization methods.
  arXiv  Detail & Related papers  (2024-12-30T07:01:34Z)
• Towards More Robust Retrieval-Augmented Generation: Evaluating RAG Under Adversarial Poisoning Attacks [45.07581174558107]
  Retrieval-Augmented Generation (RAG) systems have emerged as a promising solution to mitigate hallucinations. RAG systems are vulnerable to adversarial poisoning attacks, where malicious passages injected into retrieval databases can mislead the model into generating factually incorrect outputs. This paper investigates both the retrieval and the generation components of RAG systems to understand how to enhance their robustness against such attacks.
  arXiv  Detail & Related papers  (2024-12-21T17:31:52Z)
• UncertaintyRAG: Span-Level Uncertainty Enhanced Long-Context Modeling for Retrieval-Augmented Generation [93.38604803625294]
  We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) We use Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks. UncertaintyRAG outperforms baselines by 2.03% on LLaMA-2-7B, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2024-10-03T17:39:38Z)
• ShieldGemma: Generative AI Content Moderation Based on Gemma [49.91147965876678]
  ShieldGemma is a suite of safety content moderation models built upon Gemma2. Models provide robust, state-of-the-art predictions of safety risks across key harm types.
  arXiv  Detail & Related papers  (2024-07-31T17:48:14Z)
• RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content [62.685566387625975]
  Current mitigation strategies, while effective, are not resilient under adversarial attacks. This paper introduces Resilient Guardrails for Large Language Models (RigorLLM), a novel framework designed to efficiently moderate harmful and unsafe inputs.
  arXiv  Detail & Related papers  (2024-03-19T07:25:02Z)
• Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity [80.16488817177182]
  GNNs are vulnerable to the model stealing attack, a nefarious endeavor geared towards duplicating the target model via query permissions. We introduce three model stealing attacks to adapt to different actual scenarios.
  arXiv  Detail & Related papers  (2023-12-18T05:42:31Z)
• Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations [29.32704733570445]
  We introduce Llama Guard, an input-output safeguard model geared towards Human-AI conversation use cases. Llama Guard incorporates a safety risk taxonomy, a valuable tool for categorizing a specific set of safety risks. Llama Guard demonstrates strong performance on existing benchmarks such as the OpenAI Moderation Evaluation dataset and ToxicChat.
  arXiv  Detail & Related papers  (2023-12-07T19:40:50Z)
• Self-Supervised Class Incremental Learning [51.62542103481908]
  Existing Class Incremental Learning (CIL) methods are based on a supervised classification framework sensitive to data labels. When updating them based on the new class data, they suffer from catastrophic forgetting: the model cannot discern old class data clearly from the new. In this paper, we explore the performance of Self-Supervised representation learning in Class Incremental Learning (SSCIL) for the first time.
  arXiv  Detail & Related papers  (2021-11-18T06:58:19Z)
• Policy Smoothing for Provably Robust Reinforcement Learning [109.90239627115336]
  We study the provable robustness of reinforcement learning against norm-bounded adversarial perturbations of the inputs. We generate certificates that guarantee that the total reward obtained by the smoothed policy will not fall below a certain threshold under a norm-bounded adversarial of perturbation the input.
  arXiv  Detail & Related papers  (2021-06-21T21:42:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.