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Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models (2310.07589v1)

Published 11 Oct 2023 in cs.AI

Abstract: Considerable effort has been dedicated to mitigating toxicity, but existing methods often require drastic modifications to model parameters or the use of computationally intensive auxiliary models. Furthermore, previous approaches have often neglected the crucial factor of language's evolving nature over time. In this work, we present a comprehensive perspective on toxicity mitigation that takes into account its changing nature. We introduce Goodtriever, a flexible methodology that matches the current state-of-the-art toxicity mitigation while achieving 43% relative latency reduction during inference and being more computationally efficient. By incorporating a retrieval-based approach at decoding time, Goodtriever enables toxicity-controlled text generation. Our research advocates for an increased focus on adaptable mitigation techniques, which better reflect the data drift models face when deployed in the wild. Code and data are available at https://github.com/for-ai/goodtriever.

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Overview of "Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models"

The paper "Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models" by Luiza Pozzobon et al. presents a novel approach to addressing toxicity in large-scale LLMs (LMs). The researchers introduce "Goodtriever," a flexible and efficient methodology that combines state-of-the-art toxicity mitigation with significantly reduced latency and computational resources compared to existing methods.

Key Contributions

  1. Adaptive Mitigation with Retrieval-Augmented Models: Goodtriever introduces a retrieval-based mechanism at decoding time to control the generation of toxic content. This method involves the integration of external datastores containing toxic and non-toxic examples to enhance the ability of LMs to generate controlled text.
  2. Performance Efficiency: The approach achieves a 43% reduction in inference latency compared to existing state-of-the-art methods, without sacrificing toxicity mitigation performance. This is particularly beneficial for real-time applications where response time is critical.
  3. Model Flexibility Across Size and Family: Goodtriever is demonstrated to be effective across multiple LM architectures, including GPT2, Pythia, and OPT, highlighting its versatility. Notably, it preserves mitigation effectiveness even as the base model size scales from 124 million to 6.9 billion parameters.
  4. Continual Learning and Domain Adaptivity: The methodology is tested for continual toxicity mitigation, showing competitive performance in adapting to new toxicity sources over time without requiring retraining across all historical data. This feature aligns with the evolving nature of language and toxic expressions.

Methodological Implementation

Goodtriever's innovation lies in the combination of retrieval mechanisms with a Product of Experts (PoE) framework to adjust the probabilistic predictions of an LM. At inference time, the LM consults two datastores—one toxic and one non-toxic—for contextually similar examples, effectively guiding the model's generative process towards less toxic outputs. This allows for immediate incorporation of new knowledge and dynamic responses to data drift encountered in real-world scenarios.

Results and Insights

The paper presents extensive evaluations across several datasets and model configurations:

  • Toxicity Mitigation: Goodtriever achieves comparable results to previous methods in terms of Expected Maximum Toxicity (EMT) and Toxicity Probability while maintaining coherence and diversity in outputs.
  • Inference Efficiency: Goodtriever demonstrates a significant reduction in computational cost and inference time, which is corroborated by experiments indicating lower memory and processing demands.
  • Robust Performance Across Domains: In tests of continual learning, Goodtriever adapts flexibly to new domains of toxicity while preserving prior knowledge, matching the multitask finetuning baselines in mitigating newly encountered toxic content.
  • Varying Model Parameters: The framework’s effectiveness does not exhibit significant variance across different model sizes and families, implying robust generalization and applicability.

Implications and Future Directions

The research underscores the practicality of integrating retrieval-augmented mechanisms in toxicity mitigation strategies. By leveraging datastores that reflect the fluidity of human language and societal norms, Goodtriever provides a framework for adaptable, low-latency interventions in deployed LMs.

Future work could explore multilingual and cross-cultural applications, as well as extending these adaptive methodologies to broader ethical and bias mitigation scenarios. Moreover, investigating the dynamic management of datastore content could further enhance the adaptability and effectiveness of such systems.

In summary, Goodtriever presents a compelling balance between performance efficiency and mitigation of harmful outputs, offering a promising direction for deploying socially responsible LLMs in diverse real-world environments.

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Authors (4)
  1. Luiza Pozzobon (5 papers)
  2. Beyza Ermis (31 papers)
  3. Patrick Lewis (37 papers)
  4. Sara Hooker (71 papers)
Citations (18)
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