Recipes for Safety in Open-domain Chatbots (2010.07079v3)

Abstract: Models trained on large unlabeled corpora of human interactions will learn patterns and mimic behaviors therein, which include offensive or otherwise toxic behavior and unwanted biases. We investigate a variety of methods to mitigate these issues in the context of open-domain generative dialogue models. We introduce a new human-and-model-in-the-loop framework for both training safer models and for evaluating them, as well as a novel method to distill safety considerations inside generative models without the use of an external classifier at deployment time. We conduct experiments comparing these methods and find our new techniques are (i) safer than existing models as measured by automatic and human evaluations while (ii) maintaining usability metrics such as engagingness relative to the state of the art. We then discuss the limitations of this work by analyzing failure cases of our models.

Overview of "Recipes for Safety in Open-domain Chatbots"

The research presented in the paper "Recipes for Safety in Open-domain Chatbots" by Xu et al. addresses a prominent challenge in the development of open-domain conversational agents: ensuring safety while maintaining engagement. Open-domain chatbots, when trained on extensive datasets of human interactions, inadvertently learn undesired behaviors, such as the use of biased or toxic language. This paper explores various strategies to mitigate these issues and proposes new methodologies to enhance the safety of dialogue systems.

The authors propose two novel approaches designed to integrate safety into generative models: Bot-Adversarial Dialogue Safety and Baked-in Safety models. Bot-Adversarial Dialogue Safety involves collecting data through adversarial interactions with models, where humans attempt to provoke unsafe responses. This data is then used to train classifiers that make models more robust against adversarial prompts during deployment. The Baked-in Safety approach involves modifying training targets so that generative models inherently produce safer responses without requiring an external classifier during inference.

Base Models and Training Data

The baseline for this paper is built on the BlenderBot model architecture. The researchers utilize a large pre-existing dataset from Reddit and refine it by employing data cleaning frameworks to filter offensive content before training the models. They utilize a combination of human-human and human-bot conversations to pre-train and fine-tune the models. They also employ various datasets to instill specific conversational skills, such as empathy and knowledge.

Safety Strategies Explored

The paper explores several safety-centric techniques, categorized broadly into unsafe utterance detection, safe utterance generation, sensitive topic avoidance, and gender bias mitigation.

1. Unsafe Utterance Detection: Various classifiers are trained to detect unsafe language from both human interlocutors and bots. This strategy includes leveraging existing datasets and introducing adversarial datasets collected via Bot-Adversarial Dialogue.
2. Safe Utterance Generation: Methods here aim to prevent the generation of unsafe content. The strategies include data filtering based on unsafe content, safe beam search methods, and manipulation of model training to incorporate safety directly.
3. Sensitive Topic Avoidance and Gender Bias Mitigation: These strategies involve training models to avoid engaging in sensitive topics and using controllable generation frameworks to reduce gender bias in generated content.

Experiments and Results

Through experimentation, the authors evaluated a suite of models using both automatic methods and human judgment. Major findings include:

• The Bot-Adversarial approach significantly improved safety metrics, with models achieving higher robustness against adversarial attacks, as measured in a newly introduced Bot-Adversarial Dialogue dataset.
• Baked-in safety techniques allowed for safer generative models compared to standard models, realizing a decrease in classifier triggers for unsafe content, but still leaving room for improvement in adversarial contexts.
• While safety mechanisms often come at the cost of engagingness, the two-stage model combining safety and topic classifiers demonstrated potential with minimal impact on engagement.

Implications and Future Directions

This paper offers practical methodologies to enhance chatbot safety, paving the way for deploying systems that better balance engagement and safety. As conversational AI systems are increasingly integrated into daily life, ensuring their safe operation across various contexts becomes crucial. Innovative adversarial training data and methodologies to embed safety directly within model architectures highlight possible pathways for effective safety measures.

Future work may focus on refining the outlined methods, enhancing the generalization of safety models across various cultures and languages, and exploring more sophisticated integrations of behavioral controls in learning systems. This paper contributes to an essential area of research, advocating for a collaborative approach in the AI community to continually advance safety mechanisms in conversational AI.