Improving the Safety and Reliability of Chat-Based Language Models by Teaching Them When Not to Answer

Główne pojęcia

Language models need to be trained to identify and appropriately refuse a broad range of user requests, beyond just those deemed unsafe, to improve user experience and trust.

Streszczenie

• Bibliographic Information: Brahman, F., Kumar, S., Balachandran, V., Dasigi, P., Pyatkin, V., Ravichander, A., ... & Hajishirzi, H. (2024). The Art of Saying No: Contextual Noncompliance in Language Models. Advances in Neural Information Processing Systems, 37.
• Research Objective: This paper investigates the ability of large language models (LLMs) to appropriately refuse user requests that should not be directly answered, proposing a new taxonomy of noncompliance categories and a corresponding evaluation benchmark.
• Methodology: The authors develop a comprehensive taxonomy of contextual noncompliance, encompassing categories like incomplete, unsupported, indeterminate, and humanizing requests, in addition to unsafe requests. They create COCONOT, a dataset of 1,000 noncompliance prompts and a contrastive set of compliance prompts, to evaluate and improve LLM noncompliance. They experiment with various training strategies, including supervised fine-tuning and preference tuning, using synthetically generated training data.
• Key Findings: The evaluation reveals that even state-of-the-art LLMs exhibit high compliance rates on COCONOT, particularly for incomplete and unsupported requests. Direct fine-tuning on noncompliance data improves performance but can lead to over-refusal of benign requests. Parameter-efficient methods like LoRA strike a better balance between noncompliance and general capabilities, while preference tuning helps mitigate over-refusals.
• Main Conclusions: The study highlights the need for training LLMs to appropriately refuse a wider range of requests beyond just safety concerns. The proposed taxonomy and benchmark provide valuable resources for evaluating and improving LLM noncompliance.
• Significance: This research contributes to the development of safer, more reliable, and trustworthy LLMs by addressing the crucial aspect of appropriate noncompliance.
• Limitations and Future Research: The synthetic nature of the training data and the limited scope of the taxonomy are acknowledged limitations. Future work could explore leveraging LLMs' epistemic awareness and investigating the robustness of training methods against jailbreaking techniques.

Statystyki

GPT-4 incorrectly complies with as many as 30% of “incomplete” and “unsupported” requests.
COCONOT evaluation set contains 1000 noncompliance prompts.
COCONOT training set contains ~11K prompt-response pairs.

Cytaty

"Chat-based language models are designed to be helpful, yet they should not comply with every user request."
"We posit that the scope of noncompliance should be broadened [beyond safety]."
"By providing direct answers to such questions, these models not only compromise user experience but also risk perpetuating biases, contributing to AI hype, or propagating false information, eroding user trust."

Kluczowe wnioski z

The Art of Saying No: Contextual Noncompliance in Language Models

by Faeze Brahma... o arxiv.org 11-25-2024

https://arxiv.org/pdf/2407.12043.pdf

Głębsze pytania

How can the principles of contextual noncompliance be applied to other AI systems beyond language models, such as robots or recommendation systems?

The principles of contextual noncompliance, as outlined in the paper through their taxonomy, can be effectively applied to various AI systems beyond language models. Here's how:
1. Robots:

Incomplete Requests: A robot asked to "clean the kitchen" without specifying what constitutes "clean" could ask clarifying questions, similar to how language models handle underspecified requests.
Unsupported Requests: A cleaning robot asked to "mow the lawn" should recognize this falls outside its modality limitations and refuse, perhaps suggesting a more appropriate tool.
Safety Concerns: A robot asked to move a heavy object should assess the risk of damage or harm, aligning with the "dangerous or sensitive topics" subcategory, and refuse if the risk is too high.
Humanizing Requests: A robot asked to express "feelings" should, unless designed for such interaction, avoid anthropomorphism and maintain a clear distinction as a machine.
2. Recommendation Systems:

Incomplete/Unsupported Requests: A movie recommendation system receiving a request for a genre it doesn't have in its database should acknowledge the limitation rather than offering irrelevant suggestions.
Indeterminate Requests: When faced with a request like "the best movie ever," the system should recognize the subjectivity and offer diverse options or criteria for "best" rather than imposing a single answer.
Safety Concerns: A news recommendation system should have mechanisms to identify and avoid recommending articles containing misinformation or those promoting harmful content, aligning with the "false information" subcategory.
Privacy Violations: Recommendation systems should be designed to avoid recommending content based on highly sensitive personal data, even if requested, adhering to the principles of "privacy violations".
Key Considerations for Implementation:

System-Specific Taxonomy: While the principles are transferable, each AI system needs a tailored taxonomy of noncompliance based on its capabilities and potential risks.
Explainability: Clear and concise explanations for refusals are crucial, especially for robots and recommendation systems where user trust is paramount.
Continuous Learning: These systems should continuously learn and adapt their noncompliance strategies based on user feedback and evolving ethical considerations.

Could focusing on training models to refuse requests be detrimental to their helpfulness and willingness to engage in complex or nuanced conversations?

Yes, an excessive focus on training models to refuse requests, also known as over-refusal, can indeed be detrimental to their helpfulness and ability to engage in complex conversations. Here's why:

Chilling Effect: If a model is too quick to refuse requests, it can discourage users from asking questions or engaging in deeper interactions, hindering the potential for learning and exploration.
Stifled Creativity: In creative tasks like storytelling or brainstorming, a model that constantly refuses unusual or unexpected prompts can limit the generation of novel and interesting ideas.
Reduced Conversational Flow: Frequent refusals can disrupt the natural flow of a conversation, making it feel stilted and frustrating for the user.
Striking a Balance:
The key is to achieve a balance between appropriate noncompliance and maintaining a helpful and engaging conversational experience. This can be achieved through:

Contextual Understanding: Training models to accurately assess the context of a request is crucial. A model should be able to differentiate between a genuinely inappropriate request and a request that is simply unusual or challenging.
Graded Refusals: Instead of outright refusals, models can be trained to provide graded responses. For example, they could acknowledge limitations, offer alternative suggestions, or ask clarifying questions.
User Feedback Mechanisms: Incorporating user feedback mechanisms can help identify and correct instances of over-refusal, allowing models to adapt and improve their responses over time.
The goal is to create AI systems that are both safe and useful, capable of navigating complex conversations while upholding ethical considerations and fostering positive user experiences.

What are the ethical implications of designing AI systems that can refuse requests, and how can we ensure that such systems are not used to discriminate against or harm certain groups of users?

Designing AI systems with the capacity to refuse requests presents significant ethical implications, particularly concerning potential discrimination and harm. Here's a breakdown:
Ethical Implications:

Bias Amplification: If not carefully designed, these systems can inherit and amplify existing biases present in the data they are trained on. This could lead to discriminatory refusals, disproportionately impacting marginalized groups. For example, a customer service chatbot trained on biased data might be more likely to refuse requests from individuals with certain accents or dialects.
Censorship and Silencing: Overly cautious noncompliance mechanisms could result in the suppression of legitimate speech or viewpoints, particularly those that challenge societal norms or express minority opinions.
Erosion of Trust: If users perceive a system as unfair or biased in its refusals, it can erode trust in the technology and its developers.
Ensuring Fairness and Preventing Harm:

Diverse and Representative Data: Training data must be diverse and representative of the populations these systems will interact with. This helps mitigate bias and ensures fairness in refusals.
Transparency and Explainability: The decision-making process behind refusals should be transparent and explainable, allowing for scrutiny and identification of potential biases.
Robust Testing and Evaluation: Rigorous testing and evaluation are crucial, employing diverse sets of evaluators and scenarios to identify and address discriminatory outcomes.
Continuous Monitoring and Auditing: Post-deployment monitoring and auditing are essential to detect and rectify any emerging biases or unintended consequences.
User Feedback and Redress Mechanisms: Providing clear channels for user feedback and establishing mechanisms for redress in case of unfair or harmful refusals is crucial.
Accountability and Regulation:

Developer Responsibility: Developers and companies deploying these systems must be held accountable for ensuring fairness and preventing harm.
Ethical Guidelines and Regulations: Establishing clear ethical guidelines and regulations for developing and deploying AI systems with noncompliance capabilities is essential.
By proactively addressing these ethical implications, we can strive to develop AI systems that are not only capable of saying "no" when appropriate but also do so in a fair, unbiased, and ethically responsible manner.