Introduce a user-friendly feedback mechanism that allows users to “raise a flag” when encountering incorrect, unhelpful, or unexpected model responses. This feature would integrate with automated testing and validation pipelines to create a self-improving feedback loop, enhancing model performance and reducing recurring quirks or edge cases.
Suggested Implementation
When a user identifies an incorrect response, they can select part of the transcript to flag for feedback. A guided form prompts the user to describe:
Goal: What they were trying to achieve.
Expected Outcome: What response they expected.
Issues: Specific problems with the actual response.
Users can optionally include the entire transcript for context, but this is off by default to protect privacy. Once feedback is submitted:
Validation: The system attempts to reproduce the problem using the provided details.
First, using the flagged text alone.
If unsuccessful, it tests using the full transcript (if available).
If the behavior cannot be reproduced, users may be prompted for additional details.
Categorization: Feedback is sorted into:
Reproducible with specific text.
Reproducible with full transcript.
Not reproducible.
Successfully validated cases can be used to generate unit tests, ensuring that future model iterations don’t reintroduce the same issues.
Technical Considerations
Utilize embeddings to capture and cluster core issues for pattern detection and dataset growth.
Feedback forms could seed a high-quality dataset for negative prompts and automated validation.
Respect data privacy: Avoid default inclusion of transcripts and allow granular user control over shared data; allow the option to opt-in by default in user preferences
Benefit to Users
Provides a constructive outlet for users to handle incorrect responses, improving their experience by turning frustration into actionable feedback.
Developers and power users can actively contribute to the system’s evolution.
Benefit to the OpenAI Community
Accumulates a growing dataset of edge cases and edge-corrective prompts.
Enhances future model iterations by generating unit tests, reducing the reintroduction of quirks and improving stability.
Builds transparency and trust in the model refinement process.
Caveats / Unexplored Areas
Data Privacy: Ensuring user-submitted transcripts are anonymized and optional.
Training Considerations: How to balance using flagged responses for improvement without overfitting or reinforcing undesirable behaviors.
Adoption by Non-Developers: Simplifying the interface to encourage participation from users outside technical domains.
When user feedback is submitted, it goes through a series of specialized validation agents. Each agent has a narrow focus, allowing for targeted insights and refinement of the feedback data. The output of these agents is aggregated into actionable datasets, targeted fixes, and potentially new training data.
Layers of Validation
Each layer focuses on a specific aspect of the feedback, ensuring the data is high-quality, ethically sound, relevant, and not redundant. Here’s how the process might look:
1. Ethics & Policy Agent
Purpose: Identify feedback that violates ethical or policy guidelines.
Examples:
Request for harmful or illegal content was (correctly) not allowed.
Implicit bias in user expectations (e.g., problematic stereotypes).
Outcome:
Reject feedback due to policy violation.
Flag for human review
Exclude from potential training datasets.
Use for refining policy boundaries or improving moderation systems.
2. Bias Detection Agent
Purpose: Detect systemic biases or user assumptions that may highlight model shortcomings.
Examples:
Feedback highlighting stereotypes in responses.
Assumptions about language, gender, race, etc.
Outcome:
Used to augment fine-tuning datasets focused on fairness and inclusivity.
Inform system prompts to handle such cases better in the future.
3. Domain-Specific Agents
Purpose: Validate specific domains of feedback (math, coding, logic, art, etc).
Examples:
Math Agent: Checks for numerical or logical errors.
Code Agent: Validates that programming feedback aligns with coding best practices.
Art Agent: Ensures subjective feedback aligns with the user’s context.
Outcome:
Feedback categorized into specific datasets for retraining or fine-tuning.
Generate automated regression tests for these domains.
4. Utility Validation Agent
Purpose: Evaluate whether the feedback is actionable and helpful.
Examples:
Feedback like “This response is bad” without context is flagged as non-actionable.
Detailed feedback (“The response had a logical fallacy in line 3 because…”) is categorized as actionable.
Outcome:
Ensures low-quality or vague feedback doesn’t pollute datasets.
5. Final Aggregation
Feedback is categorized into:
Reproducible Issues: Feedback where the problem can be reliably reproduced.
Edge Cases: Rare issues needing manual review or further exploration.
Irrelevant or Unactionable Feedback: Dropped or archived.
Output and Use Cases
The processed data isn’t just dumped into one big bucket — rather, it’s routed to targeted areas:
Training Datasets:
High-quality, domain-specific feedback gets incorporated into targeted datasets.
System Improvements:
Insights feed back into prompt engineering, system prompts, or architecture updates.
Automated Regression Testing:
Unit tests generated from reproducible feedback ensure recurring issues don’t reappear in future builds.
Policy Refinement:
Feedback on bias, ethics, or systemic issues helps refine content moderation and fairness initiatives.
Benefits of Multi-Agent Approach
Precision: Each agent specializes in a single task, avoiding broad generalizations or overfitting.
Scalability: New agents can be added over time to tackle emerging issues.
Transparency: Clearly defined processes make the system auditable and trustworthy.
Challenges
Complexity: Building and coordinating multiple agents requires significant engineering effort.
Performance: Processing large volumes of feedback might introduce latency.
False Positives/Negatives: Agents may need fine-tuning to avoid incorrectly classifying feedback.
Why This Matters
This layered approach ensures that each piece of feedback gets maximum utility while maintaining ethical and technical rigor. It also respects the user’s effort in providing thoughtful feedback, creating a feedback loop that benefits everyone. And most importantly, this allows us to gain many benefits without needing a human at the keyboard to churn through all the data.
I presented this outline to GPT-4o and asked for it’s thoughts:
This idea is more than just sunshine and rainbows—it’s a scalable and logical path forward for AI systems to truly learn from their users. I hope OpenAI gives it the attention it deserves.
