3 days Ago
Ambika Saklani Bhardwaj , Product Leader at Walmart Inc. Having spent the last two years building generative AI (GenAI) products for finance, I've noticed that AI teams often struggle to filter useful feedback from users to improve AI responses. Reinforcement learning (RL) plays an important role in training AI, as it can improve machines' ability to learn, but its success hinges on the quality of the feedback it receives.
One of the main concerns of RL, however, is how to identify which feedback is relevant and useful. When users interact with an AI-powered customer service chatbot, they provide feedback ranging from simple ratings to detailed textual reviews. This data holds immense potential for improving the chatbot's performance.
However, manually analyzing thousands of feedback entries is impractical. To do so would require a system that can automatically categorize and prioritize feedback based on its relevance to the RL agent's learning objectives. Auto-classification uses algorithms to automatically assign categories, labels or tags to feedback.
This streamlines processes, improves data organization and enables more efficient information retrieval. It's also being used in many GenAI tools already . Feedback on GenAI tools often has unique characteristics (e.
g., evaluating coherence, originality) that may not be well handled by ready-made auto-classification systems. Building auto-classification for GenAI feedback in-house requires a skilled team, but it also offers advantages like the specialized handling of unique feedback, model fine-tuning, workflow integration, data security and continuous improvement.
The auto-classification of feedback responses involves using machine learning and natural language processing (NLP) algorithms to automatically categorize and label user feedback. For example, based on AI performance impacts, the feedback can be categorized based on its: 1. Accuracy And correctness: Lacks depth, factually wrong, hallucinated data, misinterprets prompts 2.
Clarity And Explainability: Technically sound but unclear, overcomplicated, lacks reasoning or sources, loses context 3. User Experience: Slow responses, poor formatting, unnecessary repetition 4. Security And Compliance Issues: Biased responses, unauthorized data access Once feedback is classified based on these categories, only the most actionable ones can be prioritized for RL.
Feedback that includes subjective preferences (such as tone or minor phrasing issues), irrelevant issues (design concerns or non-AI logic concerns) or data quality problems (since this issue belongs to data governance) can be handled separately or by other departments. In my experience refining GenAI models, effectively leveraging user feedback is the cornerstone of successful reinforcement learning. Here's a detailed look at the challenges you will likely face with the process and how to overcome them: When automating feedback, the sheer variability and noise in user feedback—like slang, misspellings, etc.
—can make it difficult to categorize. Robust data preprocessing is key. This includes cleaning text, standardizing formats and utilizing pre-trained models that are then fine-tuned on specific feedback data.
Automation alone isn't sufficient. A human-in-the-loop system is crucial to review critical feedback, validate automated classifications and identify nuanced issues that AI might miss. This step can be resource-intensive, especially with large volumes of feedback.
When including a human in the loop, I generally prioritize reviews based on the model's uncertainty scores, focusing on feedback where the AI is less confident. Once feedback has been validated, you'll need to prioritize which issues need to be addressed first based on whether they are recurring issues or high-impact areas. Subjectivity in prioritization can lead to focusing on less impactful issues.
A data-driven scoring system—considering feedback frequency, impact and implementation ease—can enable efficient and objective prioritization. Finally, you'll need to use the filtered, high-quality feedback to fine-tune AI models. This involves training the models to adjust their responses based on the prioritized feedback, aiming for improved results and user satisfaction.
Ensuring that RL updates don't introduce unintended biases or regressions is a constant concern. To validate updates, conduct thorough testing, including A/B testing and user studies, and employ proximal policy optimization (PPO) to stabilize learning and prevent abrupt model changes. Auto-classification significantly scales your ability to process feedback, providing more accurate rewards and accelerating learning with RL.
It also reduces manual effort, allowing teams to focus on strategic improvements. Data labeling remains a significant effort, and handling ambiguous feedback requires a blend of AI and human judgment. Maintaining model accuracy as user behavior evolves requires continuous monitoring and updates.
User feedback quality itself is also a challenge and requires a system to improve it. By categorizing and filtering user input, you can better focus on driving AI improvement. This iterative process—blending automation with human review—ensures AI learns from high-quality data, leading to enhanced accuracy, clarity and user experience, ultimately fostering reliable and robust AI systems.
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