Back to Risk Catalogue

Human feedback loop

Human Feedback Loop

Implementing a human feedback loop is crucial for the effective deployment and continuous improvement of Generative AI solutions. Without appropriate feedback mechanisms, the solution may not perform optimally, and opportunities for enhancement over time could be missed. The following considerations should be kept in mind when designing a feedback loop:

  1. Alignment with Key Performance Indicators (KPIs)
  • Define a robust set of KPIs and evaluation metrics for the solution. For example, measure how many queries are answered correctly as annotated by subject matter experts (SMEs).
  • Feedback should include both quantitative and qualitative data to adequately assess if the solution is meeting established KPIs and/or objectives.
  1. Intended Use of Feedback Data Solution developers should clearly define and document how the feedback data will be utilized:
  • Prompt Fine-tuning: Analyze user feedback in relation to the LLM’s responses. Identify gaps between the solution’s purpose and the responses generated. Adjust prompts to bridge these gaps.
  • RAG Document Update: Examine low-rated responses to identify content improvement opportunities where the quality of the underlying data is the root cause.
  • Model/Data Drift Detection: Use feedback data to quantitatively detect model or data drift due to changes in the foundational model version or data quality over time.
  • Advanced Usages: Consider using feedback data for Reinforcement Learning from Human Feedback (RLHF) or fine-tuning the model itself to enhance performance.

Defining these goals ensures the long-term viability and continuous improvement of the solution.

  1. User Experience
  • Survey the target audience to gauge their willingness to provide feedback.
  • Ensure that the feedback mechanism does not hamper the effectiveness or usability of the solution.
  1. Wide vs. Narrow Feedback
  • In scenarios where user experience is paramount, and feedback might interfere with usability, consider creating a smaller group of tester SMEs.
  • These SMEs can collaborate closely with the development team to provide continuous, detailed feedback without impacting the broader user base.

Types of Feedback Mechanisms

As stated earlier, there are several ways one can collect feedback data. The choice you make will ultimately depend on the intention and use-case of feedback data usage. The two major categories of feedback are:

  1. Quantitative Feedback
  • Involves questions that can be answered with categorical responses or numerical ratings. For example, asking users to rate the chatbot’s response on a scale of 1-5 with defined parameters.
  • Quantitative data can be effectively used alongside other metrics to assess KPIs.
  1. Qualitative Feedback
  • Consists of open-ended questions where users provide free-form text feedback.
  • Allows users to offer insights not captured in quantitative metrics.
  • Natural Language Processing (NLP) or additional LLMs can be employed to analyze and derive insights from this feedback.
Reinforcement Learning from Human Feedback (RLHF)

It is important to briefly discuss RLHF given its importance and prevelance in the GenAI space, but first, you might be asking: What is RLHF?

Reinforcement Learning from Human Feedback (RLHF) is a machine learning technique that incorporates human evaluations to optimize models for more efficient self-learning. Traditional Reinforcement Learning (RL) trains software agents to make decisions that maximize rewards, improving accuracy over time. RLHF integrates human feedback into the reward function, enabling the model to perform tasks more aligned with human goals, preferences, and ethical considerations.

How RLHF Works

  1. State Space and Action Space
  • State Space: Represents all relevant information about the task at hand.
  • Action Space: Contains all possible decisions the AI agent can make.
  1. Reward Function
  • Human evaluators compare the model’s responses to desired outcomes, scoring them based on criteria like correctness, helpfulness, and alignment with human values.
  • The reward function incorporates these human evaluations, providing positive reinforcement for desirable outputs and penalties for undesirable ones.
  1. Policy Optimization
  • The model adjusts its policy (decision-making strategy) to maximize cumulative rewards based on human feedback.
  • Over time, the model learns to generate responses that are more accurate, contextually appropriate, and aligned with user expectations.

Implement RLHF in the Feedback Loop

  1. Data Collection
  • Gather human feedback systematically, ensuring a diverse and representative set of evaluations.
  • Both quantitative ratings and qualitative comments can be used to capture the nuances of user expectations.
  1. Model Training
  • Incorporate the collected feedback into the model’s training process using RL algorithms.
  • Regularly update the model to reflect new insights and changing user needs.
  1. Monitoring and Evaluation
  • Continuously assess the model’s performance against defined KPIs.
  • Monitor for unintended behaviors or biases, adjusting the training process as necessary.

Benefits of RLHF

RLHF helps mitigate risks associated with unaligned AI behavior, such as generating harmful or biased content. By incorporating human judgments, the model becomes better at avoiding inappropriate or unsafe outputs. Models trained with RLHF are more likely to produce outputs that are ethical, fair, and respectful of social norms. RLHF allows for ongoing refinement of the model as it interacts with users and receives more feedback, leading to sustained performance improvements.

Integration with LLM-as-a-Judge (CT-15)

Another aspect of Human Feeback which one should consider is related to the growing field of LLM-as-a-Judge (see: CT-15). When we use LLM-as-a-Judge, one should consider the same set of Quantitative and Qaulitative feedback from the LLM as they will from humans. This will provide them an opportunity to compare feedback between machine and humans. Also it will allow to rate the effectiveness of using LLM-as-a-Judge. One should also consider Narrow feedback on atleast a sample of the LLM-as-a-Judge results. The sample size (in terms of %) and the methodology is use-case dependent. For instance, if the use case is business-critical, then one needs to verify a higher number of samples

Wrap Up

A well-designed human feedback loop, enhanced with techniques like RLHF, is essential for the success of Generative AI solutions. By aligning the feedback mechanism with KPIs, clearly defining the intended use of feedback data, and ensuring a positive user experience, organizations can significantly improve the performance, safety, and reliability of their AI models. Incorporating RLHF, for instance, not only refines the model’s outputs but also ensures that the AI system remains aligned with human values and organizational objectives over time.