Keeping AI in Check: Human Guardrails for LLM Workflows

LLMs like GPT, BERT, and others are capable of generating human-like text with remarkable fluency and coherence. They can be trained on vast amounts of data to perform a wide range of language tasks, from simple question answering to complex story generation.

However, as powerful as LLMs are, they are not perfect. They can sometimes generate outputs that are factually incorrect, logically inconsistent, biased, or misaligned with user intentions. This is where human feedback and oversight come in.

By incorporating human input and guidance into LLM-powered workflows, we can significantly improve the quality, accuracy, and trustworthiness of AI-generated content. Human-in-the-loop learning, prompt engineering, output filtering, and continuous monitoring are all strategies that can help bridge the gap between the capabilities of LLMs and the needs of real-world applications.

In this post, we'll explore these techniques in depth and provide practical tips for implementing them in your own projects. We'll also discuss some of the key challenges and considerations around human-AI collaboration, such as managing costs, ensuring consistency, and addressing ethical concerns. By the end, you'll have a comprehensive framework for leveraging the power of LLMs while keeping humans in the loop.

Start with Well-Defined Outputs and Metrics

The first step to incorporating effective human oversight is to have a clear definition of what you want the LLM to achieve, and how you will measure success. What are the key outputs you need the model to generate? What does good look like for each output type? What are the most important metrics that indicate the model is performing well?

For example, let's say you are using an LLM to automatically generate product descriptions for an e-commerce catalog. The key output is the product description text. Some success metrics could include:

• Factual accuracy: The descriptions should be factually correct based on the structured product data (title, specs, features, etc.)
• Comprehensiveness: The descriptions should mention the most relevant and important product attributes and selling points
• Brand alignment: The descriptions should align with your company's brand voice and tone guidelines
• Uniqueness: The descriptions should be original and not too similar to competitors or other products on your site

Having these clear metrics gives you a framework for evaluating the model's performance and knowing where human intervention is needed. You can spot-check a sample of outputs against these criteria and provide targeted feedback to improve the model.

Implement Human-in-the-Loop Learning

One of the most effective ways to inject human oversight into LLM workflows is through human-in-the-loop learning (HITL). This is an iterative process where humans provide feedback on model outputs, which is then used to fine-tune the model's performance over time.

Here's a basic process for implementing HITL with an LLM:

1. Define your task, outputs and success metrics as described above
2. Collect an initial set of training examples consisting of inputs and ideal outputs
3. Fine-tune the base LLM on these examples to create a task-specific model
4. Generate outputs from the model for a subset of inputs
5. Have human raters evaluate the outputs and provide feedback/scores based on the success metrics
6. Convert the human feedback into additional training examples
7. Fine-tune the model again on the expanded training set
8. Repeat steps 4-7 over multiple rounds to continuously improve the model

Let's make this concrete with our product description example. Say you have 100,000 products that need descriptions. You could start by writing ideal descriptions for 500 of them to create your initial training set.

After fine-tuning the model, you generate descriptions for another 500 products. You then have a team of human raters score each description on a 1-5 scale for accuracy, comprehensiveness, brand alignment, and uniqueness. Scores of 4-5 mean the description is good enough to use as-is. Scores of 1-3 mean the description needs modifications, which the human raters make manually.

You then add the human-scored and human-edited examples back to the training set, and fine-tune the model again. Repeat this process until you achieve your desired level of quality. By the end, you'll have a model that has "learned" to generate product descriptions that meet your specific criteria with minimal human intervention needed.

Here's an example of what the HITL flow might look like in code:

The key points are:

1. We start with an initial set of training examples
2. We fine-tune the model, generate outputs for evaluation, and collect human feedback
3. We expand the training set with the human-scored and human-edited examples
4. We repeat this process over multiple rounds to arrive at a high-quality fine-tuned model

Use Prompt Engineering to Guide the Model

Another way to incorporate human guidance into LLM workflows is through prompt engineering. This is the practice of carefully designing the text prompts you feed into the model to get the desired outputs. By crafting prompts that give the model detailed instructions, examples, and guardrails, you can coax it to generate better aligned and more consistent results.

Some prompt engineering techniques include:

• Few-shot learning: Include examples of the desired input-output mapping directly in the prompt to "teach" the model what to do
• Role specification: Specify the role or persona the model should embody in the prompt, such as "You are a helpful assistant" or "You are an experienced Python programmer"
• Step-by-step instructions: Break down a complex task into a series of explicit steps in the prompt for the model to follow
• Do's and don'ts: List out things the model should and should not do in the prompt, such as "Do not mention competitors" or "Use a friendly tone"

Here's an example prompt for our product description generator:

Feeding this prompt to the model gives it much more context and guidance than simply saying "write a product description for wireless earbuds". The role specification primes it to write in the appropriate format, the key features act as a content outline, and the do's and don'ts set guardrails for tone and style.

You can even include an example product description directly in the prompt to make it crystal clear what you're looking for:

By including an example, you give the model a template to follow and minimize the guesswork needed to generate a high-quality output. This kind of prompt engineering can produce solid results with little to no human post-editing required.

Filter and Fact-Check Model Outputs

Even with HITL learning and prompt engineering, LLMs can still sometimes produce outputs that are incorrect, inconsistent, biased, or misaligned. Therefore, it's important to have safeguards in place to catch and filter problematic outputs before they reach end users.

Some common output filtering techniques include:

• Keyword blocking: Check for the presence of specific words or phrases that are not allowed, like profanity, hate speech, competitor brands, etc.
• Sentiment analysis: Automatically score the sentiment of each output and filter out those that are overly negative, hostile, or controversial
• Fact checking: For outputs that make factual claims, cross-reference them against trusted data sources to verify accuracy
• Plagiarism detection: Compare outputs against existing content to ensure they are sufficiently unique
• Semantic similarity: Compute the semantic similarity of outputs to the inputs and filter out irrelevant or off-topic responses

For our product description example, we would want to check each generated description against the source product data to make sure it only mentions accurate specs and features. We may also want to compare the descriptions to existing descriptions on our site and competitor sites to ensure they are not too similar.

We can automate a lot of this filtering using additional ML models and heuristics. For example, we can fine-tune a separate classification model to detect off-brand or inappropriate content in the generated descriptions. We can also use named entity recognition and keyword matching to verify that key product attributes are present.

Here's a sketch of what the automated filtering step might look like:

The specifics of each filtering function will depend on your particular use case and requirements. The key point is to have multiple layers of automated checking to validate the model's outputs and catch potential errors.

For the small percentage of outputs that make it through the automated filters but still have issues, you can fall back to manual human review. This is where your human raters come in again to do a final quality check and edit any remaining mistakes.

Continuously Monitor and Improve

Incorporating human feedback into LLM workflows is not a one-time process, but an ongoing effort. Even after you've done multiple rounds of HITL fine-tuning, there is always room for further improvement.

It's important to continuously monitor the model's outputs and collect feedback from end users, customer support, and other stakeholders who are consuming the generated content. Look for patterns in the feedback to identify areas where the model consistently falls short.

For example, maybe you find that the product descriptions are always missing a key feature or using the wrong tone for a particular product category. You can target these weaknesses by adding more training examples in those areas and updating the prompt instructions.

You should also keep an eye out for potential "drift" in the model's performance over time, especially as new products and data are added. If you notice a gradual degradation in quality, it may be time for another round of HITL fine-tuning to get the model back on track.

It can be helpful to have a dashboard that tracks the model's key performance metrics over time, such as:

• Percentage of outputs that pass automated filters
• Average human rating scores across different dimensions
• Percentage of outputs that require manual edits
• Number of customer complaints or support tickets related to the generated content
• Engagement metrics like click-through rates and conversion rates

By monitoring these metrics, you can spot trends and proactively address issues before they become major problems.

Conclusion

LLMs are extremely powerful tools for generating human-like text at scale, but they are not perfect. To get the most value from LLMs in a production setting, it's crucial to incorporate human feedback and oversight into the workflow.

By combining techniques like human-in-the-loop learning, prompt engineering, output filtering, and continuous monitoring, you can significantly improve the quality and reliability of your LLM-generated content. This hybrid approach allows you to leverage the speed and scale of LLMs while still maintaining the necessary level of human control and judgment.

The key is to have a clear definition of success, a systematic process for collecting and incorporating human feedback, and a suite of automated safeguards to catch potential issues. By putting these pieces in place, you can unlock the full potential of LLMs to drive efficiency and innovation in your business.

1. What is human feedback in the context of large language models (LLMs)?

Human feedback refers to the process of incorporating human evaluation, judgment, and preferences into the training and deployment of LLMs. This can include humans providing ratings, edits, or selections of model outputs to improve their quality and alignment with desired goals.

2. Why is human feedback important for LLM-powered workflows?

Human feedback is important because LLMs, while powerful, are not perfect and can generate outputs that are incorrect, biased, inconsistent, or misaligned with user needs. Human oversight helps catch and correct these issues, ensuring that the models are producing high-quality and trustworthy results.

3. What are some key strategies for incorporating human feedback into LLM workflows?

The implementation of human-in-the-loop learning, where humans provide targeted feedback to iteratively enhance model performance, prompt engineering to provide guardrails and instructions to guide model behavior. Filtering and fact-checking model outputs to identify potential errors or inconsistencies, continuous monitoring of model performance and proactive identification of areas for improvement are among the key strategies.

4. How do you measure the quality of LLM outputs for a specific use case?

The quality of LLM outputs should be measured against well-defined metrics that reflect the specific goals and requirements of the use case. These could include dimensions such as accuracy, coherence, relevance, style, tone, or business impact. Metrics can be quantitative (e.g., numeric scores) or qualitative (e.g., human judgments), and should be tracked over time to identify trends and improvements.

5. What is human-in-the-loop learning and how does it work?

Human-in-the-loop learning is an approach where humans provide iterative feedback on model outputs, which is then used to fine-tune the model's performance. The basic steps are: 1) Train an initial model on a seed dataset, 2) Generate outputs from the model and have humans evaluate their quality, 3) Convert the human feedback into additional training examples, 4) Fine-tune the model on the expanded dataset. 5) Repeat steps 2-4 until the desired level of performance is achieved.

6. What are some best practices for prompt engineering with LLMs?

Some best practices for prompt engineering include providing clear instructions and constraints for the desired output, including positive and negative examples to illustrate the desired behavior. Using techniques like zero-shot, few-shot, and chain-of-thought prompting to provide additional context, experimenting with different prompts and variations to optimize performance, documenting and sharing effective prompts across the organization.

7. How can you automatically filter or fact-check LLM outputs at scale?

There are several techniques for automatically filtering or fact-checking LLM outputs at scale, such as keyword or pattern-based filtering to find known issues or inconsistencies. Using additional classification models to find outputs that are irrelevant, toxic, biased, etc., and comparing outputs against each other.

8. What are some common challenges or pitfalls to watch out for when incorporating human feedback into LLM workflows?

Some common challenges and pitfalls include balancing the cost and speed of human feedback with the need for high-quality data, ensuring consistency and reliability of human ratings across different evaluators and over time. Avoiding biases or blind spots in human judgments that may skew the model's performance. Managing the complexity and overhead of human-in-the-loop workflows, especially at scale, and ensuring the security and privacy of sensitive data used in the feedback process.

9. How can you effectively combine human and machine intelligence in LLM workflows?

To effectively combine human and machine intelligence, it's important to play to the strengths of each: use LLMs for fast and scalable text generation, and humans for nuanced evaluation and judgment. Design workflows and interfaces that enable smooth collaboration and communication between humans and AI. Provide humans with the right tools and information to make informed decisions and give actionable feedback. Continuously learn and adapt based on human input, while also empowering humans to override or adjust model outputs as needed. Foster a culture of trust, transparency, and accountability around the use of LLMs in decision-making processes.

10. What are some key ethical and social considerations around human feedback and oversight of LLMs?

Some key ethical and social considerations include ensuring that the humans providing feedback are diverse and representative of the intended users and stakeholders. Paying fair wages and providing good working conditions for human evaluators, especially if using crowdsourcing or gig work platforms. Also, being transparent about how human feedback is collected and used.

‍