User behavior and data drift in LLMs

Data drift-the subtle yet impactful change in data distribution over time-has been a significant concern in MLOps and extends to LLMOps. LLM-based applications are currently used across different domains, and as their uses expand, the need for reliability over time becomes pertinent. The primary source of input data for LLM-based applications is queries, which can be modeled from user behavior. Data drift can arise from evolving user behavior, LLM system updates, shifting social norms, and even targeted adversarial attacks. The different types of data drifts need to be understood and managed to continuously maintain the high efficacy of LLM-based systems.

Photo by Emre Aslihak

LLMs and LLM-based applications input user data, which are data points that shape the model’s behavior and performance. For example, user queries in retrieval-augmented generation (RAG) systems guide the retrieval and generation process, and the user’s behavior-how they phrase their questions, the topics they inquire about, and the frequency of certain types of queries-will yield different results and influence how these models perform over time.

The long-term effects of sustained changes in user behavior are even more concerning. Over time, if input data continues to drift away from a model’s training distribution, its internal understanding of relevant concepts becomes corrupted. This may result in broader inconsistencies in output as the model struggles to adapt to new patterns in the data. Without intervention, such as fine-tuning or updating the knowledge base for RAG or re-engineering its prompts, such drift can lead to a dramatic loss of performance, turning a highly effective system into one that is much less effective or even completely unusable in its intended domain.

Understanding the many forms of drift that may ultimately impact LLMs is important for maintaining their performance and reliability. The following section describes the primary forms of drift: user input drift, prompt drift, and system drift.

User input drift

User input drift occurs when there is a change in the nature, context, or distribution of user-provided inputs to an LLM over time from what the LLM was originally trained or fine-tuned on. This can be caused by the following:

• Shift in user demographics in the form of diversity and style of inputs
• Seeking information on new topics or in different formats
• Changes in trends, slang, and popular themes
• Using an LLM for purposes beyond its intended scope
• Target adversarial attack on the LLM system

Early detection of user input drift is crucial for timely intervention with strategies such as monitoring query patterns to keep tabs on the changing nature of topics, complexity, and formats that emerge around user queries over time and using statistical techniques to identify significant variations in input data distributions relative to historical data. Also, analysis of user feedback is needed to identify emerging trends and shifts in information needs.  In addition to detection strategies, domain relevance can be maintained and restored by guiding users to the system’s intended scope of queries. Dynamic retraining or updating the knowledge base to reflect current user input patterns can also help to ensure adaptability.

Prompt drift

Prompt drift: This refers to changes in the structure, style, or content of prompts (which can be either user-facing or system-defined), with an aim to elicit a response from LLMs. Changes in prompts may occur due to:

• User adaptation: Users can modify their wording of queries for better results or due to changing communication styles.
• System updates: Adjusting prompt templates based on performance or previous shortcomings.
• Contextual variations: Different contexts or applications may have different prompting strategies.

Variations in prompt phrasing can lead to different answers to the same underlying question. Moreover, improperly structured prompts may make the responses less coherent or relevant. The LLM might misunderstand the intent behind altered prompts, leading to inappropriate or off-topic answers, increasing latency, and taking longer to generate responses.

Identifying prompt drift involves regularly evaluating the prompts being used to ensure they align with pre-established templates, formats, or guidance. Run controlled experiments through A/B testing and compare their outputs from different prompt structures to assess the effectiveness of the approaches. If adaptive prompting strategies are utilized, monitor how the prompts are dynamically adjusted based on context and the desired outputs.

System drift

System drift refers to the general changes in the performance of the LLM due to changes in infrastructure, configuration, or environment. Causes include:

• Model Architecture Updates: Most proprietary LLMs are opaque and can’t track every change made by their providers. Modifications to the model’s structure, such as periodic updates or retraining, can impact its functioning. For example, a study highlighted a ChatGPT drift and found that over a period of 4 months, the response accuracy of GPT-4 and GPT-3.5 fluctuated in both the positive and negative. Even though the inputs were controlled, there was variability in the models’ outputs at different time frames-a drift.
  

  GPT-4 and GPT-3.5 variation over time, Arxiv

• Integrations:Changes in embedding models, vector stores, external knowledge bases, frameworks, and possibly hardware and software dependencies may lead to unforeseen consequences.

Due to the various moving parts of an LLM-based application, it is important to have infrastructure tracking the various versions of integrations and LLMs utilized. Also, a rollback mechanism should be established to revert to previous stable versions in case of adverse effects from updates. Comprehensive testing is encouraged after each change to ensure all components function correctly and cohesively.

Data drift can significantly impact the reliability and performance of LLM-based workflows. Below are some key approaches to mitigate and monitor these drifts:

Continuous monitoring of user inputs

Implement systems that regularly collect and analyze input data, queries, and prompts to detect shifts in patterns, topics, or style. This can be achieved via statistical analysis of the current input data distribution with historical data, trend analysis to monitor emerging changes in user behavior over time, and anomaly detection to identify unusual spikes or drops in text quality, which could point to potential drift or adversarial attacks. Data quality assurance can be employed with regular audits to ensure high-quality,  unbiased, and non-adversarial input data.

User feedback mechanism

Incorporate various feedback channels into your LLM-based workflow to capture user thoughts on the use of the system, allow reporting of any issues, and provide ratings. This feedback can quickly highlight user perspectives, point to system anomalies and specific text qualities, and help identify and prioritize critical issues affecting user satisfaction, which may not be apparent via automated monitoring alone. The feedback mechanisms can take the form of surveys and ratings, feedback loops, and sentiment analysis of user feedback.

Adaptive Prompting Strategies

Develop prompting strategies that dynamically adjust input or system prompts in order to optimize LLM performance based on the specific context, tasks, or user interactions. These strategies provide personalization, systems that are context-aware,  and dynamic adjustments,  increasing user engagement and improving system reliability and consistency. Some strategies include input query rewriting or expansion, self-reflective prompting, automatic chain of thoughts (Auto-CoT) prompting, and developing a standard input prompt template structure for users to minimize variability based on different contexts.

Regular Model Evaluation

Schedule regular quantitative (performance metrics or benchmark dataset) and qualitative (human or LLM-assisted) evaluations of LLMs used for applications to ensure they continue to perform effectively over time. Different experiments which might involve different models and configurations should be carried out in controlled environments to determine the most effective setup. Techniques such as A/B testing should be employed when releasing these systems to production, and user impact analysis should be carried out to determine how changes affect user satisfaction and engagement.

System version control and documentation

Implement comprehensive version control to track changes in LLM versions, system configurations, prompts, and integrations, as seen in several critical components in the LLM-based applications lifecycle in the figure below. Ensure change log documents, updates, and rollback procedures are established to revert to previous stable versions if new changes introduce issues to minimize system downtime. Lastly, maintain meticulous records of these changes and updates to provide a guide for tracing and troubleshooting issues and understanding the impacts of modification to the system.

LLM application lifecycle, by Josh Poduska

Scenario Planning and Testing

Proactive testing through simulation, stress tests, and penetration tests prepares the system for potential drift scenarios. Utilize synthetic data that simulate possible future changes in user behavior or input patterns and assess how the system performs with these synthetic data. Stress and penetration tests test the system under conditions such as edge cases, rare events, and extreme input loads, finding weaknesses and vulnerabilities through adversarial or unexpected inputs, pointing to the system’s generalizability. These help identify areas where the model is sensitive to distributional shifts, signaling potential drift.  Lastly, LLM-based workflows should be regularly assessed on different ethical considerations based on the geographics of use and application.

As we’ve explored, data drift in LLMs can lead to significant performance degradation. Changes in user behavior and prompt structures, as well as updates in systems over time, are the likely causes of drift. This is of particular concern in critical applications where safety and trust are paramount, where unexpected drifts could mean inaccurate or harmful responses that undermine system value. Some strategies have been recommended to aid proactive management of data drift in LLM workflows to ensure that their systems remain effective, reliable, and aligned with user needs. We can maintain the efficacy and reliability of LLM-based systems if we remain vigilant and proactive about incorporating such improvements.