Ways to Monitor LLM Behavior

Large language models (LLMs) like GPT-4, LLaMA-2, Claude, and others have shown immense promise in their ability to generate human-like text and engage in intelligent dialogue. However, as these models grow more powerful, there is an increasing need to monitor their behavior to prevent issues like bias, toxicity, and factual incorrectness.

Now Let’s explore the following topics related to monitoring LLMs:

• The risks and challenges of uncontrolled LLMs
• Key indicators to monitor in LLMs
• Manual monitoring methods
• Automatic monitoring tools — LangKit & WhyLabs
• Hybrid monitoring approaches
• Monitoring model behavior changes over time
• Next steps in LLM monitoring

Risks and Challenges of Uncontrolled LLMs

As language models become more advanced, they run the risk of inherited, learned, and amplified biases. For example, models trained on vast swaths of internet data can pick up toxic, racist, sexist rhetoric. Even with efforts to filter the training data, traces of harmful biases can sneak through. If deployed unchecked, these models could spread misinformation or cause real-world harm through abusive, toxic responses.

There are also risks around factual correctness. Language models can hallucinate facts or generate convincing but entirely made up responses. As they continue to improve in their ability to sound human, it may become increasingly difficult to detect incorrect information without robust monitoring.

In addition, there are economic risks associated with uncontrolled language generation. Models that can churn out human-sounding text at scale could be used for mass-scale disinformation campaigns, content farm spamming, political astroturfing and more. The environmental impacts are also substantial — training ever-larger models consumes massive amounts of energy and computing resources.

For all these reasons, maintaining strict oversight over LLMs through ongoing monitoring is crucial. Companies like Anthropic have formed oversight committees and implemented constitutional AI techniques to constrain model behavior. However, vigilance is needed to ensure these guardrails remain effective as models evolve.

Key LLM Behavior Indicators to Monitor

When monitoring LLMs, there are several key indicators we can track over time:

• Bias and toxicity: Monitoring aggregated metrics on the bias and toxicity characteristics of model outputs based on protected attributes like race, gender, religion, and more. Also tracking toxicity like threats, abusive language, or steelmanning of harmful ideologies.
• Factual accuracy: Evaluating responses for hallucinated facts, made up information falsely claimed as true, or inconsistencies with the real-world. Verifying correctness on historical events, scientific knowledge, current events and more. Conducting robustness checks by perturbing inputs.
• Relevance: Assessing whether model outputs adhere to the conversational context and directives provided by the human user. Monitoring for non-sequiturs.
• Referenced sources: Reviewing what sources the model cites in responses and cross-checking against known reliable information sources.
• Plagiarism: Programmatically comparing model outputs to existing internet content to detect copying or lack of originality.
• Cost-risk estimates: For models with capabilities to compare options and estimate hypotheticals, validating model logic and math. Testing predictions against real-world outcomes.

We’ll now dig deeper into established methods for monitoring LLMs across these behavioral indicators.

Manual Monitoring Methods

The most basic form of monitoring is manual human review. This can be quite labor intensive but allows for nuanced qualitative assessment of model behavior. Some best practices for manual review include:

• User surveys: Gathering feedback through questionnaires or simple rating scales on perceived bias, toxicity, accuracy etc. Crowdsourcing these reviews can help scale insights.
• Spot checks: Having human reviewers directly interact with the model by posing written prompts and assessing responses. This provides first-hand qualitative catch of issues.
• Output reviews: More systematically sampling model outputs generated for end users and having reviewers label issues. This moves beyond one-off spot checks for more representative coverage.
• Background source checking: Identifying factual claims made by the model and cross-referencing against known reliable sources.
• Verifying predictions: For models making real-world forecasts, tracking outcomes when possible to quantify error rates.
• Benchmark performance: Evaluating models against existing benchmarks that test for desirable qualities through question answering, logical reasoning, common sense, and more. Monitor for regression.
• Debugging strange behaviors: Having technical oversight teams investigate model oddities raised through informal channels or complaints. The goal is catching substantial behavior drifts early.

The downside of manual monitoring is clearly a lack of scalability. It is time-intensive, inconsistent, and liable to overweight dramatic singular examples over subtle systemic shifts. This motivates the need for automated techniques.

Automatic Monitoring Tools

Specialized tools have emerged to help automate LLM monitoring by programmatically catching issues at scale. This allows for continuous oversight. We will highlight two leading platforms in this space — LangKit and WhyLabs — before discussing other options.

LangKit

LangKit offers robust capabilities specifically for monitoring large language models. It casts a wide net across the key indicators discussed earlier.

For bias and toxicity detection, LangKit utilizes classifiers trained on labeled data of offensive language. This allows estimation of toxic response rates across model versions and input perturbation tests. Analysis can be segmented by attributes like race, gender, religion and more to catch disproportionate issues.

Factual accuracy scoring relies on sophisticated QA tools and corroborative searching. Model outputs are parsed against knowledge bases and search engines to automatically surface false claims. Hallucinated facts are identified by tracing statements back to reliable sources.

Additionally, LangKit has modules for plagiarism detection, relevance analysis, contradiction identification, and citation tracking. It also monitors benchmark performance over time.

These assessments can be layered, so that problematic responses raise multiple red flags. Aggregate reporting visualizes trends across metrics enabling deep behavioral analysis.

WhyLabs

Whereas LangKit focuses specifically on LLMs, WhyLabs provides a general ML monitoring platform compatible with large language models. Its core capability is comparing model variants.

WhyLabs has an A/B testing framework that deploys different model versions side-by-side to live traffic. This could mean trying larger model sizes, different training data, or code changes. The platform tracks key performance indicators (KPIs) between variants — for LLMs, this can include toxicity, fact-checks, user ratings and more.

Statistical tests quantify significant differences between variants across KPIs. Unexpected differences provide signals on potential model degradation to roll back and debug. Product managers can also promot the best performing variants based on the KPIs that matter most.

WhyLabs further helps diagnose model errors by clustering mispredictions and surfacing salient examples. It also monitors data drift against benchmarks to catch sudden drops. Together this provides rapid iteration.

Other Automated Monitoring Tools

There is a growing ecosystem of alternative tools for automated LLM monitoring beyond LangKit and WhyLabs, though with less specialization. Examples include:

Weights & Biases: An end-to-end MLOps platform with experiment tracking and model comparison capabilities similar to WhyLabs.

Replicate: A monitoring tool focused specifically on dataset bias indicators for ML models, using techniques like representativeness scoring. Helps mitigate issues before deployment.

Monitaur: An open-source toolkit from Anthropic focused on stress testing conversational AI models with adversarial attacks and sanity checks. Enables scaling security evaluations.

Amazon CodeGuru: A developer tool that profiles application code changes for defects and security risks in automated builds. Applicable to surface software issues in LLM update pipelines.

Hybrid Approaches

In practice, most robust monitoring regimes combine both manual human reviews and automated tools. Together they balance scalability and nuanced oversight.

Automated tools cast a wide net to surface suspicious instances for human review. Domain experts then provide qualitative inputs on the severity of issues and decide on necessary interventions. Their judgments further improve and tune detection models.

Conversely, anomaly reports from front-line teams help inform development of new automated checks to cover previously missed issues. Hybrid approaches also allow blending universal automated tests with context-specific manual spot checks.

We will now shift the discussion to how monitoring needs to continue as models evolve through successive versions. Maintaining oversight requires tracking behavior over time rather than at a single point.

Monitoring LLM Behavior Over Time

LLM improvement involves an endless cycle of data collection, model training, evaluation, and updates. Monitoring must persist across these iterations. Models gradually accumulate changes that could alter performance for better or worse. Keeping behavior in check requires constant vigilance even on top of rigorous pre-deployment vetting.

We outline here leading strategies for continual monitoring across model updates.

Commitment to Responsible Development

The first requirement is simply an organizational commitment to responsible LLM development. Groups like OpenAI have pledged to uphold Constitutional AI principles in model design. This ethos guides the adoption of best practices in transparency, ethics and robustness. Monitoring processes formalize oversight mechanisms aligned to these values.

Baselining

Before releasing any model version, developers should establish behavioral baselines. This means profiling performance on key indicators of bias, accuracy, toxicity etc. against test data and benchmarks. Baselines quantify where the model stands on meeting standards to help evaluate subsequent change.

Frequent Testing

As models update, automated testing helps catch emerging issues through continual regression monitoring. Running overnight test suites on new commits provides rapid signals between longer-term evaluations. Unit testing components and integration flows isolates build breaks.

Orthogonal Methods

No single test offers full coverage of potential problems. Monitoring systems should incorporate orthogonal methods combining: behavioral testing, adversarial testing, metadata tracking, user surveys and more. Each approach catches outliers the others could miss.

Staged Deployments

Pushing model changes directly to all end users poses availability risks if new issues surface. Staged roll outs first expose updates to smaller groups, monitoring outcomes before scaling to larger production volumes. Issues caught early reduce harm.

Observable Qoos

Models should log rich telemetry on behavioral signals and feature usage. Monitoring systems can subscribe to these event streams for insight into technical health issues or shifts in consumption patterns demanding attention.

Periodic Reviews

Even with continual oversight, teams should conduct structured reviews on longer time frames across models. Quarterly or biannual audits prompt deeper investigation into performance trends. They also catch incremental model drifts that daily monitoring could miss.

The next horizon for LLM monitoring will apply self-supervised techniques to model behavior using few-shot learning. Models could potentially learn to identify harmful deviations against established standards with less manual rule creation or labeling. This would accelerate oversight further.

Closing Thoughts on LLM Monitoring Systems

As large language models advance in capability, the risks posed by uncontrolled behavior at scale escalate drastically. Avoiding harm requires thorough, ongoing monitoring mechanisms to keep model performance aligned with ethical, factual and social expectations.

Manual methods provide high signal oversight but lack comprehensive coverage for large scale deployment. Automated tools help identify problems at scale by codifying assessments across key indicators like toxicity and accuracy. LangKit and WhyLabs represent leading platforms purpose-built for LLM monitoring leveraging natural language techniques, adversarial testing, and statistical analytics. Still, even the best tools benefit from blending with human-in-the-loop reviews.

LLM development cycles never cease. Updates that improve performance could also introduce subtle deviations in behavior that require rapid response. Organizations must commit to responsible innovation through pre-deployment vetting, staged releases, observability and layered continual assessments.

There remain open challenges in building end-to-end trust & safety. Few shot learning offers promise for models to eventually self-monitor against standards they co-establish with developers through reinforcement paradigms. For now, responsibility falls on the creators and operators of these models to uphold ethical constraints against an endless array of potential issues using comprehensive, transparent and vigilant oversight.

The stakes could not be higher as large language models grow more pervasive and impactful. Unchecked, uncontrolled LLMs at scale present risks to individuals and society we simply cannot afford to bear. Our future demands commitment to Constitutional AI through robust monitoring.