Evaluating models fine-tuned with RLHF requires moving beyond traditional NLP metrics. While metrics like perplexity, BLEU, or ROUGE can gauge fluency or semantic similarity, they fall short in measuring alignment with complex human preferences related to helpfulness, honesty, and harmlessness. Assessing an RLHF-tuned model demands a specialized set of metrics tailored to these alignment goals.

As outlined in the chapter introduction, achieving alignment means ensuring the model behaves according to human intent and values. Standard metrics don't capture whether a response, even if fluent and relevant, is actually helpful, truthful, or safe. Therefore, we need metrics specifically designed to probe these dimensions.

The Limitations of Standard NLP Metrics

Consider a model fine-tuned to be helpful. A response might achieve a high ROUGE score against a reference answer by including similar keywords, yet fail to provide actionable advice or misunderstand the user's underlying need. Similarly, perplexity measures fluency under a language model's probability distribution but doesn't assess factual accuracy or safety. Relying solely on these metrics can be misleading when the objective is alignment.

Measuring the "HHH": Helpfulness, Honesty, and Harmlessness

A widely adopted framework for evaluating LLM alignment centers on three pillars: Helpfulness, Honesty, and Harmlessness (HHH). Metrics are often developed to target one or more of these aspects.

Metrics for Helpfulness

Helpfulness relates to how well the model assists users in achieving their goals, answering questions accurately and completely, and providing useful information.

Human Preference Scores: While discussed further in the section on human evaluation protocols, the aggregated results of pairwise comparisons or rating scales (e.g., Likert scales for satisfaction) are often distilled into quantitative scores. A higher average rating or win rate against a baseline model serves as a direct metric of perceived helpfulness.
Task Success Rates: For specific domains or benchmarks where the LLM assists with a concrete task (e.g., code generation, information retrieval, summarization), task completion rates or accuracy can serve as a proxy for helpfulness.
Elo Ratings: Models can be ranked using an Elo rating system based on pairwise human judgments, providing a relative measure of helpfulness compared to other models.
Reward Model (RM) Scores: The score assigned by the trained reward model itself can be used as a proxy metric for helpfulness, as defined by the preference data. However, this requires careful interpretation due to potential calibration issues and the risk of reward hacking, where the policy optimizes for the RM score without genuinely improving helpfulness.

Metrics for Honesty and Truthfulness

Honesty involves the model providing factually accurate information, avoiding fabrication ("hallucination"), admitting uncertainty when appropriate, and citing sources if applicable.

Accuracy on Question-Answering Benchmarks: Specialized datasets like TruthfulQA are designed to measure whether a model avoids generating false statements that mimic common human misconceptions. Performance is typically measured by the percentage of questions answered truthfully, often distinguishing between generating a truthful statement and correctly identifying truthful statements in multiple-choice settings.
Calibration Metrics: Evaluating how well the model's confidence scores (e.g., probabilities assigned to tokens) align with its actual accuracy. A well-calibrated honest model should express lower confidence when it is likely to be wrong. Metrics like Expected Calibration Error (ECE) can be adapted for this purpose.
Hallucination Rate: Measured via human review or automated checks (e.g., comparing generated summaries against source documents), this quantifies the frequency of factual inaccuracies or fabricated information in the model's output.

Metrics for Harmlessness

Harmlessness focuses on the model's propensity to avoid generating toxic, biased, discriminatory, unsafe, or unethical content.

Toxicity Detection Scores: Using external classifiers (like the Perspective API or specialized models trained on datasets like ToxiGen) to score the toxicity level of model outputs. Metrics often involve the average toxicity score or the percentage of outputs flagged as toxic above a certain threshold.
Performance on Safety Benchmarks: Evaluating the model's responses to prompts designed to elicit harmful content (e.g., subsets of RealToxicityPrompts, custom red-teaming prompts). Key metrics include:
- Refusal Rate: The percentage of inappropriate or harmful prompts for which the model correctly refuses to provide a substantive answer.
- Harmful Content Rate: The percentage of prompts for which the model generates harmful content, often categorized by type (e.g., hate speech, discrimination, illegal advice).
Bias Metrics: Using benchmarks like Bias Benchmark for QA (BBQ) or analyzing performance disparities across different demographic groups (e.g., using Winogender Schemas) to quantify social biases reflected in model outputs.

Diagram illustrating the relationship between the HHH alignment pillars and the types of metrics used to evaluate them.

Using Reward Model Scores and KL Divergence

While not direct measures of ground-truth alignment, two other quantities generated during RLHF are informative:

Reward Model Score: As mentioned, the average score assigned by the RM to the policy model's outputs during evaluation provides insight into how well the policy is optimizing the learned preference objective. A high RM score suggests the policy is behaving in ways the RM predicts humans would prefer. Tracking this score during training is essential, but it should be interpreted alongside other metrics due to the risk of the policy exploiting loopholes in the RM (reward hacking).
KL Divergence: The Kullback-Leibler (KL) divergence between the RLHF-tuned policy ( $\pi_{RL}$ ) and the initial SFT policy ( $\pi_{SFT}$ ) measures how much the policy has changed during RL fine-tuning. It's typically used as a penalty during PPO training ( $KL(\pi_{RL} || \pi_{SFT})$ ) to prevent the policy from deviating too drastically from the SFT model, preserving language quality and general capabilities. While primarily a constraint, the final KL divergence value can be reported as a metric. A very high KL might indicate significant behavioral changes (potentially good for alignment, but risky for capabilities), while a very low KL might suggest the RL tuning had minimal impact. Interpreting KL divergence requires context about the training setup and other evaluation results.

Aggregated Evaluation

Often, a single metric is insufficient. Evaluating alignment typically involves a suite of metrics covering HHH aspects, alongside traditional capability benchmarks. Tools like the Holistic Evaluation of Language Models (HELM) framework or the EleutherAI Language Model Evaluation Harness provide infrastructure for running standardized evaluations across many metrics and datasets, offering a more comprehensive picture of model performance and alignment. These automated suites are discussed further in a later section.

Choosing the right set of metrics depends on the specific alignment goals for your model and the potential risks associated with its deployment context. A combination of automated metrics and human evaluation is usually necessary for a thorough assessment.