Holistic Evaluation of Language Models (HELM)

The Holistic Evaluation of Language Models (HELM) is a standardized, transparent framework for evaluating Language Models (LMs). It aims to provide a comprehensive view of model capabilities, limitations and risks by:

• Holistic Evaluation: Assessing over 30 prominent models across 42 diverse real-world scenarios (tasks, domains, languages).
• Multi-Metric Measurement: Using seven key metrics that go beyond simple accuracy, calibration, robustness, fairness, bias, toxicity and efficiency.
• Standardization and Transparency: Ensuring all models are compared under uniform conditions and making the methodology and raw results publicly available

Here Language Model is evaluated against multiple real world scenarios and comprehensive metrics

HELM evaluation starts by testing a candidate LLM on diverse real world tasks like summarization or toxicity detection using a variety of benchmark datasets. It then measures performance across multiple dimensions not just accuracy but also fairness, robustness and efficiency. Finally, the results are published in open reports and leaderboards helping the community compare models and understand their strengths and trade offs.

Evaluation Method and Metrics

Evaluation Framework

HELM formalizes model evaluation as :

E = (S, M, \mathcal{A})

where:

• S represents the scenario (task, domain, language)
• M represents the metric (e.g., accuracy, fairness)
• \mathcal{A} denotes the adaptation procedure (e.g., few-shot, zero-shot, instruction-tuned prompting)

Each language model f_{\theta}​ maps an input x to a completion y = f_{\theta}(x)

For a dataset with N samples, the metric score is computed as:

\text{Metric Score} = \frac{1}{N} \sum_{i=1}^{N} \mathcal{L}(y_i, y_i^*)

where

• \mathcal{L} is the metric-specific loss or scoring function,
• y_i^* is the ground truth or reference output.

HELM Scenario Taxonomy

Rather than focusing on academic tasks, HELM uses 42 real-world contexts, including:

• Question answering & summarization
• Multilingual and cross-cultural dialogue
• Classification & information extraction
• Ethical or adversarial prompts

This variety reveals each model’s strengths and weaknesses in realistic use cases.

Evaluation Metrics

HELM evaluates each model using seven key metrics, collectively forming a multi-dimensional performance profile.

1. Accuracy : Measures how often the model predicts the correct output

\text{Accuracy} = \frac{1}{N} \sum_{i=1}^{N} \mathbf{1}(y_i = y_i^*)

where:

• ​ y_i is the model output
• ​ y_i^*is the ground truth

2. Calibration : Measures how well the model’s predicted confidence aligns with actual correctness

\text{Calibration Error} = \mathbb{E}[(p_i - \mathbf{1}(y_i = y_i^*))^2]

where p_i is the model’s confidence for prediction​.

3. Robustness : Tests model stability under small input perturbations

\text{Robustness} = \min_{\delta \in \Delta} \text{Accuracy}(x + \delta)

where \delta represents input noise or modifications

4. Fairness : Evaluates whether model performance is consistent across demographic or social groups.

\text{Fairness Gap} = |\text{Acc}_A - \text{Acc}_B|

where \text{Acc}_A and ​ \text{Acc}_B are accuracies for two demographic groups.

5. Toxicity : Measures the probability that the model generates harmful, offensive or biased text.

\text{Toxicity Score} = P(\text{toxic output} \mid \text{prompt})

Higher values indicate greater risk of unsafe responses

6. Efficiency : Represents model performance relative to computational cost.

\text{Efficiency} = \frac{\text{Accuracy}}{\text{Compute Cost}}

where Compute Cost is latency or energy consumption

7. Transparency : Indicates how openly a model’s training data, architecture and evaluation methods are disclosed. Evaluated through documentation quality and public availability of results.

Each model’s performance can be expressed as a multi-dimensional vector

\mathbf{m}_{\text{model}} = [m_{\text{acc}}, m_{\text{cal}}, m_{\text{rob}}, m_{\text{fair}}, m_{\text{tox}}, m_{\text{eff}}, m_{\text{tra}}]

Step-By-Step Implementation

Here we run a lightweight sentiment model on sentiment analysis dataset and compute HELM-style evaluations and simple calibration and robustness checks. Then we show how to wrap results into a HELM-compatible workflow.

Step 1: Import Required Libraries

• Import all the necessary libraries such as transformers, evaluate, numpy and torch.
• The pipeline from transformers is used for text classification.
• evaluate provides predefined metrics for model evaluation.
• GPU is used if available to speed up model inference.

from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
import evaluate
import numpy as np
import torch
from torch.nn.functional import softmax

pipeline_device = 0 if torch.cuda.is_available() else -1

Step 2: Load Model and Tokenizer

• Load the lightweight pretrained model.
• The tokenizer converts input text into tokens suitable for the model.
• The pipeline function combines tokenization and inference in one step.

model_name = "distilbert-base-uncased-finetuned-sst-2-english"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
sentiment_analyzer = pipeline("text-classification", model=model, tokenizer=tokenizer, device=pipeline_device)

Step 3: Prepare Dataset

• Extract the text and sentiment columns from your DataFrame df.
• Convert them to lists for easy processing with the model and metrics.

texts = df["text"].tolist()
true_labels = df["sentiment"].tolist()

Step 4: Run Model Inference

• Pass the text data to the sentiment analysis pipeline.
• Model returns both the label and the confidence score.

predictions = sentiment_analyzer(texts, truncation=True)
predicted_labels = [1 if p["label"].upper().startswith("POS") else 0 for p in predictions]

Step 5: Compute Evaluation Metrics

• Load predefined metrics such as accuracy, precision, recall and F1 score.
• Compute these metrics using the predicted and true labels.
• Print out the results to evaluate model performance comprehensively.

accuracy = evaluate.load("accuracy")
f1 = evaluate.load("f1")
precision = evaluate.load("precision")
recall = evaluate.load("recall")

acc_score = accuracy.compute(predictions=predicted_labels, references=true_labels)
f1_score = f1.compute(predictions=predicted_labels, references=true_labels)
prec_score = precision.compute(predictions=predicted_labels, references=true_labels)
rec_score = recall.compute(predictions=predicted_labels, references=true_labels)

print("\nHELM Evaluation Metrics")
print(f"Accuracy : {acc_score['accuracy']:.4f}")
print(f"Precision: {prec_score['precision']:.4f}")
print(f"Recall   : {rec_score['recall']:.4f}")
print(f"F1 Score : {f1_score['f1']:.4f}")

Output:

Step 6: Compute Calibration Metric

• Calculate model confidence for each text using the softmax probability.
• Take the maximum probability as the confidence score for that sample.
• Compute the average confidence across all samples.

def compute_confidence_batch(model, tokenizer, texts, batch_size=8, device=None):
    device = device if device else (torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu"))
    model.to(device)
    confidences = []
    for i in range(0, len(texts), batch_size):
        batch = texts[i:i+batch_size]
        encoded = tokenizer(batch, return_tensors="pt", truncation=True, padding=True).to(device)
        with torch.no_grad():
            outputs = model(**encoded)
            probs = softmax(outputs.logits, dim=-1)
            max_probs, _ = torch.max(probs, dim=-1)
            confidences.extend(max_probs.cpu().tolist())
    return np.array(confidences)

confidences = compute_confidence_batch(model, tokenizer, texts, batch_size=16)
avg_confidence = np.mean(confidences)

print("\nApproximate Calibration Metric")
print(f"Average Model Confidence: {avg_confidence:.4f}")

Output:

Step 7: Robustness Check

• Create slightly modified versions of the input text by replacing words.
• Run the model again on the modified data.
• Compare accuracy before and after perturbation to assess model robustness.

perturbed_texts = [t.replace(" good ", " excellent ").replace(" bad ", " terrible ") for t in texts]
perturbed_preds = sentiment_analyzer(perturbed_texts, truncation=True)
perturbed_labels = [1 if p["label"].upper().startswith("POS") else 0 for p in perturbed_preds]

robustness_acc = accuracy.compute(predictions=perturbed_labels, references=true_labels)
print("\nRobustness Check")
print(f"Accuracy after small perturbations: {robustness_acc['accuracy']:.4f}")

Output:

You can download full code file from here.

Advantages of HELM

Holistic Evaluation of Language Models offers a unified and transparent framework for assessing LMs beyond just accuracy.

• Comprehensive Evaluation: It assesses models across seven metrics for a complete performance view.
• Standardization: It uses uniform datasets and conditions, enabling fair and reproducible comparisons across models.
• Transparency: It publicly releases data, prompts and results to ensure reproducibility and trust.
• Broad Coverage: It tests on diverse scenarios spanning reasoning, summarization, sentiment and safety, achieving high evaluation coverage.
• Multi-Dimensional Insights: It enables trade off analysis between accuracy, fairness and efficiency.
• Responsible AI: It promotes safer, unbiased and ethical language model development.
• Extensible Framework: It is continuously updated with new models, tasks and multilingual benchmarks.

Challenges

While HELM provides a comprehensive framework for evaluating Language Models (LMs), it also faces certain limitations and implementation challenges:

• High Computational Cost: Evaluating large models across scenarios and multiple metrics requires massive compute resources.
• English-Centric Evaluation: Most HELM scenarios are based on English datasets, limiting its effectiveness in assessing multilingual or culturally diverse models.
• Limited Real-World Context: The framework primarily focuses on static text-based tasks and does not fully capture dynamic interactions or real-world system behavior.
• Metric Trade-offs: Improving one metric can sometimes degrade others making overall optimization complex.
• Incomplete Coverage of Ethical Dimensions: Metrics like interpretability, transparency and environmental impact are still underrepresented in current HELM releases.

Language Model Evaluation Frameworks

Here we compare major language model evaluation frameworks to understand how they differ.