Pros of lm-evaluation-harness

• More comprehensive evaluation framework with a wider range of tasks and metrics
• Supports multiple language models and frameworks (e.g., HuggingFace, OpenAI)
• Actively maintained with regular updates and contributions

Cons of lm-evaluation-harness

• More complex setup and configuration required
• Steeper learning curve for new users
• Potentially slower execution due to its comprehensive nature

Code Comparison

lm-evaluation-harness:

from lm_eval import evaluator, tasks

results = evaluator.simple_evaluate(
    model="gpt2",
    tasks=["hellaswag", "winogrande"],
    num_fewshot=2,
)

test:

from test import multiple_choice

score = multiple_choice.evaluate(model, dataset="hellaswag")

The lm-evaluation-harness provides a more flexible and comprehensive evaluation framework, allowing for multiple tasks and customizable parameters. The test repository offers a simpler, more straightforward approach for specific tasks but with less flexibility.

Pros of evals

• More comprehensive evaluation framework with support for various model types
• Actively maintained with regular updates and contributions
• Includes tools for creating custom evaluations and analyzing results

Cons of evals

• Steeper learning curve due to more complex architecture
• Primarily focused on OpenAI models, potentially limiting its applicability

Code comparison

test:

def check_correctness(completion, answer):
    return completion.strip() == answer.strip()

def grade_question(completion, answer):
    return 1 if check_correctness(completion, answer) else 0

evals:

class Eval(ABC):
    @abstractmethod
    def eval_sample(self, sample: Any, *args: Any, **kwargs: Any) -> Dict[str, Any]:
        pass

    @abstractmethod
    def run(self, recorder: RecorderBase) -> None:
        pass

The code snippets highlight the difference in complexity between the two repositories. test uses simple functions for grading, while evals employs a more structured, object-oriented approach with abstract classes for creating custom evaluations.

Pros of evaluate

• Comprehensive evaluation framework with a wide range of metrics and tasks
• Seamless integration with Hugging Face's ecosystem and models
• Active development and community support

Cons of evaluate

• More complex setup and usage compared to test
• Potentially overwhelming for simple evaluation tasks
• Requires additional dependencies and resources

Code comparison

test:

from test import tasks
score = tasks.math.arithmetic.evaluate_model(model)

evaluate:

from evaluate import load
metric = load("accuracy")
results = metric.compute(predictions=preds, references=refs)

Summary

evaluate offers a more robust and versatile evaluation framework, particularly suited for complex NLP tasks and integration with Hugging Face's ecosystem. test provides a simpler, more focused approach for specific benchmarks, especially in areas like math and reasoning. evaluate's flexibility comes at the cost of increased complexity, while test offers a more straightforward implementation for its targeted tasks. The choice between the two depends on the specific evaluation needs and the desired level of integration with other tools and models.

Pros of BIG-bench

• Larger and more diverse set of tasks, covering a wider range of capabilities
• Collaborative effort with contributions from multiple organizations and researchers
• Includes more complex and multi-step tasks that test advanced reasoning abilities

Cons of BIG-bench

• Less standardized format across tasks, making it potentially more challenging to implement
• May require more computational resources due to the larger number and complexity of tasks
• Some tasks may be less relevant for general language model evaluation

Code comparison

BIG-bench task example:

def evaluate_model(model, examples):
    scores = []
    for example in examples:
        prediction = model.generate(example['input'])
        score = compute_score(prediction, example['target'])
        scores.append(score)
    return sum(scores) / len(scores)

Hendrycks test example:

def evaluate_model(model, examples):
    correct = 0
    for question, choices, answer in examples:
        prediction = model.predict(question, choices)
        if prediction == answer:
            correct += 1
    return correct / len(examples)

Both repositories provide valuable benchmarks for evaluating language models, with BIG-bench offering a broader range of tasks and Hendrycks test focusing on more standardized, multiple-choice formats.