Benchmarking AI: Evaluating Large Language Models (LLMs)

Have you ever wondered how companies like OpenAI or Google ensure their chatbots don't go off the rails? Welcome to the world of Large Language Model (LLM) evaluation – a crucial yet often overlooked aspect of AI development. In this post, we'll dive into the nuts and bolts of LLM evaluation, equipping you with the knowledge to assess and improve these powerful language models.

import torch
from transformers import GPT2LMHeadModel, GPT2Tokenizer

model_name = 'gpt2'
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

def calculate_perplexity(text):
    encodings = tokenizer(text, return_tensors='pt')
    max_length = model.config.n_positions
    stride = 512
    nlls = []
    for i in range(0, encodings.input_ids.size(1), stride):
        begin_loc = max(i + stride - max_length, 0)
        end_loc = i + stride
        trg_len = end_loc - i
        input_ids = encodings.input_ids[:, begin_loc:end_loc]
        target_ids = input_ids.clone()
        target_ids[:, :-trg_len] = -100

        with torch.no_grad():
            outputs = model(input_ids, labels=target_ids)
            neg_log_likelihood = outputs.loss * trg_len

        nlls.append(neg_log_likelihood)

    ppl = torch.exp(torch.stack(nlls).sum() / end_loc)
    return ppl.item()

text = "The quick brown fox jumps over the lazy dog."
print("Perplexity:", calculate_perplexity(text))

from nltk.translate.bleu_score import sentence_bleu

reference = [['the', 'quick', 'brown', 'fox']]
hypothesis = ['the', 'fast', 'brown', 'fox']
score = sentence_bleu(reference, hypothesis)
print("BLEU Score:", score)

from rouge import Rouge

hypothesis = "The quick brown fox jumps over the lazy dog."
reference = "A fast brown fox leaps over a lazy dog."
rouge = Rouge()
scores = rouge.get_scores(hypothesis, reference)
print("ROUGE Scores:", scores)

def exact_match(prediction, ground_truth):
    return int(prediction == ground_truth)

ai_answer = "42"
correct_answer = "42"
print("Exact Match:", exact_match(ai_answer, correct_answer))

def f1_score(prediction, ground_truth):
    pred_tokens = prediction.split()
    truth_tokens = ground_truth.split()
    
    common = set(pred_tokens) & set(truth_tokens)
    if not common:
        return 0
    
    precision = len(common) / len(pred_tokens)
    recall = len(common) / len(truth_tokens)
    
    return 2 * (precision * recall) / (precision + recall)

ai_answer = "The capital of France is Paris"
correct_answer = "Paris is the capital of France"
print("F1 Score:", f1_score(ai_answer, correct_answer))

def demographic_parity(predictions, protected_attribute):
    groups = set(protected_attribute)
    group_rates = {g: sum(p for p, a in zip(predictions, protected_attribute) if a == g) / sum(a == g for a in protected_attribute) for g in groups}
    return max(group_rates.values()) - min(group_rates.values())

# Example usage
predictions = [1, 0, 1, 1, 0, 1]  # 1 for positive outcome, 0 for negative
protected_attribute = ['A', 'B', 'A', 'B', 'A', 'B']  # A and B are different groups
print(f"Demographic Parity Difference: {demographic_parity(predictions, protected_attribute)}")

def equal_opportunity(predictions, protected_attribute, true_labels):
    groups = set(protected_attribute)
    group_rates = {g: sum(p for p, a, t in zip(predictions, protected_attribute, true_labels) if a == g and t == 1) / sum(a == g and t == 1 for a, t in zip(protected_attribute, true_labels)) for g in groups}
    return max(group_rates.values()) - min(group_rates.values())

# Example usage
predictions = [1, 0, 1, 1, 0, 1]  # 1 for positive outcome, 0 for negative
protected_attribute = ['A', 'B', 'A', 'B', 'A', 'B']  # A and B are different groups
true_labels = [1, 1, 1, 1, 0, 1]  # 1 for actually qualified, 0 for not qualified
print(f"Equal Opportunity Difference: {equal_opportunity(predictions, protected_attribute, true_labels)}")

Comparing LLMs: Methodologies and Tools

The rapid advancement of Large Language Models (LLMs) has necessitated robust methods for their evaluation and comparison. This section explores the techniques and tools used to assess LLM performance, with a focus on comprehensive evaluation frameworks like jiant.

Comparing LLMs involves assessing their performance across various tasks and metrics. Popular models such as GPT-3, BERT, and T5 are often evaluated against each other to determine their strengths and weaknesses in different applications.

Top Methods for Evaluating Large Language Models

Evaluating Large Language Models (LLMs) is crucial for understanding their capabilities, limitations, and potential biases. Here are some of the top methods and tools used in the field:

Automated evaluations offer quick and repeatable assessments, allowing researchers and developers to consistently measure LLM performance across various tasks.

OpenAI's Evals Framework

OpenAI's Evals is a framework designed for systematic LLM evaluation. Key features include:

• Customizable evaluation scripts
• A registry of pre-defined evaluations
• Support for various model types, including OpenAI's models and custom implementations
• Ability to run evaluations on specific capabilities or general language understanding

Evals is particularly useful for those working with OpenAI's models or looking to create standardized evaluations for their own models.

EleutherAI's Language Model Evaluation Harness

EleutherAI's Evaluation Harness is a comprehensive framework for evaluating LLMs across a wide range of tasks. Notable aspects include:

• Support for numerous language models and tokenizers
• A large collection of NLP tasks and benchmarks
• Extensibility for adding custom tasks and models
• Detailed performance reporting and analysis tools

This harness is especially valuable for researchers looking to perform broad, multi-task evaluations of language models.

Hugging Face Datasets and Metrics

Hugging Face provides two key libraries for LLM evaluation:

Datasets Library

• Offers easy access to a vast collection of NLP datasets

• Supports efficient data loading and processing

• Allows for easy sharing and versioning of datasets

Evaluate Library

• Provides a wide range of evaluation metrics for various NLP tasks

• Supports custom metric implementation

• Ensures consistent evaluation across different models and tasks

Additionally, Hugging Face maintains an Open LLM Leaderboard, which showcases performance comparisons of various open-source LLMs using standardized benchmarks. This leaderboard serves as a valuable resource for researchers and practitioners to assess state-of-the-art models.

Stanford's HELM for LLM Evaluation

Stanford University's HELM (Holistic Evaluation of Language Models) offers another robust framework for comprehensive LLM assessment. HELM aims to provide a more nuanced and multifaceted evaluation of language models by considering a wide range of criteria beyond just performance metrics. It evaluates models across various dimensions including accuracy, calibration, robustness, fairness, bias, toxicity, and efficiency.

HELM's evaluation includes tests known as MMLU (Massive Multitask Language Understanding), a benchmark designed to assess language models across a wide range of human knowledge. MMLU covers 57 subjects, from humanities to STEM fields, testing both breadth and depth of understanding.

Building on this approach, HELM incorporates MMLU-style tests across various domains, including medicine. For example, in a medical knowledge assessment, GPT-4 was given a complex clinical scenario: Copy

A 47-year-old man with chest pain, dyslipidemia, hypertension, and diabetes presents with an ECG showing ST-segment elevation. After initial treatment, he collapses and becomes unresponsive. Which of the following is the most likely cause of death?

A) Papillary muscle rupture 
B) Ventricular fibrillation 
C) Septal wall rupture 
D) Pulmonary embolism

GPT-4 correctly identified "Ventricular fibrillation" as the most probable cause, demonstrating its ability to synthesize complex medical information and apply clinical reasoning. This showcases HELM's capacity to assess not just factual recall, but also advanced problem-solving in specialized domains, extending beyond traditional MMLU tasks to evaluate real-world application of knowledge.

These libraries are particularly useful for researchers and practitioners who want to leverage standardized datasets and metrics in their evaluation pipelines.

Putting It All Together

Remember, no single metric tells the whole story. Effective LLM evaluation typically involves a combination of these methods, tailored to the specific use case and requirements of the model. By leveraging automated benchmarks, human evaluation, task-specific assessments, and real-world testing, researchers and practitioners can gain a comprehensive understanding of an LLM's capabilities and limitations.

As the field of LLM development continues to evolve rapidly, staying updated with the latest evaluation techniques and tools is crucial for ensuring the responsible development and deployment of these powerful language models.