Mastering LLM Evaluation with PromptBench

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Introduction

As large language models (LLMs) continue to evolve, assessing their performance across diverse datasets and tasks has become increasingly important. PromptBench is a unified library designed to evaluate and understand these models effectively. In this guide, we will explore how to set up and use PromptBench, understand its core functionalities, and learn how to assess model performance efficiently.

By the end of this tutorial, you will:

Understand how to install and set up PromptBench.
Learn to load datasets and models.
Explore different evaluation methods.
Gain insights into adversarial prompt engineering and dynamic evaluations.
Learn how to interpret results for better decision-making.

Setting Up PromptBench

Installation

To begin, install the promptbench library using pip:

!pip install promptbench

Setting Up API Keys

Before you can use OpenAI models or other APIs, set up your API key:

import os
from google.colab import userdata

os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY')

Why is this necessary? API keys allow access to LLMs like OpenAI’s GPT-4 and Google’s PaLM. Ensure you have the right API credentials before proceeding.

Loading Datasets

PromptBench supports a variety of datasets for benchmarking. To list all available datasets, use:

import promptbench as pb

print('All supported datasets: ')
print(pb.SUPPORTED_DATASETS)

Loading a Specific Dataset

For example, to load the SST-2 sentiment analysis dataset:

dataset = pb.DatasetLoader.load_dataset("sst2")

Other available datasets include:

MMLU (Massive Multitask Language Understanding)
Math (Algebra, Logic, etc.)
IWSLT 2017 (Machine Translation)

To check the first five entries of the dataset:

dataset[:5]

Loading Language Models

To see all available models in PromptBench:

print('All supported models: ')
print(pb.SUPPORTED_MODELS)

Loading a Specific Model

For instance, to load FLAN-T5 Large, use:

model = pb.LLMModel(model='google/flan-t5-large', max_new_tokens=10, temperature=0.0001, device='cuda')

Other supported models include:

GPT-3.5-Turbo
GPT-4, GPT-4-Turbo
LLaMA 2 variants
Vicuna, Mistral, Mixtral

If using OpenAI’s GPT models, ensure your API key is set:

model = pb.LLMModel(model='gpt-3.5-turbo', openai_key=userdata.get("OPENAI_API_KEY"), max_new_tokens=200)

Constructing Prompts

PromptBench allows multiple prompts for evaluation. Example:

prompts = pb.Prompt([
    "Classify the sentence as positive or negative: {content}",
    "Determine the emotion of the following sentence as positive or negative: {content}"
])

Why is this useful?

Helps test different phrasings of a prompt.
Assesses model robustness to minor prompt variations.

Performing Evaluations

Defining a Label Mapping Function

Since model predictions are textual, we map outputs to numerical labels:

def proj_func(pred):
    mapping = {
        "positive": 1,
        "negative": 0
    }
    return mapping.get(pred, -1)

Running the Evaluation Loop

from tqdm import tqdm

for prompt in prompts:
    preds = []
    labels = []
    
    for data in tqdm(dataset):
        input_text = pb.InputProcess.basic_format(prompt, data)
        label = data['label']
        raw_pred = model(input_text)
        pred = pb.OutputProcess.cls(raw_pred, proj_func)
        
        preds.append(pred)
        labels.append(label)

    # Compute accuracy
    score = pb.Eval.compute_cls_accuracy(preds, labels)
    print(f"{score:.3f}, {prompt}")

Expected Output:

100%|██████████| 872/872 [01:33<00:00, 9.36it/s]
0.947, Classify the sentence as positive or negative: {content}
100%|██████████| 872/872 [01:24<00:00, 10.33it/s]
0.947, Determine the emotion of the following sentence as positive or negative: {content}

Key Takeaways:

Accuracy is computed across different prompts.
Model responses are evaluated for consistency.
Minor prompt changes can impact model accuracy.

Evaluating Adversarial Prompts

PromptBench supports black-box adversarial prompt attacks:

from promptbench.adversarial import PromptAttacks
attacker = PromptAttacks(model=model)
attacked_prompt = attacker.attack(prompt, max_attempts=5)

This helps test model robustness against manipulative prompts.

Using Dynamic Evaluation

To mitigate test data contamination, we use DyVal:

from promptbench.dynamic_eval import DyVal

dyval_evaluator = DyVal(model=model)
dynamic_samples = dyval_evaluator.generate_samples(dataset, complexity=2)

This ensures generated test samples remain unbiased and fresh.

Conclusion

PromptBench simplifies benchmarking LLMs across datasets.
Provides tools for adversarial testing and dynamic evaluation.
Supports multiple models, including GPT, LLaMA, Vicuna, and FLAN-T5.
Helps analyze how prompt engineering affects model responses.

Next Steps

Experiment with different datasets and models.
Try adversarial prompts to stress-test models.
Explore dynamic evaluation to reduce bias.

Resources

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