spark-llm-eval is a distributed evaluation framework tailored for Apache Spark. Unlike traditional tools, it handles millions of examples, offering advanced statistical measures and seamless integration with data technologies. Ideal for enterprises seeking scalability and accuracy in their LLM evaluations.
Distributed LLM Evaluation Framework for Apache Spark
The spark-llm-eval repository offers a comprehensive evaluation framework for large language models (LLMs) designed specifically for Apache Spark, enabling the handling of evaluation tasks at scale. Unlike conventional evaluation tools that are limited to single-machine execution, this framework is optimized for processing millions of examples, such as customer support tickets or vast document collections.
| Feature | Traditional Tools | spark-llm-eval |
|---|---|---|
| Scale | ~10K examples | Millions of examples |
| Statistical Rigor | Point estimates | Confidence intervals, significance tests |
| Reproducibility | Manual tracking | Delta Lake versioning + MLflow |
| Cost Efficiency | Naive API calls | Caching, batching, rate limiting |
| Enterprise Readiness | Limited | Unity Catalog, governance, audit |
The framework includes features such as:
Initializing an evaluation task involves setting up a Spark session, loading your evaluation dataset, configuring your model, defining the evaluation task, and executing the evaluation as follows:
from pyspark.sql import SparkSession
from spark_llm_eval.core.config import ModelConfig, ModelProvider, MetricConfig, StatisticsConfig
from spark_llm_eval.core.task import EvalTask
from spark_llm_eval.orchestrator import EvaluationRunner, RunnerConfig
from spark_llm_eval.datasets import load_dataset
# Initialize Spark
spark = SparkSession.builder.appName("llm-eval").getOrCreate()
# Load evaluation dataset
data = load_dataset(
spark,
table_path="/mnt/delta/datasets/qa_test",
input_column="question",
reference_column="answer",
)
# Configure model
model_config = ModelConfig(
provider=ModelProvider.OPENAI,
model_name="gpt-4o",
api_key_secret="secrets/openai_key",
)
# Define evaluation task
task = EvalTask(...)
# Configure and execute runner
runner = EvaluationRunner(spark, runner_config)
result = runner.run(data, task)
Lexical Metrics include exact match, token-level F1 score, BLEU, ROUGE-L, substring containment check, and response length ratio. Semantic Metrics feature BERTScore and cosine similarity for sentence embeddings, while LLM-as-judge metrics provide customizable evaluation options.
Included statistical features ensure that all metrics are accompanied by confidence intervals, standard error, and sample sizes. Model comparisons utilize paired t-tests and other methodologies for robust statistical analysis, allowing for insightful evaluations of different models.
This framework aims to redefine LLM evaluation by combining scalability, statistical rigor, and comprehensive tracking capabilities, making it an essential tool for practitioners and researchers in the field.
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