llm-evaluation

To evaluate LLM performance, this framework computes automated metrics like BLEU, ROUGE, METEOR, and BERTScore for text generation, classification, and retrieval tasks. It provides a library-based evaluation harness to systematically compare multiple models and prompts.

Yes, you can integrate LLM benchmarking into CI/CD workflows. The framework provides reproducible evaluation pipelines and scripts that automate model assessment, ensuring reliable performance checks during application deployment.

The best way to compare chat models is using the framework's benchmarking capabilities alongside LLM-as-Judge and human evaluation templates. This captures qualitative aspects like accuracy and relevance, enabling systematic comparisons for customer support use cases.

Yes, the tool supports human feedback and LLM-as-Judge. It integrates human judgments and LLM-powered judging to capture qualitative aspects of performance, providing human-evaluation templates alongside automated metrics for comprehensive assessment.

You need nltk, rouge_score, bert_score, transformers, detoxify, and openai dependencies to run automated NLP benchmarks. These libraries power the metric calculations, model integrations, and toxicity assessments within the evaluation harness.

To start benchmarking, run the evaluate-model.py script with an example dataset. This initiates the automated evaluation process, computing metrics like perplexity and BERTScore to generate reproducible performance results.