LLM-based System Evaluation Framework
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A LLM-based System Evaluation Framework is a AI system evaluation framework to builder LLM-based system evaluation systems.
- Context:
- It can (typically) be used to assess the quality and performance of large language models (LLMs) in specific applications.
- It can (typically) integrate with LLM training workflows to enable **continuous evaluation** as models are updated or fine-tuned.
- It can (often) include evaluation metrics such as **accuracy**, **relevance**, **fairness**, and **robustness** to determine the effectiveness of an LLM system.
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- It can include various LLM-based System Performance Measures such as Perplexity, BLEU, ROUGE, METEOR, Human Evaluation, Diversity, and Zero-shot Evaluation.
- It can simply access with LMM-based System Benchmarks, such as: Big Bench, GLUE Benchmark, SuperGLUE Benchmark, MMLU, LIT, ParlAI, CoQA, LAMBADA, and HellaSwag.
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- Example(s):
- OpenAI Evals Framework: A framework that allows developers to evaluate OpenAI models across various benchmarks, focusing on correctness and relevance in generated outputs.
- LangSmith Evaluation Framework (LangSmith): A framework for testing and benchmarking LLMs, with built-in tools for tracking performance across real-time runs.
- Google's Evaluation for Responsible AI: A framework designed to assess the ethical implications and safety of deploying LLMs in production environments.
- Hugging Face Eval Framework: Tools integrated into the Hugging Face ecosystem for benchmarking language models on a range of tasks such as classification, generation, and translation.
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- Counter-Example(s):
- Traditional ML Evaluation Frameworks that focus on models like decision trees and random forests, which do not have the same complexities in language processing.
- Rule-based System Evaluation frameworks, which evaluate deterministic systems and lack the flexibility needed to assess probabilistic outputs of LLMs.
- Embeddings-Only Evaluation frameworks, such as ones used for word embeddings (e.g., FastText), which do not handle the large context windows and tasks required for modern LLMs.
- See: AI Model Benchmarking, LLM Evaluation Metrics, Human-in-the-Loop Evaluation, AI Fairness Assessment, Large Language Model, Model Evaluation, OpenAI Moderation API, EleutherAI LM Eval,
References
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