Evaluating A.I.

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Benchmarking and testing the model against smaller proof datasets to assume its overall performance. A derivative of this was the use of an LLM to grade an LLM.

  1. HumanEval is an evaluation tool for measuring the performance of LLMs in code generation tasks.
  2. MBPP benchmark is designed to measure the ability of LLM to synthesize short Python programs from natural language descriptions.
  3. MT-bench is a challenging multi-turn question set designed to evaluate the conversational and instruction-following ability of models.
  4. AI2 Reasoning Challenge (ARC) is a more demanding “knowledge and reasoning” test, requiring more powerful knowledge and reasoning than previous challenges such as SQuAD or SNLI.
  5. HellaSwag benchmark is used to test the commonsense reasoning understanding about physical situations by testing if language model could complete the sentence by choosing the correct option with common reasoning among 4 options.
  6. Adversarial Filtering (AF) is a data collection paradigm used to create the HellaSwag dataset.
  7. MMLU benchmark measures the model’s multitask accuracy. The test covers 57 tasks including elementary mathematics, computer science, law, and more at varying depths, from elementary to advanced professional level.
  8. TriviaQA benchmark measures whether a language model is truthful in generating answers to questions.

  1. HELM - https://github.com/stanford-crfm/helm - Holistic Evaluation of Language Models
  2. BIG-bench - https://github.com/google/BIG-bench - Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
  3. BigBIO - https://github.com/bigscience-workshop/biomedical - A Framework for Data-Centric Biomedical Natural Language Processing
  4. BigScience Evaluation - https://github.com/bigscience-workshop/evaluation
  5. Language Model Evaluation Harness - https://github.com/EleutherAI/lm-evaluation-harness - Evaluating Large Language Models (LLMs) with Eleuther AI Evaluating LLMs
  6. Code Generation LM Evaluation Harness - https://github.com/bigcode-project/bigcode-evaluation-harness
  7. Chatbot Arena - https://github.com/lm-sys/FastChat
  8. GLUE - https://github.com/nyu-mll/jiant
  9. SuperGLUE - https://github.com/nyu-mll/jiant
  10. CLUE - https://github.com/CLUEbenchmark/CLUE
  11. CodeXGLUE - https://github.com/microsoft/CodeXGLUE
  12. LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models. - https://github.com/FreedomIntelligence/LLMZoo

Evaluating the Planning of the LLM

  1. Pandas-Profiling: Generates extensive descriptive statistics and visualizations to uncover data quality issues, distributions, correlations, and potential biases.
  2. DVC (Data Version Control): Tracks changes in data and model code, enabling reproducibility and comparison of different experiments.
  3. TensorBoard (for TensorFlow): Visualizes model training metrics, hyperparameter tuning, and model architecture.
  4. Weights & Biases: Tracks experiments, visualizes results, and integrates with model registries for version control.
  5. Neptune.ai: Offers experiment tracking, model registry, and collaboration features for ML teams.
  6. MLflow: Open-source platform for tracking experiments, managing models, and deploying to production.
  7. GuildAI: Open-source tool for experiment tracking, hyperparameter optimization, and model deployment.

  1. Great Expectations: Sets up data quality checks and alerts for data pipelines.
  2. Deequ: Library for defining and testing data quality constraints for large-scale datasets.

  1. Comet ML: Experiment tracking and visualization platform with a focus on deep learning.
  2. ClearML: Experiment management and optimization platform with built-in hyperparameter tuning and model deployment capabilities.
  3. EvalML: Automatic machine learning tool that includes data quality checks and model evaluation metrics.

Benchmarking LLM's

  • MMLU-PRO Raw: This benchmark is designed to be the most comprehensive and challenging, requiring LLMs to demonstrate a deep understanding of language, reasoning, and logic across a wide range of tasks.
  • MMLU-Pro: This benchmark is an enhanced version of the MMLU benchmark, designed to be more robust and challenging. It evaluates LLMs on a wide range of tasks, including language comprehension, reasoning, and problem-solving.
  • SuperGLUE: This benchmark is an updated and more challenging version of the GLUE benchmark, designed to push the boundaries of language understanding and reasoning.
  • ARC (Aristo Reasoning Challenge): This benchmark tests the ability of language models to answer complex, multi-step science questions that require reasoning, common sense, and factual knowledge.
  • BBH Raw: This benchmark is designed to be a more comprehensive and challenging version of the BBH benchmark, requiring LLMs to demonstrate multi-step reasoning and problem-solving skills.
  • BBH: This benchmark consists of 23 challenging tasks that are designed to test the limits of language models, requiring multi-step reasoning and problem-solving skills.
  • MuSR Raw: This benchmark is designed to evaluate the performance of LLMs on multi-step reasoning tasks, requiring a deeper understanding of language, reasoning, and logic.
  • MuSR: This benchmark evaluates the performance of LLMs on multi-step reasoning tasks, requiring a deep understanding of language and reasoning.
  • MATH Lvl 5 Raw: This benchmark is a more challenging version of the MATH Lvl 5 benchmark, requiring LLMs to demonstrate a deeper understanding of mathematical concepts and problem-solving skills.
  • MATH Lvl 5: This benchmark consists of 12,500 challenging competition mathematics problems, requiring LLMs to demonstrate a deep understanding of mathematical concepts and problem-solving skills.
  • GPQA Raw: This benchmark is a more challenging version of the GPQA benchmark, requiring LLMs to answer questions without any additional context or information.
  • GPQA: This benchmark evaluates the question-answering abilities of LLMs, requiring a deep understanding of language and reasoning.
  • RACE: This benchmark tests the ability of language models to comprehend and answer questions based on passages of text, similar to standardized reading comprehension tests.
  • GLUE: This benchmark evaluates the natural language understanding capabilities of language models across a diverse set of tasks, including sentiment analysis, textual entailment, and linguistic acceptability.
  • IFEval Raw: This benchmark is a variant of the IFEval benchmark, emphasizing contextual understanding, implicit meaning, and raw text comprehension.
  • IFEval: This benchmark evaluates the inductive reasoning, logical operations, and common sense abilities of LLMs.
  • ANLI - (Adversarial Natural Language Inference): A challenging benchmark focusing on adversarial reasoning and logic, pushing models to deal with tricky, ambiguous cases.
  • LAMBADA - Tests the ability to complete sentences with complex, coherent context, testing longer-range contextual understanding.
  

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