LLM Instruction Following Accuracy Measure

A LLM Instruction Following Accuracy Measure is an LLM-related accuracy measure that quantifies a large language model's ability to correctly follow instructions in prompts.

• Context:
  • It can typically evaluate LLM Instruction Adherence through verifiable instruction criteria that can be automatically checked without human judgment.
  • It can typically measure the instruction following capability of large language models through objective assessment methods rather than subjective evaluations.
  • It can typically generate instruction following scores that indicate the percentage of instructions correctly followed by an LLM.
  • It can typically distinguish between strict adherence and loose adherence to assess different levels of instruction compliance.
  • It can typically assess multi-turn instruction following accuracy by tracking adherence across consecutive conversation turns.
  • It can typically evaluate multilingual instruction following capability to measure cross-lingual instruction adherence.
  • ...
  • It can often use benchmark datasets with verifiable instruction types such as word counts, keyword inclusion, formatting requirements, and language constraints.
  • It can often analyze instruction following error patterns to identify common failure modes in LLM instruction processing.
  • It can often compare instruction following performance across different LLM sizes and model architectures.
  • ...
  • It can range from being a Simple LLM Instruction Following Accuracy Measure to being a Complex LLM Instruction Following Accuracy Measure, depending on its assessment methodology.
  • It can range from being a Single-Turn LLM Instruction Following Accuracy Measure to being a Multi-Turn LLM Instruction Following Accuracy Measure, depending on its conversation complexity.
  • It can range from being a Monolingual LLM Instruction Following Accuracy Measure to being a Multilingual LLM Instruction Following Accuracy Measure, depending on its language coverage.
  • ...
• Examples:
  • LLM Instruction Following Accuracy Measure Frameworks, such as:
    • IFEval LLM Instruction Following Accuracy Measure, which assesses LLM instruction following ability using 25 types of verifiable instructions across approximately 500 prompts.
    • DRFR LLM Instruction Following Accuracy Measure, which decomposes complex instructions into simpler criteria for more granular instruction adherence assessment.
    • M-IFEval LLM Instruction Following Accuracy Measure, which extends instruction following evaluation to multiple languages including French, Japanese, and Spanish.
    • Multi-IF LLM Instruction Following Accuracy Measure, which assesses instruction following ability across multi-turn conversations and multiple languages.
  • LLM Instruction Following Accuracy Measure Metrics, such as:
    • Strict Instruction Following Accuracy Metric, which requires complete adherence to all aspects of an instruction.
    • Loose Instruction Following Accuracy Metric, which allows partial adherence to instruction criteria.
    • Prompt-Level Instruction Following Accuracy Metric, which measures the proportion of prompts where all instructions were followed correctly.
    • Instruction-Level Accuracy Metric, which measures the proportion of individual instructions followed correctly across all prompts.
  • LLM Instruction Following Accuracy Measure Test Types, such as:
    • Keyword Inclusion LLM Instruction Following Test, which verifies that specified keywords appear in the LLM output.
    • Word Count LLM Instruction Following Test, which checks if the LLM response contains more or fewer words than specified.
    • Output Format LLM Instruction Following Test, which verifies if the LLM output follows a specified format like JSON or numbered lists.
    • Language Constraint LLM Instruction Following Test, which checks if the LLM response uses or avoids specified language or phrases.
  • ...
• Counter-Examples:
  • LLM Performance Measures, which assess general model capability rather than specific instruction adherence.
  • LLM Output Quality Measures, which evaluate the content quality rather than instruction compliance.
  • Human Evaluation Measures, which rely on subjective judgment rather than verifiable criteria.
  • LLM Factual Accuracy Measures, which focus on factual correctness rather than following instructions.
• See: LLM Evaluation Framework, Instruction Following Capability, LLM Benchmark, Prompt Engineering Metric, IFEval, Instruction Adherence.

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