Large Language Model (LLM) Inference Evaluation Task

A Large Language Model (LLM) Inference Evaluation Task is a benchmarking task that can be used to evaluate the performance of a LLM inference system based on its output quality, robustness, and other dimensions.

• AKA: LLM Evaluation, LLM Benchmarking Task, LLM Output Evaluation.
• Context:
  • Task Input: Prompt (text or structured query).
  • Optional Input: Contextual history, system instructions, or grounding documents
  • Task Output: Generated text or prediction from the LLM
  • Task Performance Measure: Automatic metrics (e.g., BLEU, ROUGE, BERTScore, Exact Match), human preference, latency, or hallucination rate.
  • It can assess the ability of a large language model to generate accurate, coherent, and relevant outputs in response to prompts.
  • It can evaluate performance based on single-turn or multi-turn dialogue, factual consistency, and instruction following.
  • It can use both automatic metrics (e.g., BLEU, ROUGE, BERTScore) and human-annotated preference ratings.
  • It can include adversarial or hallucination-prone inputs to test truthfulness and reliability.
  • It can be conducted across multilingual, multi-domain, or zero-shot settings.
  • It can range from focused benchmark tasks with fixed metrics to holistic evaluations across dimensions like fairness, toxicity, and robustness.
  • ...
• Example(s):
  • HELM (Holistic Evaluation of Language Models), which evaluates LLM inference in realistic, diverse settings across multiple axes.
    • Input: Multilingual and multi-domain prompts
    • Output: Model completions
    • Metrics: Accuracy, calibration, robustness, fairness, toxicity
  • MMLU (Massive Multitask Language Understanding), which tests inference across academic disciplines and focuses on zero-shot or few-shot inference ability on challenging datasets.
    • Input: Academic and professional QA prompts;
    • Output: Multiple-choice answers;
    • Metrics: Accuracy.
  • MT-Bench (from LMSYS), which uses model-generated preference voting to compare response quality. It evaluates comparative dialogue generation using model-graded pairwise evaluation.
    • Input: Instruction-following queries;
    • Output: Model responses;
    • Metrics: GPT-4-voted preference scoring.
  • TruthfulQA, which measures factual accuracy under deceptive or misleading questions. It assesses output quality in inference against known facts.
    • Input: Questions that test susceptibility to hallucination;
    • Output: Text answers
    • Metrics: Truthfulness, informativeness
  • ...
• Counter-Example(s):
  • LLM Pretraining Tasks, which focus on training efficiency or data coverage rather than evaluating inference output.
  • MLPerf Inference Benchmark, which evaluates computational performance but not linguistic quality.
  • Annotation Agreement Tasks, which measure human labeler consistency, not model performance.
  • Data Generation Pipelines like Self-Instruct, which focus on dataset construction rather than model evaluation.
  • ...
• See: LLM Inference Task, Natural Language Processing Task, Machine Learning Inference, Model Optimization Task, Large Language Model Configuration Parameter, Machine Translation Task, Content Generation Task.

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References

2025

• (Lin et al., 2025) ⇒ Lin, S., Hilton, J., & Evans, O. (2025). "TruthfulQA: Measuring How Models Imitate Human Falsehoods". In: GitHub.
  • QUOTE: TruthfulQA consists of two tasks that use the same sets of questions and reference answers.

    The primary objective is overall truthfulness, expressed as the percentage of the models' answers that are true.

    Secondary objectives include the percentage of answers that are informative.

2024

• (Stanford CRFM, 2024) ⇒ Stanford CRFM. (2024). "Holistic Evaluation of Language Models (HELM)". In: Stanford CRFM.
  • QUOTE: HELM benchmarks 30 prominent language models across a wide range of scenarios and metrics to elucidate their capabilities and risks.

    It aims to provide transparency for the AI community, addressing societal considerations such as fairness, robustness, and the capability to generate disinformation.

2023a

• (HuggingFaceH4, 2023) ⇒ HuggingFaceH4. (2023). "MT Bench Prompts". In: Hugging Face.
  • QUOTE: The MT Bench dataset is created for better evaluation of chat models, featuring evaluation prompts designed by the LMSYS organization.

    The dataset supports tasks such as prompt evaluation and benchmarking.

2023b

• (MLCommons, 2023) ⇒ MLCommons. (2023). "MLCommons Inference Datacenter v3.1". In: MLCommons.
  • QUOTE: The MLCommons benchmark suite includes performance metrics for various tasks such as image classification, object detection, and LLM summarization.

    It demonstrates high-efficiency inference across diverse hardware platforms.

2023c

• (Chen, Zaharia and Zou, 2023) ⇒ Lingjiao Chen, Matei Zaharia, and James Zou. (2023). “How is ChatGPT's Behavior Changing over Time?.” In: arXiv preprint arXiv:2307.09009. doi:10.48550/arXiv.2307.09009

2022

• (Hendrycks et al., 2022) ⇒ Hendrycks, D., et al. (2022). "Massive Multitask Test". In: GitHub.
  • QUOTE: The Massive Multitask Test evaluates models across 57 tasks spanning multiple domains such as elementary mathematics, US history, computer science, and law.

    It provides a comprehensive benchmark for assessing general knowledge capabilities.

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