Instruction-following benchmarks have become a cornerstone for evaluating the capabilities of large language models (LLMs) in recent years. As the field has shifted from narrow task-specific NLP systems to general-purpose foundation models, the ability of these models to interpret and execute complex natural language instructions has emerged as a critical metric. Benchmarks in this category test how well a model understands prompts, maintains context in multi-turn conversations, and produces outputs that are helpful, safe, and aligned with user intent. Unlike traditional benchmarks focused purely on accuracy, instruction-following evaluations often require a combination of linguistic understanding, reasoning, and alignment. Among the most prominent benchmarks in this space is MT-Bench (Model Test Bench), developed by LMSYS. MT-Bench comprises multi-turn questions across diverse domains and uses both human and LLM-as-a-judge scoring to assess models on coherence, helpfulness, and...
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