LexInstructEval: Lexical Instruction Following Evaluation for Large Language Models

• URL: http://arxiv.org/abs/2511.17561v1
• Date: Thu, 13 Nov 2025 08:04:30 GMT
• Title: LexInstructEval: Lexical Instruction Following Evaluation for Large Language Models
• Authors: Huimin Ren, Yan Liang, Baiqiao Su, Chaobo Sun, Hengtong Lu, Kaike Zhang, Chen Wei,
• Abstract summary: LexInstructEval is a new benchmark and evaluation framework for fine-grained lexical instruction following.<n>Our framework is built upon a formal, rule-based grammar that deconstructs complex instructions into a canonical Procedure, Relation, Value> triplet.<n>This grammar enables the systematic generation of a diverse dataset through a multi-stage, human-in-the-loop pipeline.
• Score: 7.921987175359344
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability of Large Language Models (LLMs) to precisely follow complex and fine-grained lexical instructions is a cornerstone of their utility and controllability. However, evaluating this capability remains a significant challenge. Current methods either rely on subjective and costly human evaluation or on automated LLM-as-a-judge systems, which suffer from inherent biases and unreliability. Existing programmatic benchmarks, while objective, often lack the expressiveness to test intricate, compositional constraints at a granular level. To address these limitations, we introduce LexInstructEval, a new benchmark and evaluation framework for fine-grained lexical instruction following. Our framework is built upon a formal, rule-based grammar that deconstructs complex instructions into a canonical <Procedure, Relation, Value> triplet. This grammar enables the systematic generation of a diverse dataset through a multi-stage, human-in-the-loop pipeline and facilitates objective verification via a transparent, programmatic engine. We release our dataset and open-source evaluation tools to facilitate further research into the controllability and reliability of LLMs.

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