The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Models

• URL: http://arxiv.org/abs/2406.19999v2
• Date: Thu, 03 Oct 2024 13:02:11 GMT
• Title: The SIFo Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Models
• Authors: Xinyi Chen, Baohao Liao, Jirui Qi, Panagiotis Eustratiadis, Christof Monz, Arianna Bisazza, Maarten de Rijke,
• Abstract summary: We introduce a benchmark designed to evaluate models' abilities to follow multiple instructions through sequential instruction following tasks. Our benchmark evaluates instruction following using four tasks (text modification, question answering, mathematics, and security rules) More recent and larger models significantly outperform their older and smaller counterparts on the SIFo tasks, validating the benchmark's effectiveness.
• Score: 48.455388608863785
• License:
• Abstract: Following multiple instructions is a crucial ability for large language models (LLMs). Evaluating this ability comes with significant challenges: (i) limited coherence between multiple instructions, (ii) positional bias where the order of instructions affects model performance, and (iii) a lack of objectively verifiable tasks. To address these issues, we introduce a benchmark designed to evaluate models' abilities to follow multiple instructions through sequential instruction following (SIFo) tasks. In SIFo, the successful completion of multiple instructions is verifiable by examining only the final instruction. Our benchmark evaluates instruction following using four tasks (text modification, question answering, mathematics, and security rules), each assessing different aspects of sequential instruction following. Our evaluation of popular LLMs, both closed-source and open-source, shows that more recent and larger models significantly outperform their older and smaller counterparts on the SIFo tasks, validating the benchmark's effectiveness. All models struggle with following sequences of instructions, hinting at an important lack of robustness of today's language models.

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