The SIFo Benchmark: Investigating the Sequential Instruction Following
  Ability of Large Language Models
Authors: name: Xinyi Chen, name: Baohao Liao, name: Jirui Qi, name: Panagiotis Eustratiadis, name: Christof Monz, name: Arianna Bisazza, name: Maarten de Rijke

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 rule
following), 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.