The SIF o Benchmark: Investigating the Sequential Instruction Following Ability of Large Language Models (2024)

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 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.

## introduction recent advances in training large language models (llms) to follow instructions have significantly improved their ability to comprehend open-ended language commands, encompassing a wide range of needs, preferences, and values @xcite . despite these improvements, it is still an open question whether and to what extent llms, typically trained with single task instructions, can perform complex tasks where following a sequence of instructions is required to achieve the desired outcome. current evaluation resources for instruction following mainly focus on single instruction following setups @xcite @xcite with some attempts to diagnose the ability to process cases with more than two task instructions in the same prompt. however, the instruction sequences in these benchmarks often lack coherence. a typical example is the task of "first translating a context and then answering a question based on the context" @xcite . though the two tasks are relevant, successful completion of one task does not necessarily depend on success at the other task, and the order of the instructions can be shuffled. moreover, due to positional bias in llms @xcite , the performance of following an instruction may be affected by the position it has in the sequence of instructions. in addition, the difficulty levels and evaluation metrics for the two tasks are different, making it even more challenging to evaluate how well a model can follow the sequence as a whole. when extending the llm evaluation methodology from single to multiple instruction following evaluation, we face three main challenges: (i) limited coherence between multiple instruction tasks; @xcite the influence of positional bias, where the order of the instructions affect model performance; and (iii) the lack of objectively verifiable tasks in instruction following evaluation that can be easily adapted to address the above two challenges. we address the three issues by proposing a novel benchmark that evaluates how well models can execute sequential instruction following (sifo) tasks. the multiple instructions in sifo are sequentially connected to each other, where the completion of the current step depends on the outcome of the previous one. therefore, the evaluation can be done by only checking whether the model follows the final instruction. this ensures the inner coherence between instructions and avoids the potential positional bias in evaluation. for a comprehensive evaluation, we propose four objectively verifiable tasks: (i) text modification, (ii) question answering, (iii) mathematics, and (iv) security instructions instruction 1: [...] insert the word "machine" before all occurrences of the word "language". [...] what would the context be like? instruction 2: [...] replace all occurrences of the word "model" with the word "bird". @xcite what would the context be like? instruction 3: [...] delete all occurrences of the word "achieve". @xcite what would the context be like? ## a preliminary experiment as described in section 2.3, llms often exhibit positional bias. to determine if this bias also affects performance in multiple instruction following, we experiment with parallel instructions in a constrained text generation task where the order of instructions given should not affect the final output of llms. an example of such a task is "write a passage about a school day that meets all the constrains in the following instructions. instruction 1: use less than 200 words; instruction 2: do not use the word 'today' in the passage . . . ". we combine the instruction tasks from @xcite to create such a dataset. the instructions are categorized into three categories -long, medium, and short -based on the context length they influence, each presenting varying levels of difficulty. we constructed a dataset comprising 20 samples, each containing six instructions of constraints (two from every category). we permute the order of the six instructions to generate multiple datasets with identical tasks but different orders of the constraint instructions, resulting in 20 × 6! samples. details of the dataset construction are provided in appendix a. whether the generated output meets all the constraints in an instruction can be verified with the pipeline provided by @xcite . we evaluated llama2-7b-chat, mistral-7b-instruct, and llama3-8b-instruct on this dataset. the results, displayed in figure 2 , indicate that models' performance on following constraints differs when the instructions are given in different orders. the patterns behind such position bias (e.g., whether placing an instruction in the first position or the third position is better) are different among models. furthermore, even for the same model such patterns vary among different types of instruction. in conclusion: when evaluating with multiple parallel instructions, the order of the instructionstailored to fit a model's properties and the type of instruction -influences model performance, beyond the model's intrinsic ability to follow instructions. ## the sifo benchmark to address the challenges mentioned in previous sections, we propose the sequential instruction following (sifo) benchmark. to perform well on this v0.2. we set the temperature to 0 to make llm generations more deterministic. benchmark, the model must follow multiple instructions step by step to reach the final desired outcome. this setup makes the instructions sequentially connected and mitigates the positional bias influence as all instructions must be executed in the given order. the sifo benchmark contains four commonly seen tasks for llm evaluation: text modification, question answering, mathematics, and security rule following. each focuses on evaluating sequential instruction following from a different angle. all tasks are objectively verifiable. below, we describe the task setup and the dataset construction pipeline. examples of each task can be found in figure 1 . ## experiments we perform experiments with two goals: (i) to explore to what extent llms can follow multi-step sequential instructions; and (ii) to investigate the effectiveness of the sifo benchmark. ## conclusion we have introduced sifo: a benchmark aimed at investigating how well instruction-tuned llms can follow sequential instructions. sifo addresses three main challenges in multiple instruction following evaluation, namely tasks with limited coherence, the influence of positional bias of llms, and the lack of objectively verifiable tasks. a sequential instruction setup is proposed to address these challenges and to evaluate the multiple instruction following abilities of llms in a fairer way than previous attempts. we have shown that sifo is an effective evaluation benchmark to distinguish models' abilities in following multiple sequential instructions. we have highlighted that current llms, even powerful models like gpt-4 and claude-3, are not robust enough to perform sequential instruction following tasks. for future work, we recommend adding more tasks to the sifo benchmark that address further aspects of sequential instruction following, especially cases where each instruction within a sequence corresponds to different evaluation criteria.