F ollow B ench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Models (2024)

Abstract: The ability to follow instructions is crucial for Large Language Models (LLMs) to handle various real-world applications. Existing benchmarks primarily focus on evaluating pure response quality, rather than assessing whether the response follows constraints stated in the instruction. To fill this research gap, in this paper, we propose FollowBench, a Multi-level Fine-grained Constraints Following Benchmark for LLMs. FollowBench comprehensively includes five different types (i.e., Content, Situation, Style, Format, and Example) of fine-grained constraints. To enable a precise constraint following estimation on diverse difficulties, we introduce a Multi-level mechanism that incrementally adds a single constraint to the initial instruction at each increased level. To assess whether LLMs’ outputs have satisfied every individual constraint, we propose to prompt strong LLMs with constraint-evolution paths to handle challenging open-ended instructions. By evaluating 13 closed-source and open-source popular LLMs on FollowBench, we highlight the weaknesses of LLMs in instruction following and point towards potential avenues for future work. The data and code are publicly available at https://github.com/YJiangcm/FollowBench.

## introduction large language models (llms) @xcite pre-trained on web-scale corpora have showcased proficiency in generating fluent and realistic text. yet, human instructions in real-life cases require the model to generate text that not only possesses a high degree of naturalness but adheres to specific constraints @xcite . for instance, the model may be required to recommend ten books that are specifically written in chinese (figure 1 ), or it might be expected to generate responses that have a certain tone. the dominant paradigm for assessing if a model can follow instructions involves using human annotators or strongly aligned llms to judge its response quality, in terms of helpfulness, relevance, accuracy, depth, creativity, and level of detail @xcite @xcite . however, prior work still has two limitations. firstly, they ignore the fine-grained constraints inside instructions, which are essential and objective standards for evaluating the instruction-following capability. while several benchmarks have rigorously explored individual constraint types, including semantic restrictions @xcite and complex formatting @xcite , there exists a lack of comprehensive analysis across the diverse spectrum of constraint categories. secondly, few benchmarks consider the varying difficulty of instructions, which is controlled by the number of imposed constraints. this makes it challenging to precisely assess the degree to which llms can follow instructions. towards this end, our research question is: how can we systemically and precisely eval-uate the instruction-following capability of llms? in this paper, we construct followbench, a multi-level fine-grained constraints following benchmark. followbench comprehensively includes five different types of constraints from real-world scenarios, namely content (i.e., explicit restrictions on the response content), situation (i.e., specific situation/background information added to the question), style (i.e., response style requirements), format (i.e., response format requirements), and example (i.e., example pattern recognition and following). to precisely estimate the difficulty degree to which llms can follow instructions, as shown in figure 1 , we propose a novel multi-level mechanism that incrementally adds a single constraint to straightforward instructions at each increased level. the multi-level mechanism enables us to pinpoint the difficulty level at which llms fail to follow instructions, thereby estimating the upper limit of instructionfollowing capability in llms more precisely. overall, followbench consists of 820 meticulously curated instructions from over 50 nlp tasks, including both closed-and open-ended questions. for evaluation purposes, we propose a hybrid evaluation method comprising rule-based and modelbased solutions. given llms' outputs, both solutions judge whether the outputs satisfy each of the constraints in the instructions. the rule-based solutions focus on closed-ended instructions while the model-based solutions are applied to opened-ended instructions. for model-based solutions, instead of merely using current instructions and responses as input, we additionally provide the evolution process of the instructions in the input prompts to llm judges to better understand each individual constraint. both the data construction and the evaluation undergo human verification. in our experiments, we propose three metrics to assess the instruction-following ability of 13 prominent closed-source and open-source llms on followbench. our principal observations are: @xcite the performance of all tested models declines substantially with an increase in difficulty level (the number of constraints in an instruction); ## followbench as shown in to encapsulate, we will introduce the data construction process of followbench, including fine-grained constraints and the multi-level mechanism, in §3.1. in §3.2, we propose an evaluation protocol with three metrics that seamlessly integrate with the multi-level mechanism. ## experiments this section first introduces experimental setup in §4.1, and then presents the main experiment results across two key dimensions: difficulty level in §4.2 and constraint category in §4.3. ## analysis this section includes: an ablation study confirming our prompt template's effectiveness for modelbased evaluation ( §5.1); a comparison of instruction following vs. other llm's abilities ( §5.2); an examination of failure consistency ( §5.3); and an investigation of various decoding strategies ( §5.4). in addition, a case study is presented in appendix c for further analysis. ## conclusion in this paper, we introduce followbench, a multi-level fine-grained constraints following benchmark tailored for gauging the instructionfollowing capability of llms. followbench covers five fine-grained constraint categories and over 50 nlp tasks, utilizes a novel multi-level mechanism for precisely estimating the upper limit of instruction-following capability. furthermore, we propose an evaluation protocol with three metrics that seamlessly integrate with the multi-level mechanism. our extensive tests over 13 popular llms reveal a substantial performance advantage for gpt-4 and gpt-3.5 over their counterparts, and there is still significant room for improving the instruction-following ability of current llms.