I n F o B ench: Evaluating Instruction Following Ability in Large Language Models (2024)

Abstract: This paper introduces the Decomposed Requirements Following Ratio (DRFR), a new metric for evaluating Large Language Models’ (LLMs) ability to follow instructions. Addressing a gap in current methodologies, DRFR breaks down complex instructions into simpler criteria, facilitating a detailed analysis of LLMs’ compliance with various aspects of tasks. Alongside this metric, we present InFoBench, a benchmark comprising 500 diverse instructions and 2,250 decomposed questions across multiple constraint categories. Our experiments compare DRFR with traditional scoring methods and explore annotation sources, including human experts, crowd-sourced workers, and GPT-4. The findings demonstrate DRFR’s higher reliability and the effectiveness of using GPT-4 as a cost-efficient annotator. The evaluation of several advanced LLMs using this framework reveals their strengths and areas needing improvement, particularly in complex instruction-following. This study contributes a novel metric and benchmark, offering insights for future LLM development and evaluation.

## introduction the rapidly evolving domain of natural language processing (nlp) has witnessed the advent and rise of large language models (llms) @xcite @xcite @xcite @xcite , which exhibit remarkable prowess in simulating human-like text generation. their ability to follow instructions is of utmost importance for their practical application, as it customizes the outputs according to specific user needs. despite this, evaluation of these models frequently emphasizes general natural language tasks @xcite @xcite @xcite , or specific downstream tasks @xcite , while the critical element of instructionfollowing-the model's capacity to accurately understand and execute user instructions-has not been thoroughly explored. this evident gap in research and the absence of systematic evaluation methods dedicated to this crucial aspect serve as the impetus for our present study. we aim to establish a reliable protocol and benchmark for appraising the instruction-following aptitude of llms. current research tends to utilize evaluation methodologies such as a/b testing @xcite @xcite @xcite , overall scoring @xcite , and elo rating @xcite @xcite -a derivative of a/b testing. while these strategies have proven somewhat effective, they suffer from considerable drawbacks, including scalability issues and a lack of interpretability. a/b testing, for instance, necessitates n 2 comparisons for n systems. moreover, the overall scoring system is opaque and difficult to interpret, failing to elucidate the reasoning behind the assignment of specific scores to particular instructions. in this study, we propose a novel metric for assessing the instruction-following capability of llms, namely the decomposed requirements following ratio (drfr). unlike traditional metrics, drfr decomposes each instruction into simpler, distinct criteria, allowing for a more detailed and precise assessment of an llm's ability to follow specific aspects of a given task. this decomposition enables a granular evaluation, where the compliance of the llm is measured against each individual requirement within an instruction, offering a clearer picture of the model's performance in complex instruction-following scenarios. in addition, we constructed a benchmark composed of 500 distinct instructions and provided 2,250 decomposed questions for the assessment of instructionfollowing. to facilitate a more fine-grained analysis, we classified the constraints present in each instruction into five categories: content, linguis-tic, style, format, and number. we also manually curated constraint labels for each decomposed question to identify and analyze potential areas of deficiency in each model. we conducted two key experiments: firstly, we compared the drfr metric with traditional direct scoring (ds) for evaluating responses from various large language models (llms). the results showed a higher consensus among annotators with drfr, particularly in hard set, suggesting its greater reliability over direct scoring @xcite . secondly, we explored more cost-efficient annotation sources by comparing human experts, crowd-sourced workers from amazon mechanical turk (amt), and gpt-4. the findings revealed that while human experts were the most accurate, they were also costly and time-consuming. amt workers presented a balance but with lower accuracy. significantly, gpt-4 emerged as a highly accurate, cost-effective, and time-efficient alternative, especially when employing a structured, multi-turn prompt approach, making it a viable option for large-scale annotations. we thus employ gpt-4 as an annotator to evaluate the entire infobench. the comprehensive evaluation of these advanced llms reveals that while progress has been significant, there is still a notable gap in their ability to follow instructions perfectly, particularly in more complex scenarios represented by the "hard set". the closed-source models are currently leading, possibly due to better data or more sophisticated algorithms. however, the performance across different constraint types and domains suggests that the challenges in instruction-following are more nuanced and may require focused improvements in areas like numerical and linguistic understanding. as llms continue to evolve, addressing these specific areas could lead to more robust and versatile large language models capable of handling a broader range of real-world tasks and instructions. our study presents four key contributions to the field of evaluating large language models (llm) in their ability to follow instructions. firstly, we introduce a novel and scalable metric, the decomposed requirements following ratio (drfr), which provides a reliable, detailed, and interpretable framework for assessing llms. this metric decomposes complex instructions into simpler, specific criteria, allowing for a granular analysis of a model's performance. secondly, we have developed infobench, a comprehensive benchmark dataset. it includes 500 diverse instructions paired with 2,250 decomposed questions, designed to test and analyze the instruction-following capabilities of llms systematically. thirdly, through extensive experimentation, we established the efficacy of drfr and infobench. our findings highlight the potential of using gpt-4 as an evaluator for its accuracy and cost-effectiveness, particularly when employing structured, multi-turn prompts. finally, we utilized our automatic evaluation toolkit to conduct a thorough analysis of six advanced llms. this evaluation not only underscores the current capabilities of these models but also sheds light on specific areas that require improvement for more nuanced and effective instruction-following, especially in complex scenarios. these contributions pave the way for future advancements in llm development and evaluation. ## infobench both evaluation metrics and benchmark datasets play an integral role in the objective assessment of large language models' (llm) ability to follow instructions. these tools are essential for enabling consistent comparisons, pinpointing areas for improvement, and ensuring the practicality, fairness, and effectiveness of these models in realworld applications. in section 2.1, we introduce a novel evaluation metric, termed the decomposed requirements following ratio (drfr). this metric is specifically designed to measure the proficiency of llms in adhering to complex instructions in a detailed and structured manner. furthermore, section 2.2 discusses the development of a corresponding benchmark dataset, infobench, which has been meticulously curated to rigorously test and evaluate the instruction-following capabilities of llms. this dataset presents a diverse range of scenarios and challenges, offering a comprehensive tool for benchmarking and refining the performance of these models. ## automatic evaluation in light of the findings from our previous experiments, it has become evident that gpt-4 represents a superior alternative for expert annotations. this approach not only offers a high degree of accuracy but also ensures time efficiency and costeffectiveness in annotation processes. building on this insight, we integrate the gpt-4-0314 with the multi-turn prompt for scaling up the evaluation across the entire infobench with six of the most cutting-edge llms. these include: gpt-3.5-turbo-1106 @xcite and gpt4-1106-preview (openai, 2023a) released by ope-nai, claude-2.1 (anthropic, 2023) by anthropic, gemini-pro @xcite by google, llama-2-70bchat @xcite by meta and vicuna-13b-v1.5 @xcite by lmsys. to avoid randomness during the decoding process, we use greedy decoding 9 for all the models. constraint type: to assess the influence of constraint type on adherence to instructions, we calculate the evaluation scores for each constraint type individually, as presented in figure 3 . the radar plot reveals a discernible pattern in performance across different constraint types: performance is highest on content and style, intermediate on format, and lowest on number and linguistic constraints. this general trend is observed across all six language models. domain: to investigate the influence of the instruction domains on the behavior of instructionfollowing, we restrict our analysis to the seven principal subject categories within the hard set, and independently calculate the evaluation scores for each instruction category, as depicted in figure 4 . contrary to the pattern discernible in the preceding section, the resulting shape of the evaluation scores on the radar plot approximates a circle (except for vicuna). this indicates that the domain of instruction does not serve as the principal factor affecting the instruction-following or most of the models have a good data coverage for such domains. the adaptation of llms for enhanced usability has garnered significant attention. two prevalent approaches for this adaptation are instruction tuning and alignment tuning @xcite . instruction tuning @xcite aims to unlock the capabilities of llms by finetuning them on annotated "instructional" data. this involves datasets with natural language instructions and their corresponding desired outcomes. several llm adaptations have embraced this method for effective refinement @xcite @xcite @xcite . alignment tuning @xcite @xcite differs from instruction tuning by emphasizing human feedback to ensure llms align with human values and preferences. this addresses llms' tendencies to exhibit unintended behaviors like fabricating information or producing biased expressions @xcite , steering them more in line with human expectations. a natural extension of this line of inquiry is the assessment of llms' ability to follow natural language instructions and align with human preferences accurately. in terms of evaluation, using llms for natural language generation evaluation has shown promise in numerous tasks @xcite @xcite @xcite @xcite . given this context, in our proposed work, we employ llms as evaluators to assess their aptitude for instructionfollowing. several works have concurrently assessed llms from various perspectives: wang et al. (2023b) explore the reasoning capabilities of llms for complex scientific problem solving; wang et al. (2023c) present a judge llm trained to distinguish the performance of various llms; li et al. (2023a) introduce an instruction-following evaluation protocol named verbalizer manipulation on classification tasks; li et al. (2023b) develop an llm-based automatic evaluation method to calculate win-rate of the given model's output favored over that of a reference model on an 805instruction dataset (dubois et al., 2023);xu et al. (2023) propose an automatic method to compose complex instructions for instruction-tuning;chia et al. (2023) introduce an evaluation suite designed for the assessment of instruction-tuned llms' ability on problem-solving, writing proficiency, and alignment with human values;jiang et al. (2023) adopts a distinct approach by sequentially adding fine-grained constraints to construct multi-level instructions;zhou et al. (2023) emphasizes objective evaluations with 25 verifiable instructions, contrasting with our work's broader coverage yet similar focus on objectivity. ## conclusion this study introduces a significant breakthrough in evaluating large language models (llms) through the decomposed requirements following ratio (drfr), a novel metric that dissects complex instructions into simpler criteria for a more nuanced analysis. this approach, coupled with the creation of infobench, a comprehensive benchmark dataset, enables a detailed assessment of llms' instruction-following capabilities. our findings reveal the superiority of drfr over traditional evaluation methods, offering clearer insights into model performance, especially in complex scenarios. the use of gpt-4 as an annotator emerged as a cost-effective, accurate, and efficient alternative for large-scale evaluations, highlighting its potential to automate and enhance the assessment process. the comprehensive evaluation of various llms using our methods uncovered their strengths and areas needing improvement. while significant progress is evident, especially in closed-source models, challenges remain in handling complex instructions, signaling a need for advancements in areas like numerical reasoning and linguistic comprehension. our contributions provide a foundation for future research and development, guiding the enhancement of llms for more accurate and reliable performance in real-world applications. this study not only underscores the current capabilities of llms but also paves the way for the next generation of more robust and versatile ai models.