Let Me Speak Freely? A Study On The Impact Of Format Restrictions On Large Language Model Performance. (2024)

Abstract: Structured generation, the process of producing content in standardized formats like JSON and XML, is widely utilized in real-world applications to extract key output information from large language models (LLMs).This study investigates whether such constraints on generation space impact LLMs’ abilities, including reasoning and domain knowledge comprehension. Specifically, we evaluate LLMs’ performance when restricted to adhere to structured formats versus generating free-form responses across various common tasks. Surprisingly, we observe a significant decline in LLMs’ reasoning abilities under format restrictions. Furthermore, we find that stricter format constraints generally lead to greater performance degradation in reasoning tasks.

## introduction the few-shot in-context learning @xcite and instruction-following @xcite capabilities of large language models (llms) have allowed them to solve downstream tasks out of the box. however, a major obstacle to incorporating llms into industrial applications is their lack of adherence to standardized output formats. this inconsistency complicates output parsing and undermines the reliability of these models. one common approach to overcoming this obstacle is structured generation, which involves providing output in standardized formats like json or xml through format restrictions. these restrictions can be implemented in various ways, such as instructing llms to adhere to specified formats with format-restricting instructions, or using industrial solutions like json mode @xcite , instructor @xcite , or figure 1 : gpt-3.5-turbo prompted with gsm8k math questions in standard natural language answered correctly, but failed when format restrictions were applied. guardrails @xcite . these strategies simplify parsing workflows and streamline the integration of llms into real-world applications. due to the growing demand for structured generation, the research community has shown increased interest in investigating llms' format-following abilities. for example, ifeval @xcite , infobench @xcite , and fofo @xcite focus on evaluating llms' instructionfollowing capabilities, including format adherence. however, these studies do not address a critical question for industrial applications: do formatrestricting instructions affect the quality of llms' generated content? in other words, they fail to explore whether format restrictions degrade llms' performance, which has great business impacts. this performance degradation is shown in figure 1 . in this work, we address the aforementioned research question through extensive empirical experiments. we present a comprehensive analysis of the potential impacts of format-restricting instructions on llms' performance across a wide range of tasks. the formats studied include commonly used schemas such as json, xml, and yaml. to the best of our knowledge, this is the first systematic investigation into the relationship between format-restricting instructions and the quality of generated content. our contributions are twofold: ## methodology for structured generation to study different levels of format restrictions on downstream performance, we adopt the following three common methodologies in our experiments: constrained decoding (json-mode): constrained decoding is a technique that limits the output of llms by enforcing predefined token space during the generation process. among mainstream llm providers, json mode is a widely implemented instance of this technique, especially due to its extensive use in industrial settings. this mode, available as a hyperparameter flag in openai and gemini apis, ensures the output is valid json. it is assumed that the implementation is similar to the constrained decoding methods described by @xcite , and provided in text-generation-inference foot_0 . format-restricting instructions (fri): they direct the llm to generate responses in standardized formats such as json, xml, and yaml, adhering to specified schemas. these instructions ensure that the generated output follows a structured format, facilitating the extraction and evaluation of the final answer. this approach is more relaxed than constrained decoding, as it does not enforce a predefined token space. nl-to-format: this two-step process first instructs the llm to answer the question in natural language, and then instructs it to convert its response into the target format schema. as the most relaxed version of structured generation, this method decouples content generation from format adherence, aiming to maintain the performance of unrestricted natural language responses while still providing structured output. ## conclusion our study reveals that structured generation constraints significantly impact llm performance across various tasks. format restrictions, particularly constrained decoding (json-mode), can hinder reasoning abilities while enhancing classification task accuracy. looser format restrictions generally improve performance and reduce variance in reasoning tasks. parsing errors, while not the primary cause of performance differences, can be mitigated through corrective prompting. these findings underscore the importance of balancing format adherence, reasoning capabilities, and cost efficiency in llm applications. given that our study focuses on reasoning-intensive tasks, future work should explore how reasoning tasks of vary-ing difficulty, from intensive to simple, are affected by restrictive formats and llms. to mitigate the performance degradation of llms due to restrictive formats, future studies should include a wider range of training data that contains instructions in various restrictive formats in local llms. with model size and architecture. second, our evaluation dataset, while diverse, is limited in scope. a broader range of tasks and domains could offer a more comprehensive assessment of the proposed approach's effectiveness and generalizability.