Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models

Original Paper: https://arxiv.org/abs/2408.02442

By: Zhi Rui Tam, Cheng-Kuang Wu, Yi-Lin Tsai, Chieh-Yen Lin, Hung-yi Lee, Yun-Nung Chen

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.

Summary Notes

Figure 1:GPT-3.5-turbo prompted with GSM8K math questions in standard natural language answered correctly, but failed when format restrictions were applied.

Introduction

In the rapidly evolving landscape of artificial intelligence, Large Language Models (LLMs) have emerged as powerful tools capable of performing a diverse array of tasks.

From generating creative content to solving complex mathematical problems, LLMs have proven their versatility. However, their integration into real-world applications often requires adherence to standardized output formats such as JSON, XML, or YAML.

A recent study titled "Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models" investigates whether these format constraints hinder LLM performance.

The findings reveal surprising insights into the trade-offs between structured generation and task performance.

Methodology: Structuring the Unstructured

The study explores three common methodologies to impose format constraints on LLMs:

1. Constrained Decoding (JSON-mode):
• This method restricts the model’s output to a predefined token space, ensuring the generation of valid JSON format. It is widely used in industrial settings for its ability to produce parseable and standardized outputs.
2. Format-Restricting Instructions (FRI):
• LLMs are instructed to generate responses adhering to specific schemas, such as JSON, XML, or YAML. This approach ensures the output follows a structured format without enforcing a predefined token space.
3. NL-to-Format Conversion:
• This two-step process involves generating a response in natural language first and then converting it into the target format. It aims to retain the model’s reasoning ability while providing structured outputs.

The researchers evaluated these methodologies across a variety of tasks, including reasoning-intensive tasks like mathematical problem-solving and classification tasks such as medical diagnosis and financial content categorization.

Key Findings: The Cost of Structure

The study’s results indicate that format constraints significantly impact LLM performance, particularly in reasoning tasks. Here are the key findings:

1. Reasoning Abilities Decline Under Strict Format Constraints:
• Tasks such as GSM8K (mathematical problem-solving) and Last Letter Concatenation showed a notable decline in performance when using JSON-mode compared to natural language responses. For instance, GPT-3.5 Turbo’s performance on GSM8K dropped from 76.60% accuracy in natural language to 49.25% in JSON-mode.
2. Classification Tasks Show Mixed Results:
• In contrast, classification tasks like DDXPlus (medical diagnosis) and MultiFin (financial content categorization) benefited from the structured format. Gemini 1.5 Flash, for example, showed a significant performance boost in DDXPlus when using JSON-mode, highlighting that structured outputs can enhance accuracy in tasks requiring specific answer formats.
3. Looser Format Restrictions Yield Better Reasoning Performance:
• The NL-to-Format conversion method generally maintained the performance of natural language responses, suggesting that decoupling content generation from format adherence helps preserve the LLM’s reasoning abilities. This approach occasionally introduced minor generation errors but overall provided a balanced solution.
4. Parsing Errors Are Not the Primary Cause:
• Analysis revealed that parsing errors were not the main factor behind performance differences. For example, LLaMA 3 8B exhibited only a 0.15% parsing error rate in the JSON format for the Last Letter task, yet its performance differed significantly from the natural language format.

Implications and Applications

These findings have significant implications for the deployment of LLMs in industry:

1. Balancing Format Adherence and Performance:
• Practitioners need to strike a balance between the desire for easily parseable, structured outputs and the need to preserve the LLM’s inherent reasoning abilities. For reasoning-intensive tasks, looser format restrictions or NL-to-Format conversions might be more suitable.
2. Task-Specific Format Strategies:
• The choice of format constraints should be task-specific. While strict formats like JSON-mode may hinder reasoning tasks, they can enhance performance in classification tasks by reducing answer selection errors.
3. Mitigating Performance Degradation:
• Simple corrective steps, such as using LLMs to reformat outputs with parsing errors, can effectively enhance the reliability of structured outputs without significant performance trade-offs.

Conclusion

The study "Let Me Speak Freely? A Study on the Impact of Format Restrictions on Performance of Large Language Models" underscores the nuanced trade-offs between structured generation and LLM performance. While format constraints are essential for integrating LLMs into real-world applications, their impact on task performance varies. Striking the right balance between format adherence and reasoning capabilities is crucial for harnessing the full potential of LLMs. As AI continues to advance, understanding these dynamics will be key to optimizing their deployment in diverse industrial contexts.

Quote from the Researchers

"Our findings suggest that while structured outputs can be beneficial for downstream processing, overly restrictive schemas may hinder LLM performance, particularly in reasoning-intensive tasks," the researchers conclude.

Future Research

Future studies could explore how different levels of task complexity affect the impact of format constraints and investigate more flexible format adherence strategies. Incorporating a wider range of training data that includes various restrictive formats could also help mitigate performance degradation in local LLMs.