Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost

Original Paper: https://arxiv.org/abs/2407.19825

By: Sania Nayab, Giulio Rossolini, Giorgio Buttazzo, Nicolamaria Manes, Fabrizio Giacomelli

Abstract:

Today's large language models (LLMs) can solve challenging question-answering tasks, and prompt engineering techniques, such as chain-of-thought (CoT), have gained attention for enhancing the explanation and correctness of outputs. Nevertheless, models require significant time to generate answers augmented with lengthy reasoning details. To address this issue, this paper analyzes the impact of output lengths on LLM inference pipelines and proposes novel metrics to evaluate them in terms of \textit{correct conciseness}. It also examines the impact of controlling output length through a refined prompt engineering strategy, Constrained-CoT (CCoT), which encourages the model to limit output length. Experiments on pre-trained LLMs demonstrated the benefit of the proposed metrics and the effectiveness of CCoT across different models. For instance, constraining the reasoning of LLaMA2-70b to 100 words improves the accuracy from 36.01\% (CoT) to 41.07\% (CCoT) on the GSM8K dataset, while reducing the average output length by 28 words.

Summary Notes

In the world of Large Language Models (LLMs), such as GPT-3 and LLaMA, efficiency and accuracy are paramount. However, techniques that improve the correctness of responses often come with trade-offs, particularly in terms of output length and inference time. This blog post explores a fascinating research study that delves into these trade-offs and introduces a novel method to balance them: Constrained Chain-of-Thought (CCoT) prompting.

Introduction: The Challenge of Lengthy Outputs

Large Language Models (LLMs) have demonstrated remarkable capabilities in handling complex question-answering tasks, thanks to advanced techniques like Chain-of-Thought (CoT) prompting. CoT enhances the explanation and correctness of outputs by encouraging the model to articulate its reasoning step-by-step. However, this also leads to longer outputs, significantly increasing the time required for the model to generate a response. This delay is particularly undesirable in interactive applications where response time is critical.

The research in focus analyzes the impact of output lengths on LLM inference pipelines and proposes novel metrics to evaluate their performance in terms of conciseness and correctness. Additionally, it introduces Constrained Chain-of-Thought (CCoT) prompting, a refined prompt engineering strategy designed to limit output length without sacrificing accuracy.

Key Methodologies: Evaluating Conciseness and Correctness

Motivational Experiments

The study begins with motivational experiments to show the relationship between output length and inference time. It was found that the total response time of LLMs is strongly related to the length of the answer, increasing significantly with longer outputs. For instance, using CoT prompts on Falcon-40B and LLaMA2-70B models demonstrated a substantial increase in both output length and generation time.

Novel Metrics for Concise Correctness

To address the trade-offs between accuracy and efficiency, the study proposes three novel metrics:

1. Hard-k Concise Accuracy (HCA): This metric measures the fraction of correct outputs that do not exceed a user-specified length \( k \).
2. Soft-k Concise Accuracy (SCA): This generalizes HCA by penalizing correct answers that exceed the maximum length \( k \) with a term that decreases exponentially with a decay factor \( \alpha \).
3. Consistent Concise Accuracy (CCA): This further generalizes SCA by also accounting for the variation in lengths among all outputs, promoting consistency in response lengths.

Constrained Chain-of-Thought (CCoT) Prompting

The core innovation is the CCoT prompting technique. It refines the CoT approach by including an explicit sentence in the prompt to constrain the generated output to a maximum number of words. This encourages the model to compress its reasoning and produce more concise answers, thereby reducing inference time.

Main Findings: Enhancing Efficiency and Accuracy

The study conducted a series of experiments on various pre-trained LLMs using the GSM8K dataset, a benchmark for mathematical problem-solving. The results were revealing:

• Generation Time: CCoT prompting significantly reduced the generation time for large models like LLaMA2-70B, almost halving the time compared to CoT prompts.
• Accuracy: The accuracy of LLaMA2-70B improved from 36.01% (with CoT) to 41.07% (with CCoT-100), demonstrating that conciseness can enhance correctness.
• Output Length Control: CCoT effectively controlled the output length across different constraints, making it a versatile tool for various applications.

Detailed Analysis

For instance, applying CCoT to LLaMA2-70B with a length constraint of 45 words resulted in a concise and correct answer to a mathematical problem, compared to a longer and equally correct answer generated using a traditional CoT prompt. This not only improved efficiency but also maintained accuracy.

Implications and Applications

The implications of this research are far-reaching, especially in real-time systems and interactive applications where response time and accuracy are critical. By integrating CCoT prompting, LLMs can deliver concise, accurate answers faster, enhancing user experience and operational efficiency.

Potential Applications

• Interactive Chatbots: Reducing response times while maintaining accuracy can significantly improve user interactions.
• Real-Time Decision-Making Systems: Faster, more predictable response times are crucial in applications like autonomous vehicles and financial trading platforms.
• Educational Tools: Providing concise and correct explanations can enhance learning efficiency in educational AI systems.

Conclusion: Balancing Efficiency and Accuracy

This research highlights the importance of addressing the conciseness of LLM outputs and demonstrates that it is possible to achieve a better balance between efficiency and accuracy with the right prompt engineering strategies. The proposed CCoT prompting offers a practical solution to control output length, making LLMs more predictable and efficient.

As LLMs continue to evolve, the insights from this study will be invaluable in guiding the development of more efficient and accurate models. Future research could further explore the integration of these metrics into the training process and evaluate their impact on different types of tasks and models.

In summary, Constrained Chain-of-Thought prompting is a promising advancement in the field of AI, offering a pathway to more efficient and accurate large language models.