Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost (2407.19825v2)

Abstract: Today's 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. However, many models and techniques tend to produce excessively verbose and lengthy answers, leading to issues with both conciseness and generation time. To address this, this paper analyzes the impact of output lengths on LLM inference pipelines by introducing and proposing novel metrics to evaluate the \textit{correct conciseness} of a model and related prompting techniques. Then, we examine the impact of controlling output length through a refined prompt engineering strategy, Constrained-CoT (CCoT), which encourages the model to produce more concise outputs. To better understand the effects of such a prompt, we also introduce two additional scores for analyzing the conciseness, measured in terms of redundancy and information flow in generated answers. Experiments on pretrained LLMs and multiple datasets demonstrate the benefits of the proposed metrics and the effectiveness of CCoT across different models.

• The paper introduces novel conciseness metrics (HCA, SCA, CCA) that balance response length and reasoning accuracy.
• The paper proposes Constrained-Chain-of-Thought (CCoT) prompting to guide LLMs in generating short, accurate outputs.
• Experimental results on models like LLaMA2-70b and Falcon-40b show that shorter outputs lead to faster inference and improved performance.

Impact of Output Length on LLM Reasoning and Cost

In their paper titled "Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost," Nayab et al. investigate the relationship between the lengths of outputs generated by LLMs and their inference times, emphasizing the need for conciseness to improve both efficiency and accuracy. The authors introduce a refined prompt engineering technique, Constrained-Chain-of-Thought (CCoT), which explicitly encourages models to generate concise answers while preserving the correctness inherent in chain-of-thought (CoT) prompting. This paper provides significant advancements in evaluating and controlling LLM outputs to ensure they are both accurate and efficiently generated.

Key Contributions

Novel Metrics for Conciseness:

The paper introduces three new metrics designed to evaluate the correctness of LLM outputs with an emphasis on their conciseness:

1. Hard-$k$ Concise Accuracy (HCA): Measures the fraction of correct outputs that do not exceed a specified length $k$. This metric is useful when strict adherence to length constraints is sought.
2. Soft-$k$ Concise Accuracy (SCA): Generalizes HCA by introducing a penalty term that decreases exponentially using a decay factor $\alpha$. This metric allows some tolerance for outputs exceeding $k$ slightly.
3. Consistent Concise Accuracy (CCA): Adds an additional layer by also considering the variation in lengths of the outputs. This metric promotes uniformity in the length of the generated responses.

Constrained-CoT (CCoT) Prompting:

The authors propose a new prompt engineering strategy, CCoT, which modifies the traditional CoT prompt by explicitly requesting the LLM to limit the length of its reasoning. This approach aims to combine the step-by-step correctness afforded by CoT with the efficiency of more concise answers.

Experimental Setup and Findings

The authors evaluated several pre-trained LLMs including Vicuna-13b, Falcon-40b, Falcon-7b, Llama2-7b, and Llama2-70b using the GSM8K dataset, which is focused on mathematical problem-solving tasks. Various lengths constraints were tested to measure the effects on accuracy and inference time.

Efficiency and Accuracy Gains

The experiments revealed that:

• LLaMA2-70b and Falcon-40b: Both models showed improvements in accuracy and reduced inference times under CCoT prompting. Specifically, LLaMA2-70b saw an increase in accuracy from 36.01\% (CoT) to 41.07\% (CCoT-100) while also reducing the average output length.
• Smaller Models: Models like Falcon-7b and Llama2-7b were less effective in leveraging CCoT and showed mixed results regarding accuracy and inference times.

Output Length Control

Upon reviewing the output length distribution, it was noted that:

• ANSIthe LLMs generally produced shorter outputs under CCoT prompting, though not always strictly within the specified limits. This ability to approximate the length constraints while maintaining accuracy underscores the dynamic nature of these models.

Implications and Future Directions

The introduction of metrics such as HCA, SCA, and CCA provides a more nuanced understanding of model performance, extending beyond mere accuracy to include efficiency and consistency in response length. This multifaceted evaluation is pivotal for real-world applications where timely and concise responses are critical.

The findings indicate that restraining output length can be beneficial not just for model efficiency but potentially for accuracy as well. The use of CCoT prompts could feasibly integrate into fine-tuning processes to make models inherently capable of better managing output lengths.

Future research could explore:

• Integration with Training: Embedding these conciseness metrics into the training regime to cultivate models better adapted to length constraints.
• Extensions to Other Tasks: Applying CCoT beyond the GSM8K dataset to other traditional NLP tasks, assessing the generalizability of the approach.
• Mitigating Hallucinations: Analyzing how conciseness impacts the phenomenon of hallucinations, where models generate plausible but incorrect information.

In conclusion, the paper offers substantial advancements in prompt engineering and performance metrics, presenting a balanced approach to improving LLMs' practical applicability in time-sensitive and context-specific deployments.