C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness (2412.11664v1)

Abstract: Generating Chain-of-Thought (CoT) before deriving the answer can effectively improve the reasoning capabilities of LLMs and significantly improve the accuracy of the generated answer. However, in most cases, the length of the generated CoT is much longer than the desired final answer, which results in additional decoding costs. Furthermore, existing research has discovered that shortening the reasoning steps in CoT, even while preserving the key information, diminishes LLMs' abilities. These phenomena make it difficult to use LLMs and CoT in many real-world applications that only require the final answer and are sensitive to latency, such as search and recommendation. To reduce the costs of model decoding and shorten the length of the generated CoT, this paper presents $\textbf{C}$onditioned $\textbf{C}$ompressed $\textbf{C}$hain-of-$\textbf{T}$hought (C3oT), a CoT compression framework that involves a compressor to compress an original longer CoT into a shorter CoT while maintaining key information and interpretability, a conditioned training method to train LLMs with both longer CoT and shorter CoT simultaneously to learn the corresponding relationships between them, and a conditioned inference method to gain the reasoning ability learned from longer CoT by generating shorter CoT. We conduct experiments over four datasets from arithmetic and commonsense scenarios, showing that the proposed method is capable of compressing the length of generated CoT by up to more than 50% without compromising its effectiveness.

• The paper introduces C3oT, a conditional training framework that generates concise yet effective chain-of-thought reasoning in LLMs.
• It utilizes a compressor along with conditioned training and inference methods to cut reasoning steps by over 50% without loss of accuracy.
• Empirical validation on arithmetic and commonsense datasets demonstrates enhanced model efficiency and reduced computational costs.

Overview of Conditioned Compressed Chain-of-Thought (C3oT) in LLMs

The paper "C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness" presents a novel framework aimed at condensing the Chain-of-Thought (CoT) generated by LLMs without degrading their effectiveness. The motivation stems from the need to reduce model inference costs while retaining the reasoning ability benefits brought on by CoT. This is pertinent for applications sensitive to latency such as search and recommendation systems.

Key Contributions

1. C3oT Framework: The primary contribution of the paper is the introduction of the Conditioned Compressed Chain-of-Thought (C3oT), a framework designed to compress intermediate reasoning steps in LLM outputs. The framework involves three critical components:
   • A Compressor that transforms a detailed CoT into a more concise form while maintaining essential content and interpretability.
   • A Conditioned Training Method that trains LLMs concurrently on both long and short CoT with distinctive initial prompt tokens, enabling the models to learn the relationships between them.
   • A Conditioned Inference Method that employs learned reasoning skills from longer CoTs to produce shorter, yet effective CoTs during inference.
2. Empirical Validation: Through experiments on arithmetic (GSM8K, MathQA) and commonsense reasoning datasets (ECQA, StrategyQA), the paper demonstrates that C3oT can compress CoT by over 50% without compromising accuracy.
3. Ablation Studies and Analysis: Extensive studies examine the individual contributions of the components within C3oT and explore further research trends, such as using different models as compressors and adapting the method to longer reasoning chains.

Theoretical and Practical Implications

The introduction of C3oT addresses the trade-off between reasoning robustness and inference efficiency in LLMs. The theoretical implication is that CoT, traditionally long for effective reasoning, can indeed be compressed effectively when proper conditioning is utilized. This suggests avenues for further research into CoT dynamics and complexity management of CoTs to bolster model reasoning capabilities without incurring high computational costs.

Practically, C3oT opens pathways to more efficient LLM deployments in applications and serves industries requiring fast yet accurate decision-making. This research could significantly impact designing models where cost-efficiency is as crucial as performance.

Future Directions

The findings propose several directions for future research:

• Extending the current framework to other reasoning-intensive applications beyond the tested domains.
• Investigating more sophisticated compressors or leveraging task-specific conditioning strategies for better compression rates.
• Exploring the combination of C3oT with quantization and pruning techniques in LLMs for comprehensive model efficiency.

In conclusion, the research provides compelling evidence for the use of compressed CoTs, pushing the boundaries of current LLM reasoning frameworks to balance accuracy with efficiency. This is crucial for practical LLM applications striving to deliver quick and precise results in real-world scenarios.