Compressed Chain of Thought: Efficient Reasoning Through Dense Representations (2412.13171v1)

Abstract: Chain-of-thought (CoT) decoding enables LLMs to improve reasoning performance at the cost of high generation latency in decoding. Recent proposals have explored variants of contemplation tokens, a term we introduce that refers to special tokens used during inference to allow for extra computation. Prior work has considered fixed-length sequences drawn from a discrete set of embeddings as contemplation tokens. Here we propose Compressed Chain-of-Thought (CCoT), a framework to generate contentful and continuous contemplation tokens of variable sequence length. The generated contemplation tokens are compressed representations of explicit reasoning chains, and our method can be applied to off-the-shelf decoder LLMs. Through experiments, we illustrate how CCoT enables additional reasoning over dense contentful representations to achieve corresponding improvements in accuracy. Moreover, the reasoning improvements can be adaptively modified on demand by controlling the number of contemplation tokens generated.

• The paper introduces a novel compressed chain-of-thought framework that encapsulates reasoning processes into dense tokens to enhance computational efficiency.
• It achieves a 9-point improvement in exact match accuracy on reasoning tasks with only a minimal increase in decoding time.
• The method integrates with pre-trained LLMs using LoRA, enabling scalable deployment and reduced latency in resource-constrained environments.

Insights from "Compressed Chain of Thought: Efficient Reasoning through Dense Representations"

The paper "Compressed Chain of Thought: Efficient Reasoning through Dense Representations" by Jeffrey Cheng and Benjamin Van Durme introduces a novel framework, Compressed Chain-of-Thought (CCoT), aimed at addressing the efficiency challenges associated with Chain-of-Thought (CoT) reasoning in LLMs. CoT reasoning, while effective at decomposing complex questions and improving reasoning capabilities, suffers from substantial latency due to the reliance on explicit reasoning chains. This paper's contribution centers on CCoT, which leverages compressed and continuous contemplation tokens to encapsulate reasoning processes, thereby enhancing efficiency without sacrificing accuracy.

Overview of the CCoT Framework

CCoT operates by generating contemplation tokens that are compressed representations of traditional reasoning chains. These tokens encapsulate the reasoning steps in a dense format, allowing the LLM to maintain high reasoning performance while drastically reducing the sequence length and thereby the computational cost. The method is designed to be compatible with existing decoder LLMs, facilitating integration with pre-trained models through techniques like Low-Rank Adaptation (LoRA).

The contemplation tokens in this framework are fundamentally different from the fixed-length and often noncontentful tokens used in prior related work, such as pause or filler tokens. Instead, CCoT tokens represent contentful, semantically grounded reasoning chains, albeit in a compressed form that balances the trade-off between reasoning quality and computational efficiency.

Strong Numerical Results and Claims

The experiments conducted illustrate the practical benefits of the CCoT approach. The authors report that with a compression ratio of 0.10, there is a notable 9-point improvement in exact match accuracy on reasoning tasks. This gain is achieved with a minimal increase in decoding time, demonstrating that CCoT effectively enhances reasoning capabilities without a proportional increase in computational overhead. These findings highlight CCoT's potential for applications where time efficiency is critical while maintaining high reasoning accuracy.

Practical and Theoretical Implications

Practically, CCoT could pave the way for deploying reasoning-intensive LLMs in environments where computational resources are limited or latency is a significant concern, such as real-time applications or mobile devices. By reducing the length of the reasoning sequences, CCoT not only speeds up inference but also decreases the model's memory footprint, making it more feasible for edge deployment.

Theoretically, CCoT shifts the perspective on reasoning in LLMs from relying on verbose thought processes to employing compact, yet content-rich representations. This presents a new paradigm where reasoning can be internalized and represented in a latent space. Such an approach could inspire further research into how LLMs can emulate human-like introspection and decision-making processes without extensive externalization.

Speculation on Future Developments in AI

Looking forward, CCoT opens up several avenues for future research and development. One potential direction is enhancing the scalability of CCoT to larger models and more complex tasks. Additionally, refining the subset selection of reasoning chains and exploring alternative methods for generating ground truth hidden states may yield further improvements in both accuracy and efficiency.

Another intriguing line of inquiry could involve augmenting CCoT with adaptive compression ratios that dynamically adjust based on task complexity or available computational resources. This would make the framework even more versatile and applicable to a broader range of scenarios, offering a more tailored approach to balancing performance and efficiency.

In summary, the CCoT framework represents a significant step towards more efficient and intelligent reasoning in LLMs. By leveraging dense representations, it challenges the existing paradigms of LLM reasoning and sets the stage for further advancements in AI reasoning capabilities.