Compressed Chain of Thought: Efficient Reasoning Through Dense
  Representations
Chain-of-thought (CoT) decoding enables language models 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 language models. 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.
6762f21ea3eed649f9889a1c
Jeffrey Cheng
extracted_confirmed
2025-02-20T15:37:24.321Z
65f28eebf5cf26fe0632ce67
/avatars/cd970cfe4215374c82d47df57ac30795.svg
Jeffrey Cheng
user
nexync
6762f21ea3eed649f9889a1d
Benjamin Van Durme
Compressed Chain of Thought: Efficient Reasoning Through Dense
  Representations
Chain-of-thought (CoT) decoding enables language models 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 language models. 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.
Compressed Chain-of-Thought (CCoT) enhances language model reasoning by generating efficient, variable-length contemplation tokens during inference, improving accuracy without fixed-length tokens.
Chain-of-thought decoding contemplation tokens discrete set of embeddings continuous contemplation tokens variable sequence length explicit reasoning chains off-the-shelf decoder language models reasoning accuracy