Read Quietly, Think Aloud: Decoupling Comprehension and Reasoning in LLMs

TL;DR

This paper explores methods to enable Large Language Models to perform internal silent reading and reasoning, improving their comprehension and response quality by mimicking human-like internal processing stages.

Contribution

It introduces simple yet effective techniques like contextual prompts and a 'reading buddy' architecture to enhance LLMs' internal processing capabilities.

Findings

Significant performance improvements with initial contextual prompts

Enhanced understanding through the 'reading buddy' architecture

Multiple point accuracy boosts observed

Abstract

Large Language Models (LLMs) have demonstrated remarkable proficiency in understanding text and generating high-quality responses. However, a critical distinction from human cognition is their typical lack of a distinct internal `reading' or deliberation phase before `speaking' (i.e., generating text). Humans often engage in silent reading to comprehend context and formulate thoughts prior to articulation. This paper investigates methods to imbue LLMs with a similar capacity for internal processing. We introduce and evaluate techniques that encourage LLMs to `read silently.' Our findings indicate that even a straightforward approach, such as providing the model with an initial contextual prompt or `reading space' before it begins predicting subsequent tokens for the final output, can yield significant performance improvements. We further enhance this concept by developing a `reading buddy' architecture, where an auxiliary component silently processes the input and provides refined contextual insights to the primary generation model. These approaches aim to foster deeper understanding from LLMs so that they can produce better reasoned responses, moving them one step closer to more human-like text processing. Our results indicate that these simple techniques can provide surprisingly strong impact on accuracy with multiple point accuracy boost.