linear reasoning

Definition

Linear reasoning in LLMs refers to the sequential, 'brick-by-brick' text generation process characteristic of auto-regressive models. This approach, while prevalent, is identified as a fundamental limitation preventing more holistic and globally coherent language understanding and generation.

At a glance

Executive summary

Linear reasoning describes how many AI text generators build sentences one word at a time, like adding bricks to a wall. While this method is common, it limits the AI's ability to plan ahead or make big, overall changes to the text. Newer AI models are exploring ways to think more holistically to overcome this sequential constraint.

TL;DR

Linear reasoning is the step-by-step way current AI models generate text, which limits their ability to see the big picture and refine their output holistically.

Key points

Sequential, token-by-token text generation, characteristic of auto-regressive models.
Enables predictable and efficient text generation, but creates limitations in global foresight and iterative refinement.
Predominantly used by current auto-regressive Large Language Models (LLMs) like GPT and LLaMA.
Contrasts with the holistic, bidirectional denoising processes proposed by Diffusion Language Models.
Shifting beyond linear reasoning to achieve more advanced, globally coherent, and iteratively refinable text generation.

Use cases

Predictive text and auto-completion in messaging apps and search engines.

Generating code snippets and functions in programming assistants like GitHub Copilot.

Creating short-form content, such as social media posts or email drafts.

Basic question-answering systems where responses can be built sequentially.

Definition

At a glance

Executive summary

TL;DR

Key points

Use cases

Also known as

Related papers

Related topics