Towards the Holographic Characteristic of LLMs for Efficient Short-text Generation

2026-01-30

AI Summary

This work addresses the inefficiency of large language models in short-text generation by identifying and formally naming their “holographic property”—the phenomenon wherein target keywords are captured as early as the initial generation steps. Building on this insight, the authors propose HOLO, a plug-and-play module that requires no additional training and integrates a parallel lexical constraint mechanism to enable high-quality output within a limited number of generation steps. Experimental results demonstrate that HOLO consistently maintains generation quality on par with baseline models across diverse architectures and scales, while significantly improving inference efficiency.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Search and Retrieval-Augmented AI: Large language models for search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

The recent advancements in Large Language Models (LLMs) have attracted interest in exploring their in-context learning abilities and chain-of-thought capabilities. However, there are few studies investigating the specific traits related to the powerful generation capacity of LLMs. This paper aims to delve into the generation characteristics exhibited by LLMs. Through our investigation, we have discovered that language models tend to capture target-side keywords at the beginning of the generation process. We name this phenomenon the Holographic Characteristic of language models. For the purpose of exploring this characteristic and further improving the inference efficiency of language models, we propose a plugin called HOLO, which leverages the Holographic Characteristic to extract target-side keywords from language models within a limited number of generation steps and complements the sentence with a parallel lexically constrained text generation method. To verify the effectiveness of HOLO, we conduct massive experiments on language models of varying architectures and scales in the short-text generation scenario. The results demonstrate that HOLO achieves comparable performance to the baselines in terms of both automatic and human-like evaluation metrics and highlight the potential of the Holographic Characteristic.

Problem

• Large Language Models
• text generation
• generation characteristics
• holographic characteristic
• short-text generation

Innovation

• Holographic Characteristic
• HOLO
• short-text generation
• lexically constrained generation
• inference efficiency