用大模型修复缺失文本，实现抗干扰的高效通信
Robust Transmission of Punctured Text with Large Language Model-based Recovery
- 仅传输关键字符，接收端用大模型补全
- 新选择器提升恢复准确率，低信噪比下优于传统通信
- 跨数据集任务稳定，适合实际部署场景
随着深度学习的发展，语义通信因其仅传输任务相关特征而迅速兴起。然而，特征提取依赖学习模型，性能受训练数据或任务影响。为应对实际场景，需设计对数据和任务不敏感的鲁棒模型。本文提出一种新型文本传输模型：仅选择并传输少数字符，接收端利用大语言模型（LLM）恢复缺失内容。同时提出重要字符提取器（ICE），通过优化选择传输字符以提升LLM恢复性能。仿真表明，基于ICE的筛选策略优于随机选择；该模型在不同数据集与任务上均表现稳健，并在低信噪比条件下超越传统比特通信。
原文摘要 · Abstract (English)
With the recent advancements in deep learning, semantic communication which transmits only task-oriented features, has rapidly emerged. However, since feature extraction relies on learning-based models, its performance fundamentally depends on the training dataset or tasks. For practical scenarios, it is essential to design a model that demonstrates robust performance regardless of dataset or tasks. In this correspondence, we propose a novel text transmission model that selects and transmits only a few characters and recovers the missing characters at the receiver using a large language model (LLM). Additionally, we propose a novel importance character extractor (ICE), which selects transmitted characters to enhance LLM recovery performance. Simulations demonstrate that the proposed filter selection by ICE outperforms random filter selection, which selects transmitted characters randomly. Moreover, the proposed model exhibits robust performance across different datasets and tasks and outperforms traditional bit-based communication in low signal-to-noise ratio conditions.
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