用大模型零样本提示实现无线符号检测，低数据下表现优于传统神经网络。
Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning
- 利用大模型的上下文学习能力，无需训练直接完成无线符号解调。
- 在数据稀缺场景下，性能超越传统深度神经网络，准确率显著提升。
- 通过提示模板优化与模型校准，提升预测可靠性，适合通信系统研究者。
深度神经网络（DNN）在无线系统中已取得显著进展，尤其在缺乏精确无线模型时表现突出。然而，当可用数据有限时，传统DNN常因欠拟合导致性能不佳。与此同时，以GPT-3为代表的大语言模型（LLM）在自然语言处理任务中展现出强大能力。但其能否以及如何应用于无线系统中的非语言挑战性任务尚不明确。本文提出利用LLM的上下文学习能力（即提示工程），在无需任何训练或微调的情况下解决低数据条件下的无线任务。我们进一步发现不同提示模板对LLM性能影响显著。为此，引入最新的LLM校准方法。实验结果表明，采用ICL方法的LLM在符号解调任务上普遍优于传统DNN，且结合校准技术后可生成高置信度预测。
原文摘要 · Abstract (English)
Deep neural networks (DNNs) have made significant strides in tackling challenging tasks in wireless systems, especially when an accurate wireless model is not available. However, when available data is limited, traditional DNNs often yield subpar results due to underfitting. At the same time, large language models (LLMs) exemplified by GPT-3, have remarkably showcased their capabilities across a broad range of natural language processing tasks. But whether and how LLMs can benefit challenging non-language tasks in wireless systems is unexplored. In this work, we propose to leverage the in-context learning ability (a.k.a. prompting) of LLMs to solve wireless tasks in the low data regime without any training or fine-tuning, unlike DNNs which require training. We further demonstrate that the performance of LLMs varies significantly when employed with different prompt templates. To solve this issue, we employ the latest LLM calibration methods. Our results reveal that using LLMs via ICL methods generally outperforms traditional DNNs on the symbol demodulation task and yields highly confident predictions when coupled with calibration techniques.
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