# Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning

arXiv: https://arxiv.org/abs/2409.00124 · HTML: https://mlsanity.com/paper/2409.00124/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 41 references that are in the map.
- First posted on arXiv 28 Aug 2024.

## Builds on (most cited first, 22 of 22)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [OPT: Open Pre-trained Transformer Language Models](https://mlsanity.com/paper/2205.01068/) (2022)
- [Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://mlsanity.com/paper/2202.12837/) (2022)
- [Calibrate Before Use: Improving Few-Shot Performance of Language Models](https://mlsanity.com/paper/2102.09690/) (2021)
- [Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution](https://mlsanity.com/paper/2202.10054/) (2022)
- [Unified Language Model Pre-training for Natural Language Understanding and Generation](https://mlsanity.com/paper/1905.03197/) (2019)
- [On the Stability of Fine-tuning BERT: Misconceptions, Explanations, and Strong Baselines](https://mlsanity.com/paper/2006.04884/) (2020)
- [LIFT: Language-Interfaced Fine-Tuning for Non-Language Machine Learning Tasks](https://mlsanity.com/paper/2206.06565/) (2022)
- [Deep Learning for Wireless Communications](https://mlsanity.com/paper/2005.06068/) (2020)
- [A Very Brief Introduction to Machine Learning With Applications to Communication Systems](https://mlsanity.com/paper/1808.02342/) (2018)
- [Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning](https://mlsanity.com/paper/1908.09049/) (2019)
- [Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning](https://mlsanity.com/paper/2009.09967/) (2020)
- [Deep Learning for Wireless Communications: An Emerging Interdisciplinary Paradigm](https://mlsanity.com/paper/2007.05952/) (2020)
- [From Learning to Meta-Learning: Reduced Training Overhead and Complexity for Communication Systems](https://mlsanity.com/paper/2001.01227/) (2020)
- [Enhancing In-context Learning via Linear Probe Calibration](https://mlsanity.com/paper/2401.12406/) (2024)
- [In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models](https://mlsanity.com/paper/2311.06101/) (2023)
- [Cell-Free Multi-User MIMO Equalization via In-Context Learning](https://mlsanity.com/paper/2404.05538/) (2024)
- [DeepWiPHY: Deep Learning-based Receiver Design and Dataset for IEEE 802.11ax Systems](https://mlsanity.com/paper/2010.09268/) (2020)
- [Calibrating AI Models for Few-Shot Demodulation via Conformal Prediction](https://mlsanity.com/paper/2210.04621/) (2022)
- [Bayesian Active Meta-Learning for Few Pilot Demodulation and Equalization](https://mlsanity.com/paper/2108.00785/) (2021)

## Built on by (most cited first, 9 of 9)

- [A Survey on Large Language Models for Communication, Network, and Service Management: Application Insights, Challenges, and Future Directions](https://mlsanity.com/paper/2412.19823/) (2024)
- [LLM-Enabled In-Context Learning for Data Collection Scheduling in UAV-assisted Sensor Networks](https://mlsanity.com/paper/2504.14556/) (2025)
- [FRSICL: LLM-Enabled In-Context Learning Flight Resource Allocation for Fresh Data Collection in UAV-Assisted Wildfire Monitoring](https://mlsanity.com/paper/2507.10134/) (2025)
- [Decision Feedback In-Context Symbol Detection over Block-Fading Channels](https://mlsanity.com/paper/2411.07600/) (2024)
- [AIC-VDS: Attention-Based In-Context Learning for Joint Velocity Control and Data Collection Scheduling in Multi-UAV-Assisted Pipeline Monitoring](https://mlsanity.com/paper/2510.05698/) (2025)
- [In-Context Learning for Non-Stationary MIMO Equalization](https://mlsanity.com/paper/2510.08711/) (2025)
- [Chat-Driven Optimal Management for Virtual Network Services](https://mlsanity.com/paper/2512.24614/) (2025)
- [Context-Aware Wireless Token Communication via Joint Token Masking and Detection](https://mlsanity.com/paper/2605.02123/) (2026)
- [Decision Feedback In-Context Learning for Wireless Symbol Detection](https://mlsanity.com/paper/2503.16594/) (2025)

## Abstract (arXiv)

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.