# Hallucination-aware Optimization for Large Language Model-empowered Communications

arXiv: https://arxiv.org/abs/2412.06007 · HTML: https://mlsanity.com/paper/2412.06007/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2024.

## Built on by (most cited first, 1 of 1)

- [FaithRL: Learning to Reason Faithfully through Step-Level Faithfulness Maximization](https://mlsanity.com/paper/2602.03507/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have significantly advanced communications fields, such as Telecom Q&A, mathematical modeling, and coding. However, LLMs encounter an inherent issue known as hallucination, i.e., generating fact-conflicting or irrelevant content. This problem critically undermines the applicability of LLMs in communication systems yet has not been systematically explored. Hence, this paper provides a comprehensive review of LLM applications in communications, with a particular emphasis on hallucination mitigation. Specifically, we analyze hallucination causes and summarize hallucination mitigation strategies from both model- and system-based perspectives. Afterward, we review representative LLM-empowered communication schemes, detailing potential hallucination scenarios and comparing the mitigation strategies they adopted. Finally, we present a case study of a Telecom-oriented LLM that utilizes a novel hybrid approach to enhance the hallucination-aware service experience. On the model side, we publish a Telecom hallucination dataset and apply direct preference optimization to fine-tune LLMs, resulting in a 20.6% correct rate improvement. Moreover, we construct a mobile-edge mixture-of-experts architecture for optimal LLM expert activation. Our research aims to propel the field of LLM-empowered communications forward by detecting and minimizing hallucination impacts.