# Large-Language-Model-Enabled Text Semantic Communication Systems
## 5 Discussion
### Insights and Challenges  
Relative to classical DeepSC, LLM-SC manifests several distinguishing characteristics:  
* Longer context length: Word encoding and decoding lengths can match LLM's context length, whereas DeepSC's maximum length is limited to 30. According to [PERSON]'s information theory, longer code lengths theoretically enhance error correction capability.
* DeepSC disregards distinctions such as uppercase vs. lowercase, punctuation, and special characters, simplifying semantic encoding. In contrast, LLMs consider the entire natural language vocabulary, enhancing adaptability and alignment with actual usage.
* DeepSC's training on the European Parliament dataset restricts its adaptability to other corpora, while LLMs are typically trained on broad natural language corpora, potentially making them applicable across multiple languages.
* DeepSC outputs fixed-length symbols regardless of input length, whereas LLM-SC adapts symbol length based on input, potentially improving efficiency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Content** & **BLEU (1-Gram)** & **BER** \\ \hline \multirow{2}{*}{**transmitted sentence**} & life is just a series of trying to make up your mind about what you & - & - \\  & want to do and then doing it. & & \\ \hline \multirow{2}{*}{**LLM-SC (12 dB)**} & life is just a series of trying to make up your mind about what you & \multirow{2}{*}{1} & \multirow{2}{*}{0} \\  & want to do and then doing it. & & \\ \hline \multirow{2}{*}{**LLM-SC (6 dB)**} & life is just a series of steps to make up your mind about what you & \multirow{2}{*}{0.94} & \multirow{2}{*}{0.026} \\  & want to do and then doing it. & & \\ \hline \multirow{2}{*}{**LLM-SC (3 dB)**} & hardly ever just a game of trying to make up your mind about & \multirow{2}{*}{0.86} & \multirow{2}{*}{0.031} \\  & what you want to do and then doing it. & & \\ \hline \multirow{2}{*}{**DeepSC (12 dB)**} & funding is just a number of clearly to make up your note about & \multirow{2}{*}{0.72} & \multirow{2}{*}{0.32} \\  & what you want to do that and then at & & \\ \hline \multirow{2}{*}{**DeepSC (6 dB)**} & secondly is just a m of having to make up your speaking about & \multirow{2}{*}{0.54} & \multirow{2}{*}{0.39} \\  & what you to have to do now so how at at & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The sample sentences between different methods over Rayleigh channels.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **Time** \\ \hline
**LLM-SC** & 9.2 ms/character \\ \hline
**DeepSC** & 1.24 ms/character \\ \hline \hline \end{tabular}
\end{table}
Table 5: An example of LLM demodulation.  
However, challenges remain in terms of computation constraints and real-time requirements before fully harnessing LLMs' potential in semantic communication. Advances in model architecture, accelerators, compression, and quantization methods can mitigate these challenges. Future avenues include benchmarking different model architectures, analyzing artifacts, enhancing robustness, and conducting comparative studies across diverse datasets and languages. Exploring joint optimizations with classical error-correcting codes also holds promise.