The content delves into transmitting natural language text over noiseless and character-erasure channels using small language models. By allowing the destination to predict or complete words, the author aims to balance transmission costs with semantic similarity. Key findings include the superiority of the threshold policy over the periodic policy in achieving higher similarity for a given cost over a noiseless channel. Additionally, the performance of neural language models is compared to first-order Markov chain-based models, showing improved similarity at increased complexity. However, all prediction algorithms perform poorly over a character-erasure channel. Compression through Huffman coding reduces transmission costs while preserving performance trends.
The content details various aspects such as system models, prediction algorithms (LSTM-SLM and MCM), word completion models, communication policies (TP and PP), and Huffman compression schemes. Numerical results highlight the impact of different factors on average cost-similarity pairs under varying conditions. Notably, LSTM-SLM outperforms MCM in achieving higher similarity for a given cost but incurs higher complexity and time requirements.
Further exploration includes analyzing the contribution of word prediction and word completion algorithms to reducing transmission costs for specific similarities under different thresholds. The study emphasizes the importance of balancing communication efficiency with semantic fidelity in natural language text transmission.
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arxiv.org
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by Bhavani A Ma... at arxiv.org 03-04-2024
https://arxiv.org/pdf/2403.00290.pdfDeeper Inquiries