We introduce a frustratingly simple, super efficient and surprisingly effective decoding method, which we call Frustratingly Simple Decoding (FSD), for neural text generation. The idea behind FSD is straightforward: we build an anti-LM based on previously generated text and use this anti-LM to penalize future generation of what has been generated. The anti-LM can be implemented as simple as an n-gram language model or a vectorized variant. In this way, FSD introduces no extra model parameters and negligible computational overhead (FSD can be as fast as greedy search). Despite the simplicity, FSD is surprisingly effective; Experiments show that FSD can outperform the canonical methods to date (i.e., nucleus sampling) as well as several strong baselines that were proposed recently.
@article{arxiv.2305.12675,
title = {A Frustratingly Simple Decoding Method for Neural Text Generation},
author = {Haoran Yang and Deng Cai and Huayang Li and Wei Bi and Wai Lam and Shuming Shi},
journal= {arXiv preprint arXiv:2305.12675},
year = {2024}
}