Penalty Decoding: Well Suppress the Self-Reinforcement Effect in Open-Ended Text Generation
This addresses issues in text generation for NLP applications, but it is incremental as it builds on existing penalty methods.
The paper tackled the self-reinforcement effect in open-ended text generation by proposing a penalty decoding approach with a forgetting mechanism and length penalty, resulting in improved sentence quality that resembles human output.
The decoding algorithm is critical for open-ended text generation, transforming latent representations into coherent and meaningful outputs. This paper investigates the self-reinforcement effect in text generation and the effectiveness of a repetition penalty to mitigate it. However, determining the optimal repetition penalty value is challenging. To tackle this, we propose a forgetting mechanism that disregards distant tokens, reducing the burden of penalty selection. In addition, we introduce a length penalty to address overly short sentences caused by excessive penalties. Our penalty decoding approach incorporating three strategies helps resolve issues with sampling methods deviating from factual information. Experimental results demonstrate the efficacy of our approach in generating high-quality sentences resembling human output.