Core Concepts
The author introduces adaptive decoding to dynamically determine the candidate set size, balancing coherence and diversity in text generation tasks.
Abstract
The study introduces adaptive decoding to improve text generation quality by dynamically determining a suitable candidate set size. Experimental results show superior performance over existing algorithms in maintaining coherence and enhancing diversity.
The content discusses the challenges of existing decoding algorithms like greedy decoding and beam search, highlighting issues such as repetition and incoherence. The proposed adaptive decoding method aims to address these challenges by dynamically adjusting the candidate set during text generation.
By leveraging entropy reduction and confidence metrics, the adaptive decoding algorithm effectively balances coherence and diversity in generated text. Experiments on different language models demonstrate improved performance in terms of MAUVE score, diversity, and coherence compared to traditional decoding methods.
Key metrics like HIT@k, rep-n, MAUVE score, diversity, and coherence are used to evaluate the effectiveness of the adaptive decoding algorithm across different datasets and models. Human evaluation results also indicate promising outcomes with a notable improvement over traditional decoding methods.
Overall, the study presents a novel approach to enhance open-ended text generation by dynamically adapting the candidate set size based on model confidence levels, leading to more human-like and coherent text outputs.
Stats
GPT2-XL achieves higher MAUVE with adaptive decoding.
Llama2-7B-chat shows enhanced coherence with increased diversity using adaptive decoding.
HIT@1 for GPT2-XL is 37.09.
HIT@3 for Llama2-7B is 47.14.
Top-p sampling leads to incoherence issues with GPT2-XL base model.
Adaptive decoding improves diversity while preserving coherence on WritingPrompts dataset.
Quotes
"Our method achieves higher MAUVE and diversity in story generation tasks."
"The experimental results reveal that our approach significantly enhances diversity while preserving coherence."