Alapfogalmak
This research aims to comprehensively evaluate and compare various prominent decoding methodologies utilized in text generation with a pre-trained GPT-2 model. The study endeavors to establish a set of metrics to identify the most efficacious decoding technique, which can also serve as a tool for adversarial attacks on text classification models.
Kivonat
The report begins by providing an overview of the evolution of text generation models, tracing the progress from early statistical methods to the current era of transformer-based large language models (LLMs). It highlights the key innovations and limitations of models like GPT, BERT, and their successors.
The methodology section delves into the theoretical background of several decoding strategies for text generation, including greedy search, beam search, top-K sampling, top-P sampling, contrastive search, and locally typical sampling. The working principles, strengths, and weaknesses of each method are discussed in detail.
The performance results section presents a comprehensive evaluation of these decoding techniques based on metrics like perplexity, BLEU score, and diversity. The analysis reveals that while greedy search and beam search offer efficiency, they tend to produce repetitive and less diverse outputs. In contrast, sampling-based methods like top-K and top-P sampling introduce more variability, but may compromise coherence. The report highlights how contrastive search and locally typical sampling strike a better balance between relevance, coherence, and diversity in the generated text.
Finally, the report concludes by summarizing the key findings and proposing avenues for future research, including the potential of using the developed evaluation framework as a tool for adversarial attacks on text classification models.
Statisztikák
The cat sat on the mat.
The cat sat on the chair.
The cat sat on the rug.
Idézetek
"Greedy search tends to favor locally optimal choices without considering alternative paths, potentially resulting in more unpredictable outcomes."
"Top-K sampling may still produce repetitive sequences, especially when the top-K candidates contain similar tokens with high probabilities."
"Locally typical sampling aims to generate text that closely matches the information content expected by humans given the prior context."