Comparing SEDD and GPT-2 in the Rise of Language Models #DiffusionBasedLanguageModels

Large Language Models (LLMs) have shown exceptional performance in natural language processing but face challenges due to the autoregressive training paradigm. This results in slow processing speeds and exposure bias, prompting researchers to explore alternative approaches. Techniques like efficient implementations, low-precision inference, novel architectures, and multi-token prediction have been developed to enhance LLMs. Researchers from CLAIRE have explored Score Entropy Discrete Diffusion (SEDD) as an alternative to autoregressive models, offering a balance between quality and computational efficiency. SEDD, based on a transformer backbone similar to GPT-2, shows promising results in matching or exceeding GPT-2’s performance on various datasets. It offers flexibility in sampling and non-causal token generation, allowing for reasoning over long sequences. However, challenges remain in sampling efficiency and diversity, especially in conditional generation with short prompts. The study presents SEDD as a viable alternative to autoregressive models, highlighting its potential for various applications. Further research is needed to optimize SEDD’s performance and address its limitations. The paper provides detailed insights into SEDD’s strengths and areas for improvement, emphasizing the ongoing quest to enhance language generation models.

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