Conditional [MASK] Discrete Diffusion Language Model

• URL: http://arxiv.org/abs/2411.06438v5
• Date: Mon, 24 Feb 2025 09:11:03 GMT
• Title: Conditional [MASK] Discrete Diffusion Language Model
• Authors: Hyukhun Koh, Minha Jhang, Dohyung Kim, Sangmook Lee, Kyomin Jung,
• Abstract summary: Diffusion-EAGS is a framework that integrates conditional masked language models into diffusion language models.<n>We show that Diffusion-EAGS achieves the best quality-diversity tradeoff, demonstrating its effectiveness in non-autoregressive text generation.
• Score: 14.208510167132983
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Although auto-regressive models excel in natural language processing, they often struggle to generate diverse text and provide limited controllability. Non-auto-regressive methods could be an alternative but often produce degenerate outputs and exhibit shortcomings in conditional generation. To address these challenges, we propose Diffusion-EAGS, a novel framework that integrates conditional masked language models into diffusion language models through the theoretical lens of a conditional Markov Random Field. In doing so, we propose entropy-adaptive Gibbs sampling and entropy-based noise scheduling to counterbalance each model's shortcomings. Experimental results show that Diffusion-EAGS outperforms baselines and achieves the best quality-diversity tradeoff, demonstrating its effectiveness in non-autoregressive text generation.

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