arXiv AI By Linhao Zhong, Linyu Wu, Wen Wang, Yuling Xi, Chenchen Jing, Jiaheng Zhang, Hao Chen, Chunhua Shen

Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration

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arXiv Computation and Language
2d ago

Hacking Generative Perplexity: Why Unconditional Text Evaluation Needs New Metrics

The paper critiques generative perplexity (gen‑PPL) as an unreliable metric for evaluating unconditional text generation, showing that it can be minimized by trivial, non‑diverse outputs. The authors demonstrate this by constructing simple samplers that achieve low gen‑PPL yet produce incoherent text, and they propose alternative evaluation tools—including distributional discrepancies, diversity diagnostics, and a compression‑based KL divergence measure—to better assess generative language models. They argue that future progress in diffusion language models should rely on a suite of metrics that capture diverse aspects of generated text quality.

By Antonio Franca, Alexander Tong
arXiv Computation and Language
Aug 28

Dependency-Aware Revocable Decoding for Efficient Diffusion Large Language Model Inference

The paper introduces Dependency-Aware Revocable Decoding (DARD), a training‑free framework for diffusion large language models that separates tokens into masked, candidate, and unmasked states. DARD verifies candidate tokens using a selective context that excludes less reliable tokens and adaptively regulates their influence on subsequent decoding. Experiments on 12 textual and multimodal benchmarks across three open‑source dLLMs show that DARD improves the speed‑quality Pareto frontier, achieving a 2.71× speedup and a 4.35‑point CIDEr gain over Saber on Flickr30K.

By Wooje Park, Insu Lee, Minyoung Noh, Jaeyun Jang, Sungmin Lee, Kyuhong Shim, Byonghyo Shim
Hugging Face Trending Papers
Jun 10

Re-evaluating Confidence Remasking in Masked Diffusion Language Models

Masked diffusion language models (dLLMs) have recently emerged as a competitive alternative to autoregressive language models, with the promise of faster inference via parallel token generation. A notable limitation of the masked formulation, however, is that once a token has been unmasked it can no longer be revised, leaving dLLMs vulnerable to early sampling mistakes.