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The paper investigates how diffusion language models maintain factual fidelity when generating text from tables, comparing masked diffusion models (MDLM, LLaDA) to autoregressive models (GPT‑2, GPT‑Neo) on the ToTTo dataset. Using a retrieval‑based few‑shot protocol, the study finds that while diffusion models score higher on reference similarity (BLEU), they exhibit lower source grounding and different failure modes—autoregressive models drift from the table, whereas diffusion models stay on topic but swap entities. The authors conclude that reference‑based metrics alone are insufficient for assessing faithfulness in table‑to‑text generation.
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.
arXiv:2606. 06474v1 Announce Type: cross Abstract: Discrete diffusion language models generate text by iteratively denoising an entire response in parallel.
The study compares continuous diffusion language models (CDLMs) and speculative drafters by training them from scratch on unconditional OpenWebText generation at lengths 16, 128, and 1024. Using LangFlow for CDLMs and adapted DSpark Markov and recurrent-head architectures for standalone generation, the authors evaluate generation perplexity (GenPPL) and MAUVE similarity. Results show LangFlow outperforms both DSpark variants at longer lengths (128 and 1024), while DSpark-RNN leads at length 16; recurrent memory and teacher probability matching improve metrics but the longer‑sequence gap remains, highlighting iterative full‑sequence refinement as a key advantage.
arXiv:2605. 17932v2 Announce Type: replace-cross Abstract: Prompt compression reduces inference cost and context length in large language models, but prior evaluations focus mainly on autoregressive architectures.
arXiv:2606. 08417v1 Announce Type: cross Abstract: Diffusion and continuous flow-based language models have emerged as the leading non-autoregressive alternatives to language modeling.