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5 results for “text generation”
Representation-based Masked Diffusion Model
Researchers introduced a new language modeling framework called Representation-based Masked Diffusion Model (RMDM) that uses continuous semantic representations to coordinate parallel token updates in masked diffusion, aiming to improve coherence and quality—especially in fast, few-step generation.
Sep 14, 2026
Judging LLM-as-a-Judge: Concerning Rubric Artifacts in LLM-based Automated Text Generation Evaluation
A research paper demonstrates that LLM-as-a-Judge evaluation systems often rely on rubric text alone—not candidate responses—to generate scores, undermining their validity as objective evaluators of AI-generated text.
Sep 4, 2026
Jako Tako or Fluent? Presenting PoVisLE: A Polish Vision-Language Evaluation
Researchers introduced PoVisLE, a Polish-specific vision-language benchmark with 1,117 images and 2,366 VQA pairs, designed to evaluate culturally grounded multimodal understanding beyond surface-level recognition.
Aug 11, 2026
Adaptive Multi-Step Lookahead Decoding for Diffusion Language Models
Researchers introduced AdaLook, an adaptive multi-step lookahead decoding method for masked diffusion language models that dynamically adjusts rollout depth based on candidate-score variance to improve the accuracy–decoding steps trade-off.
Jul 20, 2026
DiffusionGemma: 4x faster text generation
Google DeepMind released DiffusionGemma, a new text-generation model claiming 4x faster inference than prior models, positioning it as a step toward efficient, scalable AI deployment.
Published Jun 10, 2026 · Analyzed Jul 3, 2026