✍️ Text Generation¶

🔬 ICLR2026 · 3 paper notes

AP-OOD: Attention Pooling for Out-of-Distribution Detection: This paper proposes AP-OOD, which replaces the mean pooling in Mahalanobis distance-based OOD detection with learnable attention pooling, addressing the information loss caused by mean aggregation of token-level anomaly signals. On text OOD detection, AP-OOD reduces FPR95 on XSUM summarization from 27.84% to 4.67%, while supporting a smooth transition from unsupervised to semi-supervised settings.
FS-DFM: Fast and Accurate Long Text Generation with Few-Step Diffusion Language Model: This paper proposes FS-DFM (Few-Step Discrete Flow-Matching), which reduces the sampling steps of discrete flow-matching language models from 1024 to 8 through step-aware training and a cumulative scalar update rule, achieving a 128× speedup while maintaining comparable perplexity and generation quality.
Rethinking Uncertainty Estimation in LLMs: A Principled Single-Sequence Measure: Starting from the proper scoring rules framework, this paper proves that the negative log-likelihood of the highest-probability output sequence (MSP) is a theoretically grounded uncertainty measure, and proposes G-NLL — a method that approximates this measure with a single greedy decoding pass, matching or surpassing SOTA methods that require multiple samples across several benchmarks.