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Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models
Ej Zhou, Caiqi Zhang, Tiancheng Hu, Chengzu Li, Nigel Collier, Ivan Vuli\'c, Anna Korhonen
https://arxiv.org/abs/2510.03136

Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models
Confidence calibration, the alignment of a model's predicted confidence with its actual accuracy, is crucial for the reliable deployment of Large Language Models (LLMs). However, this critical property remains largely under-explored in multilingual contexts. In this work, we conduct the first large-scale, systematic studies of multilingual calibration across six model families and over 100 languages, revealing that non-English languages suffer from systematically worse calibration. To diagnose …

Accelerating Diffusion Transformer-Based Text-to-Speech with Transformer Layer Caching
Siratish Sakpiboonchit
https://arxiv.org/abs/2509.08696 https://arxi…

Accelerating Diffusion Transformer-Based Text-to-Speech with Transformer Layer Caching
This paper presents a method to accelerate the inference process of diffusion transformer (DiT)-based text-to-speech (TTS) models by applying a selective caching mechanism to transformer layers. Specifically, I integrate SmoothCache into the F5-TTS architecture, focusing on caching outputs of self-attention and feed-forward network layers to reduce redundant computations during the denoising process. A calibration phase is introduced to analyze L1 relative errors between timesteps, guiding the …