5 papers
Causal Autoregressive Diffusion Language Model
Junhao Ruan, Bei Li, Yongjing Yin +6
In this work, we propose Causal Autoregressive Diffusion (CARD), a novel framework that unifies the training efficiency of ARMs with the high-throughput inference of diffusion mode…
RLAIF-SPA: Structured AI Feedback for Semantic-Prosodic Alignment in Speech Synthesis
Qing Yang, Zhenghao Liu, Yangfan Du +2
Recent advances in Text-To-Speech (TTS) synthesis have achieved near-human speech quality in neutral speaking styles. However, most existing approaches either depend on costly emot…
Autoencoding-Free Context Compression for LLMs via Contextual Semantic Anchors
Xin Liu, Runsong Zhao, Pengcheng Huang +7
Context compression is an advanced technique that accelerates large language model (LLM) inference by converting long inputs into compact representations. Existing methods primaril…
Forgetting Curve: A Reliable Method for Evaluating Memorization Capability for Long-context Models
Xinyu Liu, Runsong Zhao, Pengcheng Huang +5
Numerous recent works target to extend effective context length for language models and various methods, tasks and benchmarks exist to measure model's effective memorization length…
Position IDs Matter: An Enhanced Position Layout for Efficient Context Compression in Large Language Models
Runsong Zhao, Xin Liu, Xinyu Liu +4
Using special tokens (e.g., gist, memory, or compressed tokens) to compress context information is a common practice for large language models (LLMs). However, existing approaches…