1 citations · 1 across the 8 of their papers we have counts for
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HybridThinker: Efficient Chain-of-Thought Reasoning via Compressed Memory and Transient Thought Steps
Xin Liu, Runsong Zhao, Xinyu Liu +8
Extended chain-of-thought (CoT) traces improve LLM reasoning but incur substantial computational and memory costs. While existing CoT compression methods mitigate this by condensin…
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…
Translate-and-Revise: Boosting Large Language Models for Constrained Translation
Pengcheng Huang, Yongyu Mu, Yuzhang Wu +4
Imposing constraints on machine translation systems presents a challenging issue because these systems are not trained to make use of constraints in generating adequate, fluent tra…