29 citations · 36 across the 16 of their papers we have counts for
11 papers · 1 filter
Memory Augmentation Unlocks Efficient Chain-of-Thought Reasoning
Simeng Zhang, Yilong Chen, Wenyuan Zhang +4
Large language models often rely on Chain-of-Thought (CoT) reasoning to solve complex tasks, but verbose reasoning traces introduce substantial inference overhead. CoT compression…
ConSA: Controllable Sparsity in Hybrid Attention via Learnable Allocation
Yao Chen, Yinqi Yang, Junyuan Shang +6
Hybrid architectures combining full attention (FA) and sliding-window attention (SWA) are a promising paradigm for efficient LLM inference. However, existing methods typically rely…
Improving Reasoning Capabilities in Small Models through Mixture-of-Layers Distillation with Stepwise Attention on Key Information
Yao Chen, Jiawei Sheng, Wenyuan Zhang +1
The significant computational demands of large language models have increased interest in distilling reasoning abilities into smaller models via Chain-of-Thought (CoT) distillation…
Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping
Yao Chen, Yilong Chen, Yinqi Yang +9
Existing approaches to increasing the effective depth of Transformers predominantly rely on parameter reuse, extending computation through recursive execution. Under this paradigm,…
ATTNPO: Attention-Guided Process Supervision for Efficient Reasoning
Shuaiyi Nie, Siyu Ding, Wenyuan Zhang +7
Large reasoning models trained with reinforcement learning and verifiable rewards (RLVR) achieve strong performance on complex reasoning tasks, yet often overthink, generating redu…
Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking
Yilong Chen, Junyuan Shang, Zhenyu Zhang +7
Large language models (LLMs) face inherent performance bottlenecks under parameter constraints, particularly in processing critical tokens that demand complex reasoning. Empirical…
Mixture of Hidden-Dimensions Transformer
Yilong Chen, Junyuan Shang, Zhengyu Zhang +6
Transformer models encounter challenges in scaling hidden dimensions efficiently, as uniformly increasing them inflates computational and memory costs while failing to emphasize th…
NACL: A General and Effective KV Cache Eviction Framework for LLMs at Inference Time
Yilong Chen, Guoxia Wang, Junyuan Shang +7
Large Language Models (LLMs) have ignited an innovative surge of AI applications, marking a new era of exciting possibilities equipped with extended context windows. However, hosti…
Optimal Transport Guided Correlation Assignment for Multimodal Entity Linking
Zefeng Zhang, Jiawei Sheng, Chuang Zhang +4
Multimodal Entity Linking (MEL) aims to link ambiguous mentions in multimodal contexts to entities in a multimodal knowledge graph. A pivotal challenge is to fully leverage multi-e…
Adaptive Data Augmentation for Aspect Sentiment Quad Prediction
Wenyuan Zhang, Xinghua Zhang, Shiyao Cui +3
Aspect sentiment quad prediction (ASQP) aims to predict the quad sentiment elements for a given sentence, which is a critical task in the field of aspect-based sentiment analysis.…
FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity
Shiyao Cui, Zhenyu Zhang, Yilong Chen +4
The widespread of generative artificial intelligence has heightened concerns about the potential harms posed by AI-generated texts, primarily stemming from factoid, unfair, and tox…