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Di Liang

11 papers hereh-index 556 citations11 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author9
  • last author1

Across the 10 of 11 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.AI3
  • cs.CR1
  • cs.CV1
  • cs.IR1
  • cs.LG1
same name
  • Di Liang — 5 papers, h 4
  • Di Liang — 5 papers, h 3
  • Di Liang — 4 papers, h 4
  • Di Liang — 4 papers, h 14
  • Di Liang — 1 paper, h 1
  • Di Liang — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2026

Beyond Semantic Relevance: Counterfactual Risk Minimization for Robust Retrieval-Augmented Generation

Peiyang Liu, Qiang Yan, Ziqiang Cui +3

Standard Retrieval-Augmented Generation (RAG) systems predominantly rely on semantic relevance as a proxy for utility. However, this assumption collapses in realistic decision-maki…

cs.CL2025

R-Capsule: Compressing High-Level Plans for Efficient Large Language Model Reasoning

Hongyu Shan, Mingyang Song, Chang Dai +2

Chain-of-Thought (CoT) prompting helps Large Language Models (LLMs) tackle complex reasoning by eliciting explicit step-by-step rationales. However, CoT's verbosity increases laten…

cs.CL2025

HoPE: Hyperbolic Rotary Positional Encoding for Stable Long-Range Dependency Modeling in Large Language Models

Chang Dai, Hongyu Shan, Mingyang Song +1

Positional encoding mechanisms enable Transformers to model sequential structure and long-range dependencies in text. While absolute positional encodings struggle with extrapolatio…

cs.CL2025

Progressive Mastery: Customized Curriculum Learning with Guided Prompting for Mathematical Reasoning

Muling Wu, Qi Qian, Wenhao Liu +12

Large Language Models (LLMs) have achieved remarkable performance across various reasoning tasks, yet post-training is constrained by inefficient sample utilization and inflexible…

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