7 papers
ProjQ: Project-and-Quantize for Adapter-Aware LLM Compression
Wenya Yu, Chao Zhang, Li Wang +2
Post-Training Quantization (PTQ) and Low-Rank Adaptation (LoRA) constitute the standard pipeline for efficient Large Language Model (LLM) deployment. However, applying them sequent…
S2G-RAG: Structured Sufficiency and Gap Judging for Iterative Retrieval-Augmented QA
Minghan Li, Junjie Zou, Xinxuan Lv +2
Retrieval-Augmented Generation (RAG) grounds language models in external evidence, but multi-hop question answering remains difficult because iterative pipelines must control what…
GLIER: Generative Legal Inference and Evidence Ranking for Legal Case Retrieval
Minghan Li, Tianrui Lv, Chao Zhang +1
The semantic gap between colloquial user queries and professional legal documents presents a fundamental challenge in Legal Case Retrieval (LCR). Existing dense retrieval methods t…
From Similarity to Structure: Training-free LLM Context Compression with Hybrid Graph Priors
Yitian Zhou, Chaoning Zhang, Jiaquan Zhang +6
Long-context large language models remain computationally expensive to run and often fail to reliably process very long inputs, which makes context compression an important compone…
Small Language Model Helps Resolve Semantic Ambiguity of LLM Prompt
Zhenzhen Huang, Chaoning Zhang, Fachrina Dewi Puspitasari +4
Large language models (LLMs) are increasingly utilized in various complex reasoning tasks due to their excellent instruction following capability. However, the model's performance…
Precise Attribute Intensity Control in Large Language Models via Targeted Representation Editing
Rongzhi Zhang, Liqin Ye, Yuzhao Heng +5
Precise attribute intensity control--generating Large Language Model (LLM) outputs with specific, user-defined attribute intensities--is crucial for AI systems adaptable to diverse…
Calibrating Pre-trained Language Classifiers on LLM-generated Noisy Labels via Iterative Refinement
Liqin Ye, Agam Shah, Chao Zhang +1
The traditional process of creating labeled datasets is labor-intensive and expensive. Recent breakthroughs in open-source large language models (LLMs) have opened up a new avenue…