8 papers
The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit
Huixue Zhou, Hengrui Gu, Xi Liu +15
The deployment of Large Language Models (LLMs) in recommender systems for predicting Click-Through Rates (CTR) necessitates a delicate balance between computational efficiency and…
WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agents
Hengrui Gu, Xiaotian Han, Kaixiong Zhou
Multi-turn user-facing agents must infer user intent from incomplete requests, collect missing information through dialogue and tools, and execute valid actions. A training traject…
Asymmetric Advantage Modulation Calibrates Entropy Dynamics in RLVR
Hengrui Gu, Xiaotian Han, Yujing Bian +2
Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning ability of large language models (LLMs), but it often suffers from \textit{restricted…
PPBoost: Progressive Prompt Boosting for Text-Driven Medical Image Segmentation
Xuchen Li, Hengrui Gu, Mohan Zhang +6
Text-prompted foundation models for medical image segmentation offer an intuitive way to delineate anatomical structures from natural language queries, but their predictions often…
Harnessing LLMs Explanations to Boost Surrogate Models in Tabular Data Classification
Ruxue Shi, Hengrui Gu, Xu Shen +1
Large Language Models (LLMs) have shown remarkable ability in solving complex tasks, making them a promising tool for enhancing tabular learning. However, existing LLM-based method…
Latte: Transfering LLMs` Latent-level Knowledge for Few-shot Tabular Learning
Ruxue Shi, Hengrui Gu, Hangting Ye +3
Few-shot tabular learning, in which machine learning models are trained with a limited amount of labeled data, provides a cost-effective approach to addressing real-world challenge…