1 citations · 3 across the 10 of their papers we have counts for
10 papers
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…
Layer-Level Self-Exposure and Patch: Affirmative Token Mitigation for Jailbreak Attack Defense
Yang Ouyang, Hengrui Gu, Shuhang Lin +6
As large language models (LLMs) are increasingly deployed in diverse applications, including chatbot assistants and code generation, aligning their behavior with safety and ethical…
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…
Pioneering Reliable Assessment in Text-to-Image Knowledge Editing: Leveraging a Fine-Grained Dataset and an Innovative Criterion
Hengrui Gu, Kaixiong Zhou, Yili Wang +2
During pre-training, the Text-to-Image (T2I) diffusion models encode factual knowledge into their parameters. These parameterized facts enable realistic image generation, but they…
Cross-Lingual Multi-Hop Knowledge Editing
Aditi Khandelwal, Harman Singh, Hengrui Gu +2
Large language models are often expected to constantly adapt to new sources of knowledge and knowledge editing techniques aim to efficiently patch the outdated model knowledge, wit…
PokeMQA: Programmable knowledge editing for Multi-hop Question Answering
Hengrui Gu, Kaixiong Zhou, Xiaotian Han +3
Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine's comprehension and reasoning abilities, where large language models (LLMs) have widely achie…