3 citations · 6 across the 9 of their papers we have counts for
10 papers
Interact-RAG: Reason and Interact with the Corpus, Beyond Black-Box Retrieval
Yulong Hui, Chao Chen, Zhihang Fu +3
Retrieval-Augmented Generation (RAG) has significantly enhanced LLMs by incorporating external information. However, prevailing agentic RAG approaches are constrained by a critical…
Controlling Thinking Speed in Reasoning Models
Zhengkai Lin, Zhihang Fu, Ze Chen +6
Human cognition is theorized to operate in two modes: fast, intuitive System 1 thinking and slow, deliberate System 2 thinking. While current Large Reasoning Models (LRMs) excel at…
Human-centered Interactive Learning via MLLMs for Text-to-Image Person Re-identification
Yang Qin, Chao Chen, Zhihang Fu +3
Despite remarkable advancements in text-to-image person re-identification (TIReID) facilitated by the breakthrough of cross-modal embedding models, existing methods often struggle…
ROUTE: Robust Multitask Tuning and Collaboration for Text-to-SQL
Yang Qin, Chao Chen, Zhihang Fu +4
Despite the significant advancements in Text-to-SQL (Text2SQL) facilitated by large language models (LLMs), the latest state-of-the-art techniques are still trapped in the in-conte…
Category-Extensible Out-of-Distribution Detection via Hierarchical Context Descriptions
Kai Liu, Zhihang Fu, Chao Chen +5
The key to OOD detection has two aspects: generalized feature representation and precise category description. Recently, vision-language models such as CLIP provide significant adv…
Enhancing LLM's Cognition via Structurization
Kai Liu, Zhihang Fu, Chao Chen +6
When reading long-form text, human cognition is complex and structurized. While large language models (LLMs) process input contexts through a causal and sequential perspective, thi…
Rethinking Out-of-Distribution Detection on Imbalanced Data Distribution
Kai Liu, Zhihang Fu, Sheng Jin +6
Detecting and rejecting unknown out-of-distribution (OOD) samples is critical for deployed neural networks to void unreliable predictions. In real-world scenarios, however, the eff…
ROPO: Robust Preference Optimization for Large Language Models
Xize Liang, Chao Chen, Shuang Qiu +6
Preference alignment is pivotal for empowering large language models (LLMs) to generate helpful and harmless responses. However, the performance of preference alignment is highly s…
Optimal Parameter and Neuron Pruning for Out-of-Distribution Detection
Chao Chen, Zhihang Fu, Kai Liu +3
For a machine learning model deployed in real world scenarios, the ability of detecting out-of-distribution (OOD) samples is indispensable and challenging. Most existing OOD detect…
INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection
Chao Chen, Kai Liu, Ze Chen +5
Knowledge hallucination have raised widespread concerns for the security and reliability of deployed LLMs. Previous efforts in detecting hallucinations have been employed at logit-…