7 citations · 28 across the 19 of their papers we have counts for
10 papers · 1 filter
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…
Delving into the Reversal Curse: How Far Can Large Language Models Generalize?
Zhengkai Lin, Zhihang Fu, Kai Liu +6
While large language models (LLMs) showcase unprecedented capabilities, they also exhibit certain inherent limitations when facing seemingly trivial tasks. A prime example is the r…
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…
Structure-aware Domain Knowledge Injection for Large Language Models
Kai Liu, Ze Chen, Zhihang Fu +6
This paper introduces a pioneering methodology, termed StructTuning, to efficiently transform foundation Large Language Models (LLMs) into domain specialists. It significantly redu…
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…
ESOD: Efficient Small Object Detection on High-Resolution Images
Kai Liu, Zhihang Fu, Sheng Jin +5
Enlarging input images is a straightforward and effective approach to promote small object detection. However, simple image enlargement is significantly expensive on both computati…
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-…