3 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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…
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…
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-…