7 citations · 28 across the 19 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
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-…